Repository: smthemex/ComfyUI_DiffuEraser Branch: main Commit: c8461876ad09 Files: 77 Total size: 2.5 MB Directory structure: gitextract_svppjk7y/ ├── LICENSE ├── README.md ├── __init__.py ├── diffueraser_node.py ├── example_workflows/ │ └── differaser.json ├── libs/ │ ├── __init__.py │ ├── brushnet_CA.py │ ├── diffueraser.py │ ├── pipeline_diffueraser.py │ ├── transformer_temporal.py │ ├── unet_2d_blocks.py │ ├── unet_2d_condition.py │ ├── unet_3d_blocks.py │ ├── unet_motion_model.py │ └── v1-inference.yaml ├── node_utils.py ├── propainter/ │ ├── RAFT/ │ │ ├── __init__.py │ │ ├── corr.py │ │ ├── datasets.py │ │ ├── demo.py │ │ ├── extractor.py │ │ ├── raft.py │ │ ├── update.py │ │ └── utils/ │ │ ├── __init__.py │ │ ├── augmentor.py │ │ ├── flow_viz.py │ │ ├── flow_viz_pt.py │ │ ├── frame_utils.py │ │ └── utils.py │ ├── core/ │ │ ├── __init__.py │ │ ├── dataset.py │ │ ├── dist.py │ │ ├── loss.py │ │ ├── lr_scheduler.py │ │ ├── metrics.py │ │ ├── prefetch_dataloader.py │ │ ├── trainer.py │ │ ├── trainer_flow_w_edge.py │ │ └── utils.py │ ├── inference.py │ ├── model/ │ │ ├── __init__.py │ │ ├── canny/ │ │ │ ├── __init__.py │ │ │ ├── canny_filter.py │ │ │ ├── filter.py │ │ │ ├── gaussian.py │ │ │ ├── kernels.py │ │ │ └── sobel.py │ │ ├── misc.py │ │ ├── modules/ │ │ │ ├── __init__.py │ │ │ ├── base_module.py │ │ │ ├── deformconv.py │ │ │ ├── flow_comp_raft.py │ │ │ ├── flow_loss_utils.py │ │ │ ├── sparse_transformer.py │ │ │ └── spectral_norm.py │ │ ├── propainter.py │ │ ├── recurrent_flow_completion.py │ │ └── vgg_arch.py │ └── utils/ │ ├── __init__.py │ ├── download_util.py │ ├── file_client.py │ ├── flow_util.py │ └── img_util.py ├── pyproject.toml ├── requirements.txt ├── run_diffueraser.py └── sd15_repo/ ├── feature_extractor/ │ └── preprocessor_config.json ├── model_index.json ├── safety_checker/ │ └── config.json ├── scheduler/ │ └── scheduler_config.json ├── text_encoder/ │ └── config.json ├── tokenizer/ │ ├── merges.txt │ ├── special_tokens_map.json │ ├── tokenizer_config.json │ └── vocab.json ├── unet/ │ └── config.json └── vae/ └── config.json ================================================ FILE CONTENTS ================================================ ================================================ FILE: LICENSE ================================================ MIT License Copyright (c) 2025 smthemex Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ================================================ FILE: README.md ================================================ # ComfyUI_DiffuEraser [DiffuEraser](https://github.com/lixiaowen-xw/DiffuEraser) is a diffusion model for video Inpainting, you can use it in ComfyUI # Update * 使用官方推荐的vae文件，clip-l改成comfyUI 默认的，由此剔除掉sd15底模； * 对于水印，mask_dilation_iter（遮罩膨胀系数）应适当调低，比如2，常规使用Propainter的采样也就够了（最大边是960，应用了再超分吧） * use cofmyUI v3 mode,fix bugs,add new diffuser support,you can run 1280*720 (12GVRAM) now * 修复不少bug，现在12G也能跑1280*720，DiffuEraser的sample 节点的 blend支持2种输出，关闭为降低闪烁，开启为使用合成，避免loop循环的反复加载模型 # 1. Installation In the ./ComfyUI /custom_nodes directory, run the following: ``` git clone https://github.com/smthemex/ComfyUI_DiffuEraser.git ``` --- # 2. Requirements * no need, because it's base in sd1.5 ,Perhaps someone may be missing the library.没什么特殊的库,懒得删了 ``` pip install -r requirements.txt ``` # 3. Models * vae [links](https://huggingface.co/stabilityai/sd-vae-ft-mse/tree/main) * clip-l， comfyUI normal * pcm 1.5 lora [address](https://huggingface.co/wangfuyun/PCM_Weights/tree/main/sd15) pcm_sd15_smallcfg_2step_converted.safetensors #example * ProPainter [address](https://github.com/sczhou/ProPainter/releases/tag/v0.1.0) # below example * unet and brushnet [address](https://huggingface.co/lixiaowen/diffuEraser/tree/main) # below example ``` -- ComfyUI/models/vae |-- sd-vae-ft-mse.safetensors #vae -- ComfyUI/models/clip |-- clip_l.safetensors # comfyUI normal -- ComfyUI/models/DiffuEraservae |--brushnet |-- config.json |-- diffusion_pytorch_model.safetensors |--unet_main |-- config.json |-- diffusion_pytorch_model.safetensors |--propainter |-- ProPainter.pth |-- raft-things.pth |-- recurrent_flow_completion.pth ``` * If use video to mask #可以用RMBG或者BiRefNet模型脱底 ``` -- any/path/briaai/RMBG-2.0 # or auto download |--config.json |--model.safetensors |--birefnet.py |--BiRefNet_config.py Or -- any/path/ZhengPeng7/BiRefNet # or auto download |--config.json |--model.safetensors |--birefnet.py |--BiRefNet_config.py |--handler.py ``` # 4 Example ![](https://github.com/smthemex/ComfyUI_DiffuEraser/blob/main/example_workflows/example.png) * use single mask ![](https://github.com/smthemex/ComfyUI_DiffuEraser/blob/main/example_workflows/example1.png) # 5.Citation ``` @misc{li2025diffueraserdiffusionmodelvideo, title={DiffuEraser: A Diffusion Model for Video Inpainting}, author={Xiaowen Li and Haolan Xue and Peiran Ren and Liefeng Bo}, year={2025}, eprint={2501.10018}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2501.10018}, } ``` ``` @inproceedings{zhou2023propainter, title={{ProPainter}: Improving Propagation and Transformer for Video Inpainting}, author={Zhou, Shangchen and Li, Chongyi and Chan, Kelvin C.K and Loy, Chen Change}, booktitle={Proceedings of IEEE International Conference on Computer Vision (ICCV)}, year={2023} } ``` ``` @misc{ju2024brushnet, title={BrushNet: A Plug-and-Play Image Inpainting Model with Decomposed Dual-Branch Diffusion}, author={Xuan Ju and Xian Liu and Xintao Wang and Yuxuan Bian and Ying Shan and Qiang Xu}, year={2024}, eprint={2403.06976}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ``` @article{BiRefNet, title={Bilateral Reference for High-Resolution Dichotomous Image Segmentation}, author={Zheng, Peng and Gao, Dehong and Fan, Deng-Ping and Liu, Li and Laaksonen, Jorma and Ouyang, Wanli and Sebe, Nicu}, journal={CAAI Artificial Intelligence Research}, year={2024} } ``` ================================================ FILE: __init__.py ================================================ from .diffueraser_node import * ================================================ FILE: diffueraser_node.py ================================================ # !/usr/bin/env python # -*- coding: UTF-8 -*- import os import torch import gc import numpy as np from typing_extensions import override from comfy_api.latest import ComfyExtension, io import nodes import comfy.model_management as mm from .node_utils import load_images,tensor2pil_list,image2masks,nomarl_upscale import folder_paths from .run_diffueraser import load_diffueraser,load_propainter from diffusers.hooks import apply_group_offloading import copy MAX_SEED = np.iinfo(np.int32).max current_node_path = os.path.dirname(os.path.abspath(__file__)) device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" # add checkpoints dir DiffuEraser_weigths_path = os.path.join(folder_paths.models_dir, "DiffuEraser") if not os.path.exists(DiffuEraser_weigths_path): os.makedirs(DiffuEraser_weigths_path) folder_paths.add_model_folder_path("DiffuEraser", DiffuEraser_weigths_path) class Propainter_Loader(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="Propainter_Loader", display_name="Propainter_Loader", category="DiffuEraser", inputs=[ io.Combo.Input("propainter",options= ["none"] + folder_paths.get_filename_list("DiffuEraser") ), io.Combo.Input("flow",options= ["none"] + folder_paths.get_filename_list("DiffuEraser") ), io.Combo.Input("fix_raft",options= ["none"] + folder_paths.get_filename_list("DiffuEraser") ), io.Combo.Input("device",options= ["cpu","cuda","mps"] ), ], outputs=[ io.Custom("Propainter_Loader").Output(display_name="model"), ], ) @classmethod def execute(cls, propainter,flow,fix_raft,device) -> io.NodeOutput: ProPainter_path=folder_paths.get_full_path("DiffuEraser",propainter) if propainter!="none" else None flow_path=folder_paths.get_full_path("DiffuEraser",flow) if flow!="none" else None fix_raft_path=folder_paths.get_full_path("DiffuEraser",fix_raft) if fix_raft!="none" else None if fix_raft_path is None or flow_path is None or ProPainter_path is None: raise "need load all models" model=load_propainter(fix_raft_path,flow_path,ProPainter_path,device=device) return io.NodeOutput(model) class DiffuEraser_Loader(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="DiffuEraser_Loader", display_name="DiffuEraser_Loader", category="DiffuEraser", inputs=[ io.Combo.Input("vae",options= ["none"] + folder_paths.get_filename_list("vae") ), io.Combo.Input("lora",options= ["none"] + folder_paths.get_filename_list("loras") ), ], outputs=[ io.Custom("DiffuEraser_Loader").Output(display_name="model"), ], ) @classmethod def execute(cls, vae,lora) -> io.NodeOutput: ckpt_path=folder_paths.get_full_path("vae",vae) if vae!="none" else None pcm_lora_path=folder_paths.get_full_path("loras",lora) if lora!="none" else None #print("load lora model from:",pcm_lora_path) model=load_diffueraser(os.path.join(current_node_path,"sd15_repo"),DiffuEraser_weigths_path, ckpt_path,os.path.join(current_node_path,"libs/v1-inference.yaml"),pcm_lora_path,device) gc.collect() torch.cuda.empty_cache() return io.NodeOutput(model) class DiffuEraser_PreData(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="DiffuEraser_PreData", display_name="DiffuEraser_PreData", category="DiffuEraser", inputs=[ io.Image.Input("images"), io.String.Input("seg_repo",default="briaai/RMBG-2.0"), io.Image.Input("video_mask_image",optional=True), io.Mask.Input("video_mask",optional=True), ], outputs=[ io.Conditioning.Output(display_name="conditioning"), ], ) @classmethod def execute(cls, images,seg_repo,video_mask_image=None,video_mask=None) -> io.NodeOutput: _,height,width,_ = images.size() height,width=(height-height%8, width-width%8) video_image=tensor2pil_list(images,width,height) if video_mask is None and video_mask_image is None and seg_repo: # use rmbg or BiRefNet to make video to masks print("*********** Use input video and repo to make masks **************") init_mask=image2masks(seg_repo,video_image) elif video_mask_image is not None: if not isinstance(video_mask_image,torch.Tensor): raise "video_mask_image is not a normal comfyUI image tensor, need a shape like b,h,w,c" else: init_mask=tensor2pil_list(video_mask_image,width,height) elif video_mask is not None: if isinstance(video_mask,torch.Tensor) and len(video_mask)>3: raise "video_mask is not a normal comfyUI mask tensor, need a shape like b,h,w" init_mask=tensor2pil_list( video_mask.reshape((-1, 1, video_mask.shape[-2], video_mask.shape[-1])).movedim(1, -1).expand(-1, -1, -1, 3) ,width,height) else: raise "no video_mask,you can enable video2mask and fill a rmbg or BiRefNet repo to generate mask from video_image,or link video_mask from other node" if len(init_mask)!=len(video_image) : if len(init_mask)==1: init_mask=init_mask*len(video_image) # if use one mask to inpaint all frames else: if len(init_mask)>len(video_image): init_mask=init_mask[:len(video_image)] print("init_mask length:",len(init_mask),"video_image length:",len(video_image)) else: init_mask=init_mask+init_mask[:len(video_image)-len(init_mask)] print("init_mask length:",len(init_mask),"video_image length:",len(video_image)) cond={"init_mask":init_mask,"video_image":video_image,"height":height,"width":width} return io.NodeOutput(cond) class Propainter_Sampler(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="Propainter_Sampler", display_name="Propainter_Sampler", category="DiffuEraser", inputs=[ io.Custom("Propainter_Loader").Input("model"), io.Conditioning.Input("conditioning"), io.Float.Input("fps", force_input=True), io.Int.Input("video_length", default=10, min=1, max=1024,step=1,display_mode=io.NumberDisplay.number), io.Int.Input("mask_dilation_iter", default=2, min=1, max=1024,step=1,display_mode=io.NumberDisplay.number), io.Int.Input("ref_stride", default=10, min=1, max=1024,step=1,display_mode=io.NumberDisplay.number), io.Int.Input("neighbor_length", default=10, min=1, max=1024,step=1,display_mode=io.NumberDisplay.number), io.Int.Input("subvideo_length", default=50, min=1, max=1024,step=1,display_mode=io.NumberDisplay.number), ], outputs=[ io.Conditioning.Output(display_name="conditioning"), io.Image.Output(display_name="images"), ], ) @classmethod def execute(cls, model,conditioning,fps,video_length,mask_dilation_iter,ref_stride,neighbor_length,subvideo_length) -> io.NodeOutput: model.to(device) conditioning["fps"]=fps conditioning["video_length"]=video_length conditioning["mask_dilation_iter"]=mask_dilation_iter Propainter_img=model.forward(copy.deepcopy(conditioning["video_image"]), copy.deepcopy(conditioning["init_mask"]),load_videobypath=False,video_length=video_length, height= conditioning["height"],width=conditioning["width"], ref_stride=ref_stride, neighbor_length=neighbor_length, subvideo_length = subvideo_length, mask_dilation = mask_dilation_iter,save_fps=fps) conditioning["prioris"]=Propainter_img model.to("cpu") gc.collect() torch.cuda.empty_cache() images=load_images(Propainter_img) return io.NodeOutput(conditioning,images) class DiffuEraser_Sampler(io.ComfyNode): @classmethod def define_schema(cls): return io.Schema( node_id="DiffuEraser_Sampler", display_name="DiffuEraser_Sampler", category="DiffuEraser", inputs=[ io.Custom("DiffuEraser_Loader").Input("model"), io.Conditioning.Input("positive"), io.Conditioning.Input("conditioning"), io.Int.Input("steps", default=2, min=1, max=1024,step=1,display_mode=io.NumberDisplay.number), io.Int.Input("seed", default=0, min=0, max=MAX_SEED,display_mode=io.NumberDisplay.number), io.Boolean.Input("save_result_video", default=False), io.Int.Input("unet_group", default=5, min=1, max=1024,step=1,display_mode=io.NumberDisplay.number), io.Int.Input("brush_group", default=5, min=1, max=1024,step=1,display_mode=io.NumberDisplay.number), io.Boolean.Input("blended", default=False), ], outputs=[ io.Image.Output(display_name="image"), ], ) @classmethod def execute(cls, model,positive,conditioning,steps,seed,save_result_video,unet_group,brush_group,blended) -> io.NodeOutput: # gc cf model cf_models=mm.loaded_models() try: for pipe in cf_models: pipe.unpatch_model(device_to=torch.device("cpu")) print(f"Unpatching models.{pipe}") except: pass mm.soft_empty_cache() torch.cuda.empty_cache() max_gpu_memory = torch.cuda.max_memory_allocated() print(f"After Max GPU memory allocated: {max_gpu_memory / 1000 ** 3:.2f} GB") max_img_size=1920 model.to(device) model.pipeline.enable_xformers_memory_efficient_attention() apply_group_offloading(model.pipeline.unet, onload_device=torch.device("cuda"), offload_type="block_level", num_blocks_per_group=unet_group) apply_group_offloading(model.pipeline.brushnet, onload_device=torch.device("cuda"), offload_type="block_level", num_blocks_per_group=brush_group) image_list=model.forward( copy.deepcopy(conditioning["video_image"]), copy.deepcopy(conditioning["init_mask"]), copy.deepcopy(conditioning["prioris"]),folder_paths.get_output_directory(),positive,load_videobypath=False, max_img_size = max_img_size, video_length=conditioning["video_length"], mask_dilation_iter=conditioning["mask_dilation_iter"],seed=seed,blended=blended, num_inference_steps=steps,fps=conditioning["fps"],img_size=(conditioning["width"],conditioning["height"]),if_save_video=save_result_video) #model.to("cpu") gc.collect() torch.cuda.empty_cache() images=load_images(image_list) return io.NodeOutput(images) from aiohttp import web from server import PromptServer @PromptServer.instance.routes.get("/DiffuEraser_SM_Extension") async def get_hello(request): return web.json_response("DiffuEraser_SM_Extension") class DiffuEraser_SM_Extension(ComfyExtension): @override async def get_node_list(self) -> list[type[io.ComfyNode]]: return [ Propainter_Loader, DiffuEraser_Loader, DiffuEraser_PreData, Propainter_Sampler, DiffuEraser_Sampler, ] async def comfy_entrypoint() -> DiffuEraser_SM_Extension: # ComfyUI calls this to load your extension and its nodes. return DiffuEraser_SM_Extension() ================================================ FILE: example_workflows/differaser.json ================================================ { "id": "3da45669-6ef0-4ec2-a292-abe74e953ca2", "revision": 0, "last_node_id": 24, "last_link_id": 28, "nodes": [ { "id": 17, "type": "SaveVideo", "pos": [ 4039.588713354701, 1439.7671022987934 ], "size": [ 478, 420.6194116210936 ], "flags": {}, "order": 12, "mode": 0, "inputs": [ { "name": "video", "type": "VIDEO", "link": 14 } ], "outputs": [], "properties": {}, "widgets_values": [ "video/ComfyUI", "auto", "auto" ] }, { "id": 6, "type": "DiffuEraser_PreData", "pos": [ 3639.4483963547445, 1166.3094503184818 ], "size": [ 272.4375, 98 ], "flags": {}, "order": 5, "mode": 0, "inputs": [ { "name": "images", "type": "IMAGE", "link": 18 }, { "name": "video_mask_image", "shape": 7, "type": "IMAGE", "link": 19 }, { "name": "video_mask", "shape": 7, "type": "MASK", "link": null } ], "outputs": [ { "name": "conditioning", "type": "CONDITIONING", "links": [ 1 ] } ], "properties": { "Node name for S&R": "DiffuEraser_PreData" }, "widgets_values": [ "briaai/RMBG-2.0" ] }, { "id": 10, "type": "CreateVideo", "pos": [ 4707.826211690106, 1050.8378342212904 ], "size": [ 270, 78 ], "flags": {}, "order": 11, "mode": 0, "inputs": [ { "name": "images", "type": "IMAGE", "link": 26 }, { "name": "audio", "shape": 7, "type": "AUDIO", "link": null }, { "name": "fps", "type": "FLOAT", "widget": { "name": "fps" }, "link": 23 } ], "outputs": [ { "name": "VIDEO", "type": "VIDEO", "links": [ 5 ] } ], "properties": { "Node name for S&R": "CreateVideo" }, "widgets_values": [ 30 ] }, { "id": 19, "type": "VHS_LoadVideo", "pos": [ 3372.3171964640787, 1474.4346575533937 ], "size": [ 261.6533203125, 310 ], "flags": {}, "order": 0, "mode": 0, "inputs": [ { "name": "meta_batch", "shape": 7, "type": "VHS_BatchManager", "link": null }, { "name": "vae", "shape": 7, "type": "VAE", "link": null } ], "outputs": [ { "name": "IMAGE", "type": "IMAGE", "links": [ 19 ] }, { "name": "frame_count", "type": "INT", "links": null }, { "name": "audio", "type": "AUDIO", "links": null }, { "name": "video_info", "type": "VHS_VIDEOINFO", "links": null } ], "properties": { "Node name for S&R": "VHS_LoadVideo" }, "widgets_values": { "video": "mask.mp4", "force_rate": 0, "custom_width": 0, "custom_height": 0, "frame_load_cap": 44, "skip_first_frames": 0, "select_every_nth": 1, "format": "AnimateDiff", "videopreview": { "hidden": false, "paused": false, "params": { "filename": "mask.mp4", "type": "input", "format": "video/mp4", "force_rate": 0, "custom_width": 0, "custom_height": 0, "frame_load_cap": 44, "skip_first_frames": 0, "select_every_nth": 1 } } } }, { "id": 20, "type": "VHS_VideoInfo", "pos": [ 3361.206167411345, 1183.434691122729 ], "size": [ 234.931640625, 206 ], "flags": {}, "order": 6, "mode": 0, "inputs": [ { "name": "video_info", "type": "VHS_VIDEOINFO", "link": 20 } ], "outputs": [ { "name": "source_fps🟨", "type": "FLOAT", "links": [ 21, 22, 23 ] }, { "name": "source_frame_count🟨", "type": "INT", "links": null }, { "name": "source_duration🟨", "type": "FLOAT", "links": null }, { "name": "source_width🟨", "type": "INT", "links": null }, { "name": "source_height🟨", "type": "INT", "links": null }, { "name": "loaded_fps🟦", "type": "FLOAT", "links": null }, { "name": "loaded_frame_count🟦", "type": "INT", "links": null }, { "name": "loaded_duration🟦", "type": "FLOAT", "links": null }, { "name": "loaded_width🟦", "type": "INT", "links": null }, { "name": "loaded_height🟦", "type": "INT", "links": null } ], "properties": { "Node name for S&R": "VHS_VideoInfo" }, "widgets_values": {} }, { "id": 18, "type": "VHS_LoadVideo", "pos": [ 3043.1471860575843, 1001.7635755450315 ], "size": [ 261.6533203125, 459.82388026932085 ], "flags": {}, "order": 1, "mode": 0, "inputs": [ { "name": "meta_batch", "shape": 7, "type": "VHS_BatchManager", "link": null }, { "name": "vae", "shape": 7, "type": "VAE", "link": null } ], "outputs": [ { "name": "IMAGE", "type": "IMAGE", "links": [ 18 ] }, { "name": 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"frontendVersion": "1.41.21", "VHS_latentpreview": false, "VHS_latentpreviewrate": 0, "VHS_MetadataImage": true, "VHS_KeepIntermediate": true }, "version": 0.4 } ================================================ FILE: libs/__init__.py ================================================ ================================================ FILE: libs/brushnet_CA.py ================================================ from dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Union import torch from torch import nn from torch.nn import functional as F from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.utils import BaseOutput, logging from diffusers.models.attention_processor import ( ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnAddedKVProcessor, AttnProcessor, ) from diffusers.models.embeddings import TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps from diffusers.models.modeling_utils import ModelMixin from .unet_2d_blocks import ( CrossAttnDownBlock2D, DownBlock2D, UNetMidBlock2D, UNetMidBlock2DCrossAttn, get_down_block, get_mid_block, get_up_block, MidBlock2D ) # from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel from .unet_2d_condition import UNet2DConditionModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class BrushNetOutput(BaseOutput): """ The output of [`BrushNetModel`]. Args: up_block_res_samples (`tuple[torch.Tensor]`): A tuple of upsample activations at different resolutions for each upsampling block. Each tensor should be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be used to condition the original UNet's upsampling activations. down_block_res_samples (`tuple[torch.Tensor]`): A tuple of downsample activations at different resolutions for each downsampling block. Each tensor should be of shape `(batch_size, channel * resolution, height //resolution, width // resolution)`. Output can be used to condition the original UNet's downsampling activations. mid_down_block_re_sample (`torch.Tensor`): The activation of the midde block (the lowest sample resolution). Each tensor should be of shape `(batch_size, channel * lowest_resolution, height // lowest_resolution, width // lowest_resolution)`. Output can be used to condition the original UNet's middle block activation. """ up_block_res_samples: Tuple[torch.Tensor] down_block_res_samples: Tuple[torch.Tensor] mid_block_res_sample: torch.Tensor class BrushNetModel(ModelMixin, ConfigMixin): """ A BrushNet model. Args: in_channels (`int`, defaults to 4): The number of channels in the input sample. flip_sin_to_cos (`bool`, defaults to `True`): Whether to flip the sin to cos in the time embedding. freq_shift (`int`, defaults to 0): The frequency shift to apply to the time embedding. down_block_types (`tuple[str]`, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): The tuple of downsample blocks to use. mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): The tuple of upsample blocks to use. only_cross_attention (`Union[bool, Tuple[bool]]`, defaults to `False`): block_out_channels (`tuple[int]`, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each block. layers_per_block (`int`, defaults to 2): The number of layers per block. downsample_padding (`int`, defaults to 1): The padding to use for the downsampling convolution. mid_block_scale_factor (`float`, defaults to 1): The scale factor to use for the mid block. act_fn (`str`, defaults to "silu"): The activation function to use. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. If None, normalization and activation layers is skipped in post-processing. norm_eps (`float`, defaults to 1e-5): The epsilon to use for the normalization. cross_attention_dim (`int`, defaults to 1280): The dimension of the cross attention features. transformer_layers_per_block (`int` or `Tuple[int]`, *optional*, defaults to 1): The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. encoder_hid_dim (`int`, *optional*, defaults to None): If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` dimension to `cross_attention_dim`. encoder_hid_dim_type (`str`, *optional*, defaults to `None`): If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. attention_head_dim (`Union[int, Tuple[int]]`, defaults to 8): The dimension of the attention heads. use_linear_projection (`bool`, defaults to `False`): class_embed_type (`str`, *optional*, defaults to `None`): The type of class embedding to use which is ultimately summed with the time embeddings. Choose from None, `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. addition_embed_type (`str`, *optional*, defaults to `None`): Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or "text". "text" will use the `TextTimeEmbedding` layer. num_class_embeds (`int`, *optional*, defaults to 0): Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing class conditioning with `class_embed_type` equal to `None`. upcast_attention (`bool`, defaults to `False`): resnet_time_scale_shift (`str`, defaults to `"default"`): Time scale shift config for ResNet blocks (see `ResnetBlock2D`). Choose from `default` or `scale_shift`. projection_class_embeddings_input_dim (`int`, *optional*, defaults to `None`): The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when `class_embed_type="projection"`. brushnet_conditioning_channel_order (`str`, defaults to `"rgb"`): The channel order of conditional image. Will convert to `rgb` if it's `bgr`. conditioning_embedding_out_channels (`tuple[int]`, *optional*, defaults to `(16, 32, 96, 256)`): The tuple of output channel for each block in the `conditioning_embedding` layer. global_pool_conditions (`bool`, defaults to `False`): TODO(Patrick) - unused parameter. addition_embed_type_num_heads (`int`, defaults to 64): The number of heads to use for the `TextTimeEmbedding` layer. """ _supports_gradient_checkpointing = True @register_to_config def __init__( self, in_channels: int = 4, conditioning_channels: int = 5, flip_sin_to_cos: bool = True, freq_shift: int = 0, down_block_types: Tuple[str, ...] = ( "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D", ), mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", up_block_types: Tuple[str, ...] = ( "UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", ), only_cross_attention: Union[bool, Tuple[bool]] = False, block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280), layers_per_block: int = 2, downsample_padding: int = 1, mid_block_scale_factor: float = 1, act_fn: str = "silu", norm_num_groups: Optional[int] = 32, norm_eps: float = 1e-5, cross_attention_dim: int = 1280, transformer_layers_per_block: Union[int, Tuple[int, ...]] = 1, encoder_hid_dim: Optional[int] = None, encoder_hid_dim_type: Optional[str] = None, attention_head_dim: Union[int, Tuple[int, ...]] = 8, num_attention_heads: Optional[Union[int, Tuple[int, ...]]] = None, use_linear_projection: bool = False, class_embed_type: Optional[str] = None, addition_embed_type: Optional[str] = None, addition_time_embed_dim: Optional[int] = None, num_class_embeds: Optional[int] = None, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", projection_class_embeddings_input_dim: Optional[int] = None, brushnet_conditioning_channel_order: str = "rgb", conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), global_pool_conditions: bool = False, addition_embed_type_num_heads: int = 64, ): super().__init__() # If `num_attention_heads` is not defined (which is the case for most models) # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. # The reason for this behavior is to correct for incorrectly named variables that were introduced # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking # which is why we correct for the naming here. num_attention_heads = num_attention_heads or attention_head_dim # Check inputs if len(down_block_types) != len(up_block_types): raise ValueError( f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." ) if len(block_out_channels) != len(down_block_types): raise ValueError( f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): raise ValueError( f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." ) if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): raise ValueError( f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." ) if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) # input conv_in_kernel = 3 conv_in_padding = (conv_in_kernel - 1) // 2 self.conv_in_condition = nn.Conv2d( in_channels+conditioning_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding ) # time time_embed_dim = block_out_channels[0] * 4 self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding( timestep_input_dim, time_embed_dim, act_fn=act_fn, ) if encoder_hid_dim_type is None and encoder_hid_dim is not None: encoder_hid_dim_type = "text_proj" self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") if encoder_hid_dim is None and encoder_hid_dim_type is not None: raise ValueError( f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." ) if encoder_hid_dim_type == "text_proj": self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) elif encoder_hid_dim_type == "text_image_proj": # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)` self.encoder_hid_proj = TextImageProjection( text_embed_dim=encoder_hid_dim, image_embed_dim=cross_attention_dim, cross_attention_dim=cross_attention_dim, ) elif encoder_hid_dim_type is not None: raise ValueError( f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." ) else: self.encoder_hid_proj = None # class embedding if class_embed_type is None and num_class_embeds is not None: self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) elif class_embed_type == "timestep": self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim) elif class_embed_type == "identity": self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) elif class_embed_type == "projection": if projection_class_embeddings_input_dim is None: raise ValueError( "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" ) # The projection `class_embed_type` is the same as the timestep `class_embed_type` except # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings # 2. it projects from an arbitrary input dimension. # # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. # As a result, `TimestepEmbedding` can be passed arbitrary vectors. self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) else: self.class_embedding = None if addition_embed_type == "text": if encoder_hid_dim is not None: text_time_embedding_from_dim = encoder_hid_dim else: text_time_embedding_from_dim = cross_attention_dim self.add_embedding = TextTimeEmbedding( text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads ) elif addition_embed_type == "text_image": # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)` self.add_embedding = TextImageTimeEmbedding( text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim ) elif addition_embed_type == "text_time": self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) elif addition_embed_type is not None: raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") self.down_blocks = nn.ModuleList([]) self.brushnet_down_blocks = nn.ModuleList([]) if isinstance(only_cross_attention, bool): only_cross_attention = [only_cross_attention] * len(down_block_types) if isinstance(attention_head_dim, int): attention_head_dim = (attention_head_dim,) * len(down_block_types) if isinstance(num_attention_heads, int): num_attention_heads = (num_attention_heads,) * len(down_block_types) # down output_channel = block_out_channels[0] brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) brushnet_block = zero_module(brushnet_block) self.brushnet_down_blocks.append(brushnet_block) #零卷积 for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block, transformer_layers_per_block=transformer_layers_per_block[i], in_channels=input_channel, out_channels=output_channel, temb_channels=time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads[i], attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, downsample_padding=downsample_padding, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) self.down_blocks.append(down_block) for _ in range(layers_per_block): brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) brushnet_block = zero_module(brushnet_block) self.brushnet_down_blocks.append(brushnet_block) #零卷积 if not is_final_block: brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) brushnet_block = zero_module(brushnet_block) self.brushnet_down_blocks.append(brushnet_block) # mid mid_block_channel = block_out_channels[-1] brushnet_block = nn.Conv2d(mid_block_channel, mid_block_channel, kernel_size=1) brushnet_block = zero_module(brushnet_block) self.brushnet_mid_block = brushnet_block self.mid_block = get_mid_block( mid_block_type, transformer_layers_per_block=transformer_layers_per_block[-1], in_channels=mid_block_channel, temb_channels=time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads[-1], resnet_groups=norm_num_groups, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, ) # count how many layers upsample the images self.num_upsamplers = 0 # up reversed_block_out_channels = list(reversed(block_out_channels)) reversed_num_attention_heads = list(reversed(num_attention_heads)) reversed_transformer_layers_per_block = (list(reversed(transformer_layers_per_block))) only_cross_attention = list(reversed(only_cross_attention)) output_channel = reversed_block_out_channels[0] self.up_blocks = nn.ModuleList([]) self.brushnet_up_blocks = nn.ModuleList([]) for i, up_block_type in enumerate(up_block_types): is_final_block = i == len(block_out_channels) - 1 prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] # add upsample block for all BUT final layer if not is_final_block: add_upsample = True self.num_upsamplers += 1 else: add_upsample = False up_block = get_up_block( up_block_type, num_layers=layers_per_block+1, transformer_layers_per_block=reversed_transformer_layers_per_block[i], in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=time_embed_dim, add_upsample=add_upsample, resnet_eps=norm_eps, resnet_act_fn=act_fn, resolution_idx=i, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, num_attention_heads=reversed_num_attention_heads[i], use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, ) self.up_blocks.append(up_block) prev_output_channel = output_channel for _ in range(layers_per_block+1): brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) brushnet_block = zero_module(brushnet_block) self.brushnet_up_blocks.append(brushnet_block) if not is_final_block: brushnet_block = nn.Conv2d(output_channel, output_channel, kernel_size=1) brushnet_block = zero_module(brushnet_block) self.brushnet_up_blocks.append(brushnet_block) @classmethod def from_unet( cls, unet: UNet2DConditionModel, brushnet_conditioning_channel_order: str = "rgb", conditioning_embedding_out_channels: Optional[Tuple[int, ...]] = (16, 32, 96, 256), load_weights_from_unet: bool = True, conditioning_channels: int = 5, ): r""" Instantiate a [`BrushNetModel`] from [`UNet2DConditionModel`]. Parameters: unet (`UNet2DConditionModel`): The UNet model weights to copy to the [`BrushNetModel`]. All configuration options are also copied where applicable. """ transformer_layers_per_block = ( unet.config.transformer_layers_per_block if "transformer_layers_per_block" in unet.config else 1 ) encoder_hid_dim = unet.config.encoder_hid_dim if "encoder_hid_dim" in unet.config else None encoder_hid_dim_type = unet.config.encoder_hid_dim_type if "encoder_hid_dim_type" in unet.config else None addition_embed_type = unet.config.addition_embed_type if "addition_embed_type" in unet.config else None addition_time_embed_dim = ( unet.config.addition_time_embed_dim if "addition_time_embed_dim" in unet.config else None ) brushnet = cls( in_channels=unet.config.in_channels, conditioning_channels=conditioning_channels, flip_sin_to_cos=unet.config.flip_sin_to_cos, freq_shift=unet.config.freq_shift, # down_block_types=['DownBlock2D','DownBlock2D','DownBlock2D','DownBlock2D'], down_block_types=[ "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D", ], # mid_block_type='MidBlock2D', mid_block_type="UNetMidBlock2DCrossAttn", # up_block_types=['UpBlock2D','UpBlock2D','UpBlock2D','UpBlock2D'], up_block_types=["UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"], only_cross_attention=unet.config.only_cross_attention, block_out_channels=unet.config.block_out_channels, layers_per_block=unet.config.layers_per_block, downsample_padding=unet.config.downsample_padding, mid_block_scale_factor=unet.config.mid_block_scale_factor, act_fn=unet.config.act_fn, norm_num_groups=unet.config.norm_num_groups, norm_eps=unet.config.norm_eps, cross_attention_dim=unet.config.cross_attention_dim, transformer_layers_per_block=transformer_layers_per_block, encoder_hid_dim=encoder_hid_dim, encoder_hid_dim_type=encoder_hid_dim_type, attention_head_dim=unet.config.attention_head_dim, num_attention_heads=unet.config.num_attention_heads, use_linear_projection=unet.config.use_linear_projection, class_embed_type=unet.config.class_embed_type, addition_embed_type=addition_embed_type, addition_time_embed_dim=addition_time_embed_dim, num_class_embeds=unet.config.num_class_embeds, upcast_attention=unet.config.upcast_attention, resnet_time_scale_shift=unet.config.resnet_time_scale_shift, projection_class_embeddings_input_dim=unet.config.projection_class_embeddings_input_dim, brushnet_conditioning_channel_order=brushnet_conditioning_channel_order, conditioning_embedding_out_channels=conditioning_embedding_out_channels, ) if load_weights_from_unet: conv_in_condition_weight=torch.zeros_like(brushnet.conv_in_condition.weight) conv_in_condition_weight[:,:4,...]=unet.conv_in.weight conv_in_condition_weight[:,4:8,...]=unet.conv_in.weight brushnet.conv_in_condition.weight=torch.nn.Parameter(conv_in_condition_weight) brushnet.conv_in_condition.bias=unet.conv_in.bias brushnet.time_proj.load_state_dict(unet.time_proj.state_dict()) brushnet.time_embedding.load_state_dict(unet.time_embedding.state_dict()) if brushnet.class_embedding: brushnet.class_embedding.load_state_dict(unet.class_embedding.state_dict()) brushnet.down_blocks.load_state_dict(unet.down_blocks.state_dict(),strict=False) brushnet.mid_block.load_state_dict(unet.mid_block.state_dict(),strict=False) brushnet.up_blocks.load_state_dict(unet.up_blocks.state_dict(),strict=False) return brushnet @property # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.attn_processors def attn_processors(self) -> Dict[str, AttentionProcessor]: r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. """ # set recursively processors = {} def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): if hasattr(module, "get_processor"): processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): fn_recursive_add_processors(name, module, processors) return processors # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attn_processor def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor for **all** `Attention` layers. If `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors. """ count = len(self.attn_processors.keys()) if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" f" number of attention layers: {count}. Please make sure to pass {count} processor classes." ) def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor) else: module.set_processor(processor.pop(f"{name}.processor")) for sub_name, child in module.named_children(): fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor def set_default_attn_processor(self): """ Disables custom attention processors and sets the default attention implementation. """ if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnAddedKVProcessor() elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnProcessor() else: raise ValueError( f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" ) self.set_attn_processor(processor) # Copied from diffusers.models.unets.unet_2d_condition.UNet2DConditionModel.set_attention_slice def set_attention_slice(self, slice_size: Union[str, int, List[int]]) -> None: r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compute attention in several steps. This is useful for saving some memory in exchange for a small decrease in speed. Args: slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ sliceable_head_dims = [] def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): if hasattr(module, "set_attention_slice"): sliceable_head_dims.append(module.sliceable_head_dim) for child in module.children(): fn_recursive_retrieve_sliceable_dims(child) # retrieve number of attention layers for module in self.children(): fn_recursive_retrieve_sliceable_dims(module) num_sliceable_layers = len(sliceable_head_dims) if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = [dim // 2 for dim in sliceable_head_dims] elif slice_size == "max": # make smallest slice possible slice_size = num_sliceable_layers * [1] slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size if len(slice_size) != len(sliceable_head_dims): raise ValueError( f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." ) for i in range(len(slice_size)): size = slice_size[i] dim = sliceable_head_dims[i] if size is not None and size > dim: raise ValueError(f"size {size} has to be smaller or equal to {dim}.") # Recursively walk through all the children. # Any children which exposes the set_attention_slice method # gets the message def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): if hasattr(module, "set_attention_slice"): module.set_attention_slice(slice_size.pop()) for child in module.children(): fn_recursive_set_attention_slice(child, slice_size) reversed_slice_size = list(reversed(slice_size)) for module in self.children(): fn_recursive_set_attention_slice(module, reversed_slice_size) def _set_gradient_checkpointing(self, module, value: bool = False) -> None: if isinstance(module, (CrossAttnDownBlock2D, DownBlock2D)): module.gradient_checkpointing = value def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, brushnet_cond: torch.FloatTensor, conditioning_scale: float = 1.0, class_labels: Optional[torch.Tensor] = None, timestep_cond: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guess_mode: bool = False, return_dict: bool = True, ) -> Union[BrushNetOutput, Tuple[Tuple[torch.FloatTensor, ...], torch.FloatTensor]]: """ The [`BrushNetModel`] forward method. Args: sample (`torch.FloatTensor`): The noisy input tensor. timestep (`Union[torch.Tensor, float, int]`): The number of timesteps to denoise an input. encoder_hidden_states (`torch.Tensor`): The encoder hidden states. brushnet_cond (`torch.FloatTensor`): The conditional input tensor of shape `(batch_size, sequence_length, hidden_size)`. conditioning_scale (`float`, defaults to `1.0`): The scale factor for BrushNet outputs. class_labels (`torch.Tensor`, *optional*, defaults to `None`): Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. timestep_cond (`torch.Tensor`, *optional*, defaults to `None`): Additional conditional embeddings for timestep. If provided, the embeddings will be summed with the timestep_embedding passed through the `self.time_embedding` layer to obtain the final timestep embeddings. attention_mask (`torch.Tensor`, *optional*, defaults to `None`): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. added_cond_kwargs (`dict`): Additional conditions for the Stable Diffusion XL UNet. cross_attention_kwargs (`dict[str]`, *optional*, defaults to `None`): A kwargs dictionary that if specified is passed along to the `AttnProcessor`. guess_mode (`bool`, defaults to `False`): In this mode, the BrushNet encoder tries its best to recognize the input content of the input even if you remove all prompts. A `guidance_scale` between 3.0 and 5.0 is recommended. return_dict (`bool`, defaults to `True`): Whether or not to return a [`~models.brushnet.BrushNetOutput`] instead of a plain tuple. Returns: [`~models.brushnet.BrushNetOutput`] **or** `tuple`: If `return_dict` is `True`, a [`~models.brushnet.BrushNetOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. """ # check channel order channel_order = self.config.brushnet_conditioning_channel_order if channel_order == "rgb": # in rgb order by default ... elif channel_order == "bgr": brushnet_cond = torch.flip(brushnet_cond, dims=[1]) else: raise ValueError(f"unknown `brushnet_conditioning_channel_order`: {channel_order}") # prepare attention_mask if attention_mask is not None: attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # 1. time timesteps = timestep if not torch.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) is_mps = sample.device.type == "mps" if isinstance(timestep, float): dtype = torch.float32 if is_mps else torch.float64 else: dtype = torch.int32 if is_mps else torch.int64 timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps.expand(sample.shape[0]) t_emb = self.time_proj(timesteps) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=sample.dtype) emb = self.time_embedding(t_emb, timestep_cond) aug_emb = None if self.class_embedding is not None: if class_labels is None: raise ValueError("class_labels should be provided when num_class_embeds > 0") if self.config.class_embed_type == "timestep": class_labels = self.time_proj(class_labels) class_emb = self.class_embedding(class_labels).to(dtype=self.dtype) emb = emb + class_emb if self.config.addition_embed_type is not None: if self.config.addition_embed_type == "text": aug_emb = self.add_embedding(encoder_hidden_states) elif self.config.addition_embed_type == "text_time": if "text_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" ) text_embeds = added_cond_kwargs.get("text_embeds") if "time_ids" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" ) time_ids = added_cond_kwargs.get("time_ids") time_embeds = self.add_time_proj(time_ids.flatten()) time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) add_embeds = add_embeds.to(emb.dtype) aug_emb = self.add_embedding(add_embeds) emb = emb + aug_emb if aug_emb is not None else emb # 2. pre-process brushnet_cond=torch.concat([sample,brushnet_cond],1) sample = self.conv_in_condition(brushnet_cond) # 3. down down_block_res_samples = (sample,) for downsample_block in self.down_blocks: if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) else: sample, res_samples = downsample_block(hidden_states=sample, temb=emb) down_block_res_samples += res_samples # 4. PaintingNet down blocks brushnet_down_block_res_samples = () for down_block_res_sample, brushnet_down_block in zip(down_block_res_samples, self.brushnet_down_blocks): down_block_res_sample = brushnet_down_block(down_block_res_sample) brushnet_down_block_res_samples = brushnet_down_block_res_samples + (down_block_res_sample,) # 5. mid if self.mid_block is not None: if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, ) else: sample = self.mid_block(sample, emb) # 6. BrushNet mid blocks brushnet_mid_block_res_sample = self.brushnet_mid_block(sample) # 7. up up_block_res_samples = () for i, upsample_block in enumerate(self.up_blocks): is_final_block = i == len(self.up_blocks) - 1 res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # if we have not reached the final block and need to forward the # upsample size, we do it here if not is_final_block: upsample_size = down_block_res_samples[-1].shape[2:] if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: sample, up_res_samples = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, upsample_size=upsample_size, attention_mask=attention_mask, return_res_samples=True ) else: sample, up_res_samples = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, return_res_samples=True ) up_block_res_samples += up_res_samples # 8. BrushNet up blocks brushnet_up_block_res_samples = () for up_block_res_sample, brushnet_up_block in zip(up_block_res_samples, self.brushnet_up_blocks): up_block_res_sample = brushnet_up_block(up_block_res_sample) brushnet_up_block_res_samples = brushnet_up_block_res_samples + (up_block_res_sample,) # 6. scaling if guess_mode and not self.config.global_pool_conditions: scales = torch.logspace(-1, 0, len(brushnet_down_block_res_samples) + 1 + len(brushnet_up_block_res_samples), device=sample.device) # 0.1 to 1.0 scales = scales * conditioning_scale brushnet_down_block_res_samples = [sample * scale for sample, scale in zip(brushnet_down_block_res_samples, scales[:len(brushnet_down_block_res_samples)])] brushnet_mid_block_res_sample = brushnet_mid_block_res_sample * scales[len(brushnet_down_block_res_samples)] brushnet_up_block_res_samples = [sample * scale for sample, scale in zip(brushnet_up_block_res_samples, scales[len(brushnet_down_block_res_samples)+1:])] else: brushnet_down_block_res_samples = [sample * conditioning_scale for sample in brushnet_down_block_res_samples] brushnet_mid_block_res_sample = brushnet_mid_block_res_sample * conditioning_scale brushnet_up_block_res_samples = [sample * conditioning_scale for sample in brushnet_up_block_res_samples] if self.config.global_pool_conditions: brushnet_down_block_res_samples = [ torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_down_block_res_samples ] brushnet_mid_block_res_sample = torch.mean(brushnet_mid_block_res_sample, dim=(2, 3), keepdim=True) brushnet_up_block_res_samples = [ torch.mean(sample, dim=(2, 3), keepdim=True) for sample in brushnet_up_block_res_samples ] if not return_dict: return (brushnet_down_block_res_samples, brushnet_mid_block_res_sample, brushnet_up_block_res_samples) return BrushNetOutput( down_block_res_samples=brushnet_down_block_res_samples, mid_block_res_sample=brushnet_mid_block_res_sample, up_block_res_samples=brushnet_up_block_res_samples ) def zero_module(module): for p in module.parameters(): nn.init.zeros_(p) return module ================================================ FILE: libs/diffueraser.py ================================================ import gc import copy import cv2 import os import numpy as np import torch import torchvision import re import random from einops import repeat from PIL import Image, ImageFilter from diffusers import ( AutoencoderKL, DDPMScheduler, UniPCMultistepScheduler, LCMScheduler, StableDiffusionPipeline ) from diffusers.schedulers import TCDScheduler from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.utils.torch_utils import randn_tensor from transformers import AutoTokenizer, PretrainedConfig from safetensors.torch import load_file from .unet_motion_model import MotionAdapter, UNetMotionModel from .brushnet_CA import BrushNetModel from .unet_2d_condition import UNet2DConditionModel from .pipeline_diffueraser import StableDiffusionDiffuEraserPipeline def extract_step_number(ckpt_name): # 使用正则表达式查找 "step" 前面的数字 match = re.search(r'(\d+)-Step', ckpt_name) if match: return int(match.group(1)) else: return 2 checkpoints = { "2-Step": ["pcm_{}_smallcfg_2step_converted.safetensors", 2, 0.0], "4-Step": ["pcm_{}_smallcfg_4step_converted.safetensors", 4, 0.0], "8-Step": ["pcm_{}_smallcfg_8step_converted.safetensors", 8, 0.0], "16-Step": ["pcm_{}_smallcfg_16step_converted.safetensors", 16, 0.0], "Normal CFG 4-Step": ["pcm_{}_normalcfg_4step_converted.safetensors", 4, 7.5], "Normal CFG 8-Step": ["pcm_{}_normalcfg_8step_converted.safetensors", 8, 7.5], "Normal CFG 16-Step": ["pcm_{}_normalcfg_16step_converted.safetensors", 16, 7.5], "LCM-Like LoRA": [ "pcm_{}_lcmlike_lora_converted.safetensors", 4, 0.0, ], } def import_model_class_from_model_name_or_path(pretrained_model_name_or_path: str, revision: str): text_encoder_config = PretrainedConfig.from_pretrained( pretrained_model_name_or_path, subfolder="text_encoder", revision=revision, ) model_class = text_encoder_config.architectures[0] if model_class == "CLIPTextModel": from transformers import CLIPTextModel return CLIPTextModel elif model_class == "RobertaSeriesModelWithTransformation": try: from diffusers.pipelines.alt_diffusion.modeling_roberta_series import RobertaSeriesModelWithTransformation # old diffusers version return RobertaSeriesModelWithTransformation except: print("Error: Could not import RobertaSeriesModelWithTransformation.") raise ValueError(f"{model_class} is not supported.") else: raise ValueError(f"{model_class} is not supported.") def resize_frames(frames, size=None): if size is not None: out_size = size process_size = (out_size[0] - out_size[0] % 8, out_size[1] - out_size[1] % 8) frames = [f.resize(process_size) for f in frames] else: out_size = frames[0].size process_size = (out_size[0] - out_size[0] % 8, out_size[1] - out_size[1] % 8) if not out_size == process_size: frames = [f.resize(process_size) for f in frames] return frames def read_mask(validation_mask, fps, n_total_frames, img_size, mask_dilation_iter, frames): cap = cv2.VideoCapture(validation_mask) if not cap.isOpened(): print("Error: Could not open mask video.") exit() mask_fps = cap.get(cv2.CAP_PROP_FPS) if mask_fps != fps: cap.release() raise ValueError("The frame rate of all input videos needs to be consistent.") masks = [] masked_images = [] idx = 0 while True: ret, frame = cap.read() if not ret: break if(idx >= n_total_frames): break mask = Image.fromarray(frame[...,::-1]).convert('L') if mask.size != img_size: mask = mask.resize(img_size, Image.NEAREST) mask = np.asarray(mask) m = np.array(mask > 0).astype(np.uint8) m = cv2.erode(m, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)), iterations=1) m = cv2.dilate(m, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)), iterations=mask_dilation_iter) mask = Image.fromarray(m * 255) masks.append(mask) masked_image = np.array(frames[idx])*(1-(np.array(mask)[:,:,np.newaxis].astype(np.float32)/255)) masked_image = Image.fromarray(masked_image.astype(np.uint8)) masked_images.append(masked_image) idx += 1 cap.release() return masks, masked_images def read_priori(priori, fps, n_total_frames, img_size): cap = cv2.VideoCapture(priori) if not cap.isOpened(): print("Error: Could not open video.") exit() priori_fps = cap.get(cv2.CAP_PROP_FPS) if (priori_fps - fps) > 1e-8: print(f"priori fps: {priori_fps}, fps: {fps}") cap.release() raise ValueError("The frame rate of all input videos needs to be consistent.") prioris=[] idx = 0 while True: ret, frame = cap.read() if not ret: break if(idx >= n_total_frames): break img = Image.fromarray(frame[...,::-1]) if img.size != img_size: img = img.resize(img_size) prioris.append(img) idx += 1 cap.release() os.remove(priori) # remove priori return prioris def read_video(validation_image, video_length, nframes, max_img_size): vframes, aframes, info = torchvision.io.read_video(filename=validation_image, pts_unit='sec', end_pts=video_length) # RGB fps = info['video_fps'] n_total_frames = int(video_length * fps) n_clip = int(np.ceil(n_total_frames/nframes)) frames = list(vframes.numpy())[:n_total_frames] frames = [Image.fromarray(f) for f in frames] max_size = max(frames[0].size) if(max_size<256): raise ValueError("The resolution of the uploaded video must be larger than 256x256.") if(max_size>4096): raise ValueError("The resolution of the uploaded video must be smaller than 4096x4096.") if max_size>max_img_size: ratio = max_size/max_img_size ratio_size = (int(frames[0].size[0]/ratio),int(frames[0].size[1]/ratio)) img_size = (ratio_size[0]-ratio_size[0]%8, ratio_size[1]-ratio_size[1]%8) resize_flag=True elif (frames[0].size[0]%8==0) and (frames[0].size[1]%8==0): img_size = frames[0].size resize_flag=False else: ratio_size = frames[0].size img_size = (ratio_size[0]-ratio_size[0]%8, ratio_size[1]-ratio_size[1]%8) resize_flag=True if resize_flag: frames = resize_frames(frames, img_size) img_size = frames[0].size return frames, fps, img_size, n_clip, n_total_frames class DiffuEraser: def __init__(self, device, ): self.device = device def load_model(self,repo, diffueraser_path, ckpt_path,original_config_file,ckpt="Normal CFG 4-Step",): self.noise_scheduler = DDPMScheduler.from_pretrained(repo, subfolder="scheduler", prediction_type="v_prediction", timestep_spacing="trailing", rescale_betas_zero_snr=True ) self.tokenizer = AutoTokenizer.from_pretrained( repo, subfolder="tokenizer", use_fast=False, ) vae_config=AutoencoderKL.load_config(os.path.join(repo,"vae/config.json")) self.vae=AutoencoderKL.from_config(vae_config) self.vae.load_state_dict(load_file(ckpt_path) if ckpt_path.endswith(".safetensors") else torch.load(ckpt_path,weights_only=False),strict=False) #self.vae=AutoencoderKL.from_single_file(ckpt_path,config=os.path.join(repo,"vae") ) # try: # pipe = StableDiffusionPipeline.from_single_file( # ckpt_path,config=repo, original_config=original_config_file) # except: # pipe = StableDiffusionPipeline.from_single_file( # ckpt_path, config=repo,original_config_file=original_config_file) # self.text_encoder = pipe.text_encoder #self.vae = pipe.vae #del pipe gc.collect() torch.cuda.empty_cache() self.brushnet = BrushNetModel.from_pretrained(diffueraser_path, subfolder="brushnet") self.unet_main = UNetMotionModel.from_pretrained( diffueraser_path, subfolder="unet_main", ) ## set pipeline self.pipeline = StableDiffusionDiffuEraserPipeline.from_pretrained( repo, vae=self.vae, text_encoder=None, tokenizer=self.tokenizer, unet=self.unet_main, brushnet=self.brushnet, safety_checker=None,#no need ).to(self.device, torch.float16) # self.vae=None # self.text_encoder=None self.pipeline.scheduler = UniPCMultistepScheduler.from_config(self.pipeline.scheduler.config) self.pipeline.set_progress_bar_config(disable=True) self.noise_scheduler = UniPCMultistepScheduler.from_config(self.pipeline.scheduler.config) self.vae_scale_factor = 2 ** (len(self.pipeline.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True) try: self.pipeline.load_lora_weights(pretrained_model_name_or_path_or_dict=ckpt) print("Loaded lora from", ckpt) except Exception as e: print(f"Failed to apply LoRA {str(e)}") pass if "lcmlike" in ckpt.lower(): self.pipeline.scheduler = LCMScheduler() self.num_inference_steps= 4 else: self.pipeline.scheduler = TCDScheduler( num_train_timesteps=1000, beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", timestep_spacing="trailing", ) self.num_inference_steps=extract_step_number(ckpt) #self.num_inference_steps = checkpoints[ckpt][1] if "normal" in ckpt.lower(): self.guidance_scale = 7.5 else: self.guidance_scale = 0 #self.guidance_scale = 0 def to(self, device): self.device=device self.pipeline.to(device) def forward(self, validation_image, validation_mask, prioris, output_path,positive,load_videobypath=False, max_img_size = 1280, video_length=2, mask_dilation_iter=4, nframes=22, seed=None, revision = None, guidance_scale=None, blended=True,num_inference_steps=None,fps=24,img_size=(512, 512),if_save_video=False): validation_prompt = "" # guidance_scale_final = self.guidance_scale if guidance_scale==None else guidance_scale num_inference_steps_final = self.num_inference_steps if num_inference_steps==None else num_inference_steps if (max_img_size<256 or max_img_size>1920): raise ValueError("The max_img_size must be larger than 256, smaller than 1920.") ################ read input video ################ if load_videobypath: frames, fps, img_size, n_clip, n_total_frames = read_video(validation_image, video_length, nframes, max_img_size) else: frames=validation_image n_total_frames=len(validation_image) n_clip = int(np.ceil(n_total_frames/nframes)) video_len = len(frames) #frames[0].save("input0.png") ################ read mask ################ if load_videobypath: validation_masks_input, validation_images_input = read_mask(validation_mask, fps, video_len, img_size, mask_dilation_iter, frames) else: validation_masks_list=[i.convert('L') for i in validation_mask.copy()] validation_images_input=[] validation_masks_input=[] for idx ,mask in enumerate(validation_masks_list): #mask = Image.fromarray(i[...,::-1]).convert('L') if mask.size != img_size: mask = mask.resize(img_size, Image.NEAREST) mask = np.asarray(mask) m = np.array(mask > 0).astype(np.uint8) m = cv2.erode(m, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)), iterations=1) m = cv2.dilate(m, cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)), iterations=mask_dilation_iter) mask = Image.fromarray(m * 255) validation_masks_input.append(mask) masked_image = np.array(frames[idx])*(1-(np.array(mask)[:,:,np.newaxis].astype(np.float32)/255)) masked_image = Image.fromarray(masked_image.astype(np.uint8)) validation_images_input.append(masked_image) ################ read priori ################ #validation_images_input[0].save("input1.png") #prioris = read_priori(priori, fps, n_total_frames, img_size) if prioris[0].size != img_size: prioris = [img.resize(img_size) for img in prioris] ## recheck n_total_frames = min(min(len(frames), len(validation_masks_input)), len(prioris)) if(n_total_frames<22): raise ValueError("The effective video duration is too short. Please make sure that the number of frames of video, mask, and priori is at least greater than 22 frames.") validation_masks_input = validation_masks_input[:n_total_frames] validation_images_input = validation_images_input[:n_total_frames] frames = frames[:n_total_frames] prioris = prioris[:n_total_frames] prioris = resize_frames(prioris) validation_masks_input = resize_frames(validation_masks_input) validation_images_input = resize_frames(validation_images_input) resized_frames = resize_frames(frames) #resized_frames[0].save("input2.png") ############################################## # DiffuEraser inference ############################################## print("DiffuEraser inference...") if seed is None: generator = None else: generator = torch.Generator(device=self.device).manual_seed(seed) ## random noise real_video_length = len(validation_images_input) tar_width, tar_height = validation_images_input[0].size shape = ( nframes, 4, tar_height//8, tar_width//8 ) if self.unet_main is not None: prompt_embeds_dtype = self.unet_main.dtype else: prompt_embeds_dtype = torch.float16 noise_pre = randn_tensor(shape, device=torch.device(self.device), dtype=prompt_embeds_dtype, generator=generator) noise = repeat(noise_pre, "t c h w->(repeat t) c h w", repeat=n_clip)[:real_video_length,...] ################ prepare priori ################ images_preprocessed = [] for image in prioris: image = self.image_processor.preprocess(image, height=tar_height, width=tar_width).to(dtype=torch.float32) image = image.to(device=torch.device(self.device), dtype=torch.float16) images_preprocessed.append(image) pixel_values = torch.cat(images_preprocessed) with torch.no_grad(): pixel_values = pixel_values.to(dtype=torch.float16) latents = [] num=4 for i in range(0, pixel_values.shape[0], num): latents.append(self.pipeline.vae.encode(pixel_values[i : i + num]).latent_dist.sample()) latents = torch.cat(latents, dim=0) latents = latents * self.pipeline.vae.config.scaling_factor #[(b f), c1, h, w], c1=4 self.pipeline.vae.to("cpu") torch.cuda.empty_cache() timesteps = torch.tensor([0], device=self.device) timesteps = timesteps.long() validation_masks_input_ori = copy.deepcopy(validation_masks_input) resized_frames_ori = copy.deepcopy(resized_frames) ################ Pre-inference ################ if n_total_frames > nframes*2: ## do pre-inference only when number of input frames is larger than nframes*2 ## sample step = n_total_frames / nframes sample_index = [int(i * step) for i in range(nframes)] sample_index = sample_index[:22] validation_masks_input_pre = [validation_masks_input[i] for i in sample_index] validation_images_input_pre = [validation_images_input[i] for i in sample_index] latents_pre = torch.stack([latents[i] for i in sample_index]) ## add proiri noisy_latents_pre = self.noise_scheduler.add_noise(latents_pre, noise_pre, timesteps) latents_pre = noisy_latents_pre with torch.no_grad(): latents_pre_out = self.pipeline( num_frames=nframes, prompt=None, images=validation_images_input_pre, masks=validation_masks_input_pre, prompt_embeds=positive[0][0], num_inference_steps=num_inference_steps_final, generator=generator, guidance_scale=guidance_scale_final, latents=latents_pre, ).latents torch.cuda.empty_cache() def decode_latents(latents, weight_dtype): latents = 1 / self.pipeline.vae.config.scaling_factor * latents video = [] for t in range(latents.shape[0]): video.append(self.pipeline.vae.decode(latents[t:t+1, ...].to(weight_dtype)).sample) video = torch.concat(video, dim=0) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 video = video.float() return video with torch.no_grad(): video_tensor_temp = decode_latents(latents_pre_out, weight_dtype=torch.float16) images_pre_out = self.image_processor.postprocess(video_tensor_temp, output_type="pil") torch.cuda.empty_cache() ## replace input frames with updated frames black_image = Image.new('L', validation_masks_input[0].size, color=0) for i,index in enumerate(sample_index): latents[index] = latents_pre_out[i] validation_masks_input[index] = black_image validation_images_input[index] = images_pre_out[i] resized_frames[index] = images_pre_out[i] else: latents_pre_out=None sample_index=None gc.collect() torch.cuda.empty_cache() ################ Frame-by-frame inference ################ ## add priori noisy_latents = self.noise_scheduler.add_noise(latents, noise, timesteps) latents = noisy_latents with torch.no_grad(): images = self.pipeline( num_frames=nframes, prompt=None, images=validation_images_input, masks=validation_masks_input, prompt_embeds=positive[0][0], num_inference_steps=num_inference_steps_final, generator=generator, guidance_scale=guidance_scale_final, latents=latents, ).frames images = images[:real_video_length] gc.collect() torch.cuda.empty_cache() ################ Compose ################ binary_masks = validation_masks_input_ori mask_blurreds = [] if blended: for i in range(len(binary_masks)): mask_array = np.array(binary_masks[i]) mask_blurred = morphological_edge_blur(np.array(mask_array), sigma=2.0, edge_width=3) #mask_blurred = cv2.GaussianBlur(np.array(binary_masks[i]), blur_kernel, 0)/255. binary_mask = 1-(1-mask_array/255.) * (1-mask_blurred) mask_blurreds.append(Image.fromarray((binary_mask*255).astype(np.uint8))) binary_masks = mask_blurreds comp_frames = [] for i in range(len(images)): mask = np.expand_dims(np.array(binary_masks[i]),2).repeat(3, axis=2).astype(np.float32)/255. img = (np.array(images[i]).astype(np.uint8) * mask + np.array(resized_frames_ori[i]).astype(np.uint8) * (1 - mask)).astype(np.uint8) comp_frames.append(Image.fromarray(img)) else: comp_frames = simple_flicker_smoothing(images, alpha=0.15) if if_save_video: default_fps = fps prefix = ''.join(random.choice("0123456789") for _ in range(6)) priori_path = os.path.join(output_path, f"priori_{prefix}.mp4") os.makedirs(os.path.dirname(priori_path), exist_ok=True) writer = cv2.VideoWriter(priori_path, cv2.VideoWriter_fourcc(*"mp4v"), default_fps, comp_frames[0].size) for f in range(real_video_length): img = np.array(comp_frames[f]).astype(np.uint8) writer.write(cv2.cvtColor(img, cv2.COLOR_BGR2RGB)) writer.release() ################################ return comp_frames def simple_flicker_smoothing(frames, alpha=0.1): """ 简单的闪烁平滑，最小化对动态内容的影响 """ if len(frames) < 2: return frames smoothed_frames = [frames[0]] for i in range(1, len(frames)): current = np.array(frames[i]).astype(np.float32) previous = np.array(frames[i-1]).astype(np.float32) # 只对变化很小的像素进行平滑（可能是闪烁） diff = np.abs(current - previous) static_mask = (diff < 10.0).astype(np.float32) # 阈值可根据需要调整 # 只在静态区域应用轻微平滑 smoothed = previous * alpha * static_mask + current * (1 - alpha * static_mask) smoothed_frames.append(Image.fromarray(np.clip(smoothed, 0, 255).astype(np.uint8))) return smoothed_frames def morphological_edge_blur(mask, sigma=3.0, edge_width=5): """ 使用形态学操作提取边缘并只模糊边缘区域 """ if mask.dtype != np.float32: mask_float = mask.astype(np.float32) else: mask_float = mask.copy() # 转换为二值图像 binary_mask = (mask_float > 0.5).astype(np.uint8) if not np.any(binary_mask): return mask_float # 创建边缘遮罩 # 腐蚀操作缩小遮罩 kernel_size = max(3, edge_width) kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (kernel_size, kernel_size)) eroded = cv2.erode(binary_mask, kernel, iterations=1) # 边缘 = 原始遮罩 - 腐蚀后的遮罩 edge_mask = binary_mask - eroded # 只对边缘区域进行高斯模糊 edge_region = mask_float * edge_mask.astype(np.float32) # 模糊边缘区域 ksize = int(2 * np.ceil(3 * sigma) + 1) ksize = max(3, min(101, ksize if ksize % 2 == 1 else ksize + 1)) blurred_edges = cv2.GaussianBlur(edge_region, (ksize, ksize), sigmaX=sigma, sigmaY=sigma) # 合成结果：内部保持原值，边缘使用模糊值 inner_region = mask_float * eroded.astype(np.float32) result = inner_region + blurred_edges return np.clip(result, 0.0, 1.0) ================================================ FILE: libs/pipeline_diffueraser.py ================================================ import inspect from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np import PIL.Image from einops import rearrange, repeat from dataclasses import dataclass import copy import torch import torch.nn.functional as F from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection from diffusers.image_processor import PipelineImageInput, VaeImageProcessor from diffusers.loaders import FromSingleFileMixin, IPAdapterMixin, LoraLoaderMixin, TextualInversionLoaderMixin from diffusers.models import AutoencoderKL, ImageProjection from diffusers.models.lora import adjust_lora_scale_text_encoder from diffusers.schedulers import KarrasDiffusionSchedulers from diffusers.utils import ( USE_PEFT_BACKEND, deprecate, logging, replace_example_docstring, scale_lora_layers, unscale_lora_layers, BaseOutput ) from diffusers.utils.torch_utils import is_compiled_module, is_torch_version, randn_tensor from diffusers.pipelines.pipeline_utils import DiffusionPipeline, StableDiffusionMixin from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker from diffusers import ( AutoencoderKL, DDPMScheduler, UniPCMultistepScheduler, ) from .unet_2d_condition import UNet2DConditionModel from .brushnet_CA import BrushNetModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.retrieve_timesteps def retrieve_timesteps( scheduler, num_inference_steps: Optional[int] = None, device: Optional[Union[str, torch.device]] = None, timesteps: Optional[List[int]] = None, **kwargs, ): """ Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs will be supplied to `scheduler.set_timesteps`. Args: scheduler (`SchedulerMixin`): The scheduler to get timesteps from. num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. If used, `timesteps` must be `None`. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. timesteps (`List[int]`, *optional*): Custom timesteps used to support arbitrary spacing between timesteps. If `None`, then the default timestep spacing strategy of the scheduler is used. If `timesteps` is passed, `num_inference_steps` must be `None`. Returns: `Tuple[torch.Tensor, int]`: A tuple where the first element is the timestep schedule from the scheduler and the second element is the number of inference steps. """ if timesteps is not None: accepts_timesteps = "timesteps" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accepts_timesteps: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" timestep schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(timesteps=timesteps, device=device, **kwargs) timesteps = scheduler.timesteps num_inference_steps = len(timesteps) else: scheduler.set_timesteps(num_inference_steps, device=device, **kwargs) timesteps = scheduler.timesteps return timesteps, num_inference_steps def get_frames_context_swap(total_frames=192, overlap=4, num_frames_per_clip=24): if total_framesnum_frames_per_clip: ## [0,num_frames_per_clip-1], [num_frames_per_clip, 2*num_frames_per_clip-1].... for k in range(0,n-num_frames_per_clip,num_frames_per_clip-overlap): context_list.append(sample_interval[k:k+num_frames_per_clip]) if k+num_frames_per_clip < n and i==1: context_list.append(sample_interval[n-num_frames_per_clip:n]) context_list_swap.append(sample_interval[0:num_frames_per_clip]) for k in range(num_frames_per_clip//2, n-num_frames_per_clip, num_frames_per_clip-overlap): context_list_swap.append(sample_interval[k:k+num_frames_per_clip]) if k+num_frames_per_clip < n and i==1: context_list_swap.append(sample_interval[n-num_frames_per_clip:n]) if n==num_frames_per_clip: context_list.append(sample_interval[n-num_frames_per_clip:n]) context_list_swap.append(sample_interval[n-num_frames_per_clip:n]) return context_list, context_list_swap @dataclass class DiffuEraserPipelineOutput(BaseOutput): frames: Union[torch.Tensor, np.ndarray] latents: Union[torch.Tensor, np.ndarray] class StableDiffusionDiffuEraserPipeline( DiffusionPipeline, StableDiffusionMixin, TextualInversionLoaderMixin, LoraLoaderMixin, IPAdapterMixin, FromSingleFileMixin, ): r""" Pipeline for video inpainting using Video Diffusion Model with BrushNet guidance. This model inherits from [`DiffusionPipeline`]. Check the superclass documentation for the generic methods implemented for all pipelines (downloading, saving, running on a particular device, etc.). The pipeline also inherits the following loading methods: - [`~loaders.TextualInversionLoaderMixin.load_textual_inversion`] for loading textual inversion embeddings - [`~loaders.LoraLoaderMixin.load_lora_weights`] for loading LoRA weights - [`~loaders.LoraLoaderMixin.save_lora_weights`] for saving LoRA weights - [`~loaders.FromSingleFileMixin.from_single_file`] for loading `.ckpt` files - [`~loaders.IPAdapterMixin.load_ip_adapter`] for loading IP Adapters Args: vae ([`AutoencoderKL`]): Variational Auto-Encoder (VAE) model to encode and decode images to and from latent representations. text_encoder ([`~transformers.CLIPTextModel`]): Frozen text-encoder ([clip-vit-large-patch14](https://huggingface.co/openai/clip-vit-large-patch14)). tokenizer ([`~transformers.CLIPTokenizer`]): A `CLIPTokenizer` to tokenize text. unet ([`UNet2DConditionModel`]): A `UNet2DConditionModel` to denoise the encoded image latents. brushnet ([`BrushNetModel`]`): Provides additional conditioning to the `unet` during the denoising process. scheduler ([`SchedulerMixin`]): A scheduler to be used in combination with `unet` to denoise the encoded image latents. Can be one of [`DDIMScheduler`], [`LMSDiscreteScheduler`], or [`PNDMScheduler`]. safety_checker ([`StableDiffusionSafetyChecker`]): Classification module that estimates whether generated images could be considered offensive or harmful. Please refer to the [model card](https://huggingface.co/runwayml/stable-diffusion-v1-5) for more details about a model's potential harms. feature_extractor ([`~transformers.CLIPImageProcessor`]): A `CLIPImageProcessor` to extract features from generated images; used as inputs to the `safety_checker`. """ model_cpu_offload_seq = "text_encoder->image_encoder->unet->vae" _optional_components = ["safety_checker", "feature_extractor", "image_encoder"] _exclude_from_cpu_offload = ["safety_checker"] _callback_tensor_inputs = ["latents", "prompt_embeds", "negative_prompt_embeds"] def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, brushnet: BrushNetModel, scheduler: KarrasDiffusionSchedulers, safety_checker: StableDiffusionSafetyChecker, feature_extractor: CLIPImageProcessor, image_encoder: CLIPVisionModelWithProjection = None, requires_safety_checker: bool = True, ): super().__init__() if safety_checker is None and requires_safety_checker: logger.warning( f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure" " that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered" " results in services or applications open to the public. Both the diffusers team and Hugging Face" " strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling" " it only for use-cases that involve analyzing network behavior or auditing its results. For more" " information, please have a look at https://github.com/huggingface/diffusers/pull/254 ." ) if safety_checker is not None and feature_extractor is None: raise ValueError( "Make sure to define a feature extractor when loading {self.__class__} if you want to use the safety" " checker. If you do not want to use the safety checker, you can pass `'safety_checker=None'` instead." ) self.register_modules( vae=vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, brushnet=brushnet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=feature_extractor, image_encoder=image_encoder, ) self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor, do_convert_rgb=True) self.register_to_config(requires_safety_checker=requires_safety_checker) # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._encode_prompt def _encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, lora_scale: Optional[float] = None, **kwargs, ): deprecation_message = "`_encode_prompt()` is deprecated and it will be removed in a future version. Use `encode_prompt()` instead. Also, be aware that the output format changed from a concatenated tensor to a tuple." deprecate("_encode_prompt()", "1.0.0", deprecation_message, standard_warn=False) prompt_embeds_tuple = self.encode_prompt( prompt=prompt, device=device, num_images_per_prompt=num_images_per_prompt, do_classifier_free_guidance=do_classifier_free_guidance, negative_prompt=negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=lora_scale, **kwargs, ) # concatenate for backwards comp prompt_embeds = torch.cat([prompt_embeds_tuple[1], prompt_embeds_tuple[0]]) return prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_prompt def encode_prompt( self, prompt, device, num_images_per_prompt, do_classifier_free_guidance, negative_prompt=None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, lora_scale: Optional[float] = None, clip_skip: Optional[int] = None, ): r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*): prompt to be encoded device: (`torch.device`): torch device num_images_per_prompt (`int`): number of images that should be generated per prompt do_classifier_free_guidance (`bool`): whether to use classifier free guidance or not negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts not to guide the image generation. If not defined, one has to pass `negative_prompt_embeds` instead. Ignored when not using guidance (i.e., ignored if `guidance_scale` is less than `1`). prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, text embeddings will be generated from `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs, *e.g.* prompt weighting. If not provided, negative_prompt_embeds will be generated from `negative_prompt` input argument. lora_scale (`float`, *optional*): A LoRA scale that will be applied to all LoRA layers of the text encoder if LoRA layers are loaded. clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. """ # set lora scale so that monkey patched LoRA # function of text encoder can correctly access it if lora_scale is not None and isinstance(self, LoraLoaderMixin): self._lora_scale = lora_scale # dynamically adjust the LoRA scale if not USE_PEFT_BACKEND: adjust_lora_scale_text_encoder(self.text_encoder, lora_scale) else: scale_lora_layers(self.text_encoder, lora_scale) if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] if prompt_embeds is None: # textual inversion: process multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): prompt = self.maybe_convert_prompt(prompt, self.tokenizer) text_inputs = self.tokenizer( prompt, padding="max_length", max_length=self.tokenizer.model_max_length, truncation=True, return_tensors="pt", ) text_input_ids = text_inputs.input_ids untruncated_ids = self.tokenizer(prompt, padding="longest", return_tensors="pt").input_ids if untruncated_ids.shape[-1] >= text_input_ids.shape[-1] and not torch.equal( text_input_ids, untruncated_ids ): removed_text = self.tokenizer.batch_decode( untruncated_ids[:, self.tokenizer.model_max_length - 1 : -1] ) logger.warning( "The following part of your input was truncated because CLIP can only handle sequences up to" f" {self.tokenizer.model_max_length} tokens: {removed_text}" ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = text_inputs.attention_mask.to(device) else: attention_mask = None if clip_skip is None: prompt_embeds = self.text_encoder(text_input_ids.to(device), attention_mask=attention_mask) prompt_embeds = prompt_embeds[0] else: prompt_embeds = self.text_encoder( text_input_ids.to(device), attention_mask=attention_mask, output_hidden_states=True ) # Access the `hidden_states` first, that contains a tuple of # all the hidden states from the encoder layers. Then index into # the tuple to access the hidden states from the desired layer. prompt_embeds = prompt_embeds[-1][-(clip_skip + 1)] # We also need to apply the final LayerNorm here to not mess with the # representations. The `last_hidden_states` that we typically use for # obtaining the final prompt representations passes through the LayerNorm # layer. prompt_embeds = self.text_encoder.text_model.final_layer_norm(prompt_embeds) if self.text_encoder is not None: prompt_embeds_dtype = self.text_encoder.dtype elif self.unet is not None: prompt_embeds_dtype = self.unet.dtype else: prompt_embeds_dtype = prompt_embeds.dtype prompt_embeds = prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) bs_embed, seq_len, _ = prompt_embeds.shape # duplicate text embeddings for each generation per prompt, using mps friendly method prompt_embeds = prompt_embeds.repeat(1, num_images_per_prompt, 1) prompt_embeds = prompt_embeds.view(bs_embed * num_images_per_prompt, seq_len, -1) # get unconditional embeddings for classifier free guidance if do_classifier_free_guidance and negative_prompt_embeds is None: uncond_tokens: List[str] if negative_prompt is None: uncond_tokens = [""] * batch_size elif prompt is not None and type(prompt) is not type(negative_prompt): raise TypeError( f"`negative_prompt` should be the same type to `prompt`, but got {type(negative_prompt)} !=" f" {type(prompt)}." ) elif isinstance(negative_prompt, str): uncond_tokens = [negative_prompt] elif batch_size != len(negative_prompt): raise ValueError( f"`negative_prompt`: {negative_prompt} has batch size {len(negative_prompt)}, but `prompt`:" f" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches" " the batch size of `prompt`." ) else: uncond_tokens = negative_prompt # textual inversion: process multi-vector tokens if necessary if isinstance(self, TextualInversionLoaderMixin): uncond_tokens = self.maybe_convert_prompt(uncond_tokens, self.tokenizer) max_length = prompt_embeds.shape[1] uncond_input = self.tokenizer( uncond_tokens, padding="max_length", max_length=max_length, truncation=True, return_tensors="pt", ) if hasattr(self.text_encoder.config, "use_attention_mask") and self.text_encoder.config.use_attention_mask: attention_mask = uncond_input.attention_mask.to(device) else: attention_mask = None negative_prompt_embeds = self.text_encoder( uncond_input.input_ids.to(device), attention_mask=attention_mask, ) negative_prompt_embeds = negative_prompt_embeds[0] if do_classifier_free_guidance: # duplicate unconditional embeddings for each generation per prompt, using mps friendly method seq_len = negative_prompt_embeds.shape[1] negative_prompt_embeds = negative_prompt_embeds.to(dtype=prompt_embeds_dtype, device=device) negative_prompt_embeds = negative_prompt_embeds.repeat(1, num_images_per_prompt, 1) negative_prompt_embeds = negative_prompt_embeds.view(batch_size * num_images_per_prompt, seq_len, -1) if isinstance(self, LoraLoaderMixin) and USE_PEFT_BACKEND: # Retrieve the original scale by scaling back the LoRA layers unscale_lora_layers(self.text_encoder, lora_scale) return prompt_embeds, negative_prompt_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.encode_image def encode_image(self, image, device, num_images_per_prompt, output_hidden_states=None): dtype = next(self.image_encoder.parameters()).dtype if not isinstance(image, torch.Tensor): image = self.feature_extractor(image, return_tensors="pt").pixel_values image = image.to(device=device, dtype=dtype) if output_hidden_states: image_enc_hidden_states = self.image_encoder(image, output_hidden_states=True).hidden_states[-2] image_enc_hidden_states = image_enc_hidden_states.repeat_interleave(num_images_per_prompt, dim=0) uncond_image_enc_hidden_states = self.image_encoder( torch.zeros_like(image), output_hidden_states=True ).hidden_states[-2] uncond_image_enc_hidden_states = uncond_image_enc_hidden_states.repeat_interleave( num_images_per_prompt, dim=0 ) return image_enc_hidden_states, uncond_image_enc_hidden_states else: image_embeds = self.image_encoder(image).image_embeds image_embeds = image_embeds.repeat_interleave(num_images_per_prompt, dim=0) uncond_image_embeds = torch.zeros_like(image_embeds) return image_embeds, uncond_image_embeds def decode_latents(self, latents, weight_dtype): latents = 1 / self.vae.config.scaling_factor * latents video = [] for t in range(latents.shape[0]): video.append(self.vae.decode(latents[t:t+1, ...].to(weight_dtype)).sample) video = torch.concat(video, dim=0) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 video = video.float() return video # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_ip_adapter_image_embeds def prepare_ip_adapter_image_embeds( self, ip_adapter_image, ip_adapter_image_embeds, device, num_images_per_prompt, do_classifier_free_guidance ): if ip_adapter_image_embeds is None: if not isinstance(ip_adapter_image, list): ip_adapter_image = [ip_adapter_image] if len(ip_adapter_image) != len(self.unet.encoder_hid_proj.image_projection_layers): raise ValueError( f"`ip_adapter_image` must have same length as the number of IP Adapters. Got {len(ip_adapter_image)} images and {len(self.unet.encoder_hid_proj.image_projection_layers)} IP Adapters." ) image_embeds = [] for single_ip_adapter_image, image_proj_layer in zip( ip_adapter_image, self.unet.encoder_hid_proj.image_projection_layers ): output_hidden_state = not isinstance(image_proj_layer, ImageProjection) single_image_embeds, single_negative_image_embeds = self.encode_image( single_ip_adapter_image, device, 1, output_hidden_state ) single_image_embeds = torch.stack([single_image_embeds] * num_images_per_prompt, dim=0) single_negative_image_embeds = torch.stack( [single_negative_image_embeds] * num_images_per_prompt, dim=0 ) if do_classifier_free_guidance: single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) single_image_embeds = single_image_embeds.to(device) image_embeds.append(single_image_embeds) else: repeat_dims = [1] image_embeds = [] for single_image_embeds in ip_adapter_image_embeds: if do_classifier_free_guidance: single_negative_image_embeds, single_image_embeds = single_image_embeds.chunk(2) single_image_embeds = single_image_embeds.repeat( num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) ) single_negative_image_embeds = single_negative_image_embeds.repeat( num_images_per_prompt, *(repeat_dims * len(single_negative_image_embeds.shape[1:])) ) single_image_embeds = torch.cat([single_negative_image_embeds, single_image_embeds]) else: single_image_embeds = single_image_embeds.repeat( num_images_per_prompt, *(repeat_dims * len(single_image_embeds.shape[1:])) ) image_embeds.append(single_image_embeds) return image_embeds # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.run_safety_checker def run_safety_checker(self, image, device, dtype): if self.safety_checker is None: has_nsfw_concept = None else: if torch.is_tensor(image): feature_extractor_input = self.image_processor.postprocess(image, output_type="pil") else: feature_extractor_input = self.image_processor.numpy_to_pil(image) safety_checker_input = self.feature_extractor(feature_extractor_input, return_tensors="pt").to(device) image, has_nsfw_concept = self.safety_checker( images=image, clip_input=safety_checker_input.pixel_values.to(dtype) ) return image, has_nsfw_concept # Copied from diffusers.pipelines.text_to_video_synthesis/pipeline_text_to_video_synth.TextToVideoSDPipeline.decode_latents def decode_latents(self, latents, weight_dtype): if self.vae.device!= latents.device: self.vae.to(latents.device) latents = 1 / self.vae.config.scaling_factor * latents video = [] for t in range(latents.shape[0]): video.append(self.vae.decode(latents[t:t+1, ...].to(weight_dtype)).sample) video = torch.concat(video, dim=0) # we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16 video = video.float() return video # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_extra_step_kwargs def prepare_extra_step_kwargs(self, generator, eta): # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers. # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502 # and should be between [0, 1] accepts_eta = "eta" in set(inspect.signature(self.scheduler.step).parameters.keys()) extra_step_kwargs = {} if accepts_eta: extra_step_kwargs["eta"] = eta # check if the scheduler accepts generator accepts_generator = "generator" in set(inspect.signature(self.scheduler.step).parameters.keys()) if accepts_generator: extra_step_kwargs["generator"] = generator return extra_step_kwargs def check_inputs( self, prompt, images, masks, callback_steps, negative_prompt=None, prompt_embeds=None, negative_prompt_embeds=None, ip_adapter_image=None, ip_adapter_image_embeds=None, brushnet_conditioning_scale=1.0, control_guidance_start=0.0, control_guidance_end=1.0, callback_on_step_end_tensor_inputs=None, ): if callback_steps is not None and (not isinstance(callback_steps, int) or callback_steps <= 0): raise ValueError( f"`callback_steps` has to be a positive integer but is {callback_steps} of type" f" {type(callback_steps)}." ) if callback_on_step_end_tensor_inputs is not None and not all( k in self._callback_tensor_inputs for k in callback_on_step_end_tensor_inputs ): raise ValueError( f"`callback_on_step_end_tensor_inputs` has to be in {self._callback_tensor_inputs}, but found {[k for k in callback_on_step_end_tensor_inputs if k not in self._callback_tensor_inputs]}" ) if prompt is not None and prompt_embeds is not None: raise ValueError( f"Cannot forward both `prompt`: {prompt} and `prompt_embeds`: {prompt_embeds}. Please make sure to" " only forward one of the two." ) elif prompt is None and prompt_embeds is None: raise ValueError( "Provide either `prompt` or `prompt_embeds`. Cannot leave both `prompt` and `prompt_embeds` undefined." ) elif prompt is not None and (not isinstance(prompt, str) and not isinstance(prompt, list)): raise ValueError(f"`prompt` has to be of type `str` or `list` but is {type(prompt)}") if negative_prompt is not None and negative_prompt_embeds is not None: raise ValueError( f"Cannot forward both `negative_prompt`: {negative_prompt} and `negative_prompt_embeds`:" f" {negative_prompt_embeds}. Please make sure to only forward one of the two." ) if prompt_embeds is not None and negative_prompt_embeds is not None: if prompt_embeds.shape != negative_prompt_embeds.shape: raise ValueError( "`prompt_embeds` and `negative_prompt_embeds` must have the same shape when passed directly, but" f" got: `prompt_embeds` {prompt_embeds.shape} != `negative_prompt_embeds`" f" {negative_prompt_embeds.shape}." ) # Check `image` is_compiled = hasattr(F, "scaled_dot_product_attention") and isinstance( self.brushnet, torch._dynamo.eval_frame.OptimizedModule ) if ( isinstance(self.brushnet, BrushNetModel) or is_compiled and isinstance(self.brushnet._orig_mod, BrushNetModel) ): self.check_image(images, masks, prompt, prompt_embeds) else: assert False # Check `brushnet_conditioning_scale` if ( isinstance(self.brushnet, BrushNetModel) or is_compiled and isinstance(self.brushnet._orig_mod, BrushNetModel) ): if not isinstance(brushnet_conditioning_scale, float): raise TypeError("For single brushnet: `brushnet_conditioning_scale` must be type `float`.") else: assert False if not isinstance(control_guidance_start, (tuple, list)): control_guidance_start = [control_guidance_start] if not isinstance(control_guidance_end, (tuple, list)): control_guidance_end = [control_guidance_end] if len(control_guidance_start) != len(control_guidance_end): raise ValueError( f"`control_guidance_start` has {len(control_guidance_start)} elements, but `control_guidance_end` has {len(control_guidance_end)} elements. Make sure to provide the same number of elements to each list." ) for start, end in zip(control_guidance_start, control_guidance_end): if start >= end: raise ValueError( f"control guidance start: {start} cannot be larger or equal to control guidance end: {end}." ) if start < 0.0: raise ValueError(f"control guidance start: {start} can't be smaller than 0.") if end > 1.0: raise ValueError(f"control guidance end: {end} can't be larger than 1.0.") if ip_adapter_image is not None and ip_adapter_image_embeds is not None: raise ValueError( "Provide either `ip_adapter_image` or `ip_adapter_image_embeds`. Cannot leave both `ip_adapter_image` and `ip_adapter_image_embeds` defined." ) if ip_adapter_image_embeds is not None: if not isinstance(ip_adapter_image_embeds, list): raise ValueError( f"`ip_adapter_image_embeds` has to be of type `list` but is {type(ip_adapter_image_embeds)}" ) elif ip_adapter_image_embeds[0].ndim not in [3, 4]: raise ValueError( f"`ip_adapter_image_embeds` has to be a list of 3D or 4D tensors but is {ip_adapter_image_embeds[0].ndim}D" ) def check_image(self, images, masks, prompt, prompt_embeds): for image in images: image_is_pil = isinstance(image, PIL.Image.Image) image_is_tensor = isinstance(image, torch.Tensor) image_is_np = isinstance(image, np.ndarray) image_is_pil_list = isinstance(image, list) and isinstance(image[0], PIL.Image.Image) image_is_tensor_list = isinstance(image, list) and isinstance(image[0], torch.Tensor) image_is_np_list = isinstance(image, list) and isinstance(image[0], np.ndarray) if ( not image_is_pil and not image_is_tensor and not image_is_np and not image_is_pil_list and not image_is_tensor_list and not image_is_np_list ): raise TypeError( f"image must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(image)}" ) for mask in masks: mask_is_pil = isinstance(mask, PIL.Image.Image) mask_is_tensor = isinstance(mask, torch.Tensor) mask_is_np = isinstance(mask, np.ndarray) mask_is_pil_list = isinstance(mask, list) and isinstance(mask[0], PIL.Image.Image) mask_is_tensor_list = isinstance(mask, list) and isinstance(mask[0], torch.Tensor) mask_is_np_list = isinstance(mask, list) and isinstance(mask[0], np.ndarray) if ( not mask_is_pil and not mask_is_tensor and not mask_is_np and not mask_is_pil_list and not mask_is_tensor_list and not mask_is_np_list ): raise TypeError( f"mask must be passed and be one of PIL image, numpy array, torch tensor, list of PIL images, list of numpy arrays or list of torch tensors, but is {type(mask)}" ) if image_is_pil: image_batch_size = 1 else: image_batch_size = len(image) if prompt is not None and isinstance(prompt, str): prompt_batch_size = 1 elif prompt is not None and isinstance(prompt, list): prompt_batch_size = len(prompt) elif prompt_embeds is not None: prompt_batch_size = prompt_embeds.shape[0] if image_batch_size != 1 and image_batch_size != prompt_batch_size: raise ValueError( f"If image batch size is not 1, image batch size must be same as prompt batch size. image batch size: {image_batch_size}, prompt batch size: {prompt_batch_size}" ) def prepare_image( self, images, width, height, batch_size, num_images_per_prompt, device, dtype, do_classifier_free_guidance=False, guess_mode=False, ): images_new = [] for image in images: image = self.image_processor.preprocess(image, height=height, width=width).to(dtype=torch.float32) image_batch_size = image.shape[0] if image_batch_size == 1: repeat_by = batch_size else: # image batch size is the same as prompt batch size repeat_by = num_images_per_prompt image = image.repeat_interleave(repeat_by, dim=0) image = image.to(device=device, dtype=dtype) # if do_classifier_free_guidance and not guess_mode: # image = torch.cat([image] * 2) images_new.append(image) return images_new # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline.prepare_latents def prepare_latents(self, batch_size, num_channels_latents, num_frames, height, width, dtype, device, generator, latents=None): # shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor) #b,c,n,h,w shape = ( batch_size, num_channels_latents, num_frames, height // self.vae_scale_factor, width // self.vae_scale_factor ) if isinstance(generator, list) and len(generator) != batch_size: raise ValueError( f"You have passed a list of generators of length {len(generator)}, but requested an effective batch" f" size of {batch_size}. Make sure the batch size matches the length of the generators." ) if latents is None: # noise = randn_tensor(shape, generator=generator, device=device, dtype=dtype) noise = rearrange(randn_tensor(shape, generator=generator, device=device, dtype=dtype), "b c t h w -> (b t) c h w") else: noise = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = noise * self.scheduler.init_noise_sigma return latents, noise @staticmethod def temp_blend(a, b, overlap): factor = torch.arange(overlap).to(b.device).view(overlap, 1, 1, 1) / (overlap - 1) a[:overlap, ...] = (1 - factor) * a[:overlap, ...] + factor * b[:overlap, ...] a[overlap:, ...] = b[overlap:, ...] return a # Copied from diffusers.pipelines.latent_consistency_models.pipeline_latent_consistency_text2img.LatentConsistencyModelPipeline.get_guidance_scale_embedding def get_guidance_scale_embedding(self, w, embedding_dim=512, dtype=torch.float32): """ See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 Args: timesteps (`torch.Tensor`): generate embedding vectors at these timesteps embedding_dim (`int`, *optional*, defaults to 512): dimension of the embeddings to generate dtype: data type of the generated embeddings Returns: `torch.FloatTensor`: Embedding vectors with shape `(len(timesteps), embedding_dim)` """ assert len(w.shape) == 1 w = w * 1000.0 half_dim = embedding_dim // 2 emb = torch.log(torch.tensor(10000.0)) / (half_dim - 1) emb = torch.exp(torch.arange(half_dim, dtype=dtype) * -emb) emb = w.to(dtype)[:, None] * emb[None, :] emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=1) if embedding_dim % 2 == 1: # zero pad emb = torch.nn.functional.pad(emb, (0, 1)) assert emb.shape == (w.shape[0], embedding_dim) return emb @property def guidance_scale(self): return self._guidance_scale @property def clip_skip(self): return self._clip_skip # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2) # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1` # corresponds to doing no classifier free guidance. @property def do_classifier_free_guidance(self): return self._guidance_scale > 1 and self.unet.config.time_cond_proj_dim is None @property def cross_attention_kwargs(self): return self._cross_attention_kwargs @property def num_timesteps(self): return self._num_timesteps # based on BrushNet: https://github.com/TencentARC/BrushNet/blob/main/src/diffusers/pipelines/brushnet/pipeline_brushnet.py @torch.no_grad() def __call__( self, num_frames: Optional[int] = 24, prompt: Union[str, List[str]] = None, images: PipelineImageInput = None, ##masked images masks: PipelineImageInput = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, timesteps: List[int] = None, guidance_scale: float = 7.5, negative_prompt: Optional[Union[str, List[str]]] = None, num_images_per_prompt: Optional[int] = 1, eta: float = 0.0, generator: Optional[Union[torch.Generator, List[torch.Generator]]] = None, latents: Optional[torch.FloatTensor] = None, prompt_embeds: Optional[torch.FloatTensor] = None, negative_prompt_embeds: Optional[torch.FloatTensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, ip_adapter_image_embeds: Optional[List[torch.FloatTensor]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, brushnet_conditioning_scale: Union[float, List[float]] = 1.0, guess_mode: bool = False, control_guidance_start: Union[float, List[float]] = 0.0, control_guidance_end: Union[float, List[float]] = 1.0, clip_skip: Optional[int] = None, callback_on_step_end: Optional[Callable[[int, int, Dict], None]] = None, callback_on_step_end_tensor_inputs: List[str] = ["latents"], **kwargs, ): r""" The call function to the pipeline for generation. Args: prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide image generation. If not defined, you need to pass `prompt_embeds`. image (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): The BrushNet branch input condition to provide guidance to the `unet` for generation. mask (`torch.FloatTensor`, `PIL.Image.Image`, `np.ndarray`, `List[torch.FloatTensor]`, `List[PIL.Image.Image]`, `List[np.ndarray]`,: `List[List[torch.FloatTensor]]`, `List[List[np.ndarray]]` or `List[List[PIL.Image.Image]]`): The BrushNet branch input condition to provide guidance to the `unet` for generation. height (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The height in pixels of the generated image. width (`int`, *optional*, defaults to `self.unet.config.sample_size * self.vae_scale_factor`): The width in pixels of the generated image. num_inference_steps (`int`, *optional*, defaults to 50): The number of denoising steps. More denoising steps usually lead to a higher quality image at the expense of slower inference. timesteps (`List[int]`, *optional*): Custom timesteps to use for the denoising process with schedulers which support a `timesteps` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. Must be in descending order. guidance_scale (`float`, *optional*, defaults to 7.5): A higher guidance scale value encourages the model to generate images closely linked to the text `prompt` at the expense of lower image quality. Guidance scale is enabled when `guidance_scale > 1`. negative_prompt (`str` or `List[str]`, *optional*): The prompt or prompts to guide what to not include in image generation. If not defined, you need to pass `negative_prompt_embeds` instead. Ignored when not using guidance (`guidance_scale < 1`). num_images_per_prompt (`int`, *optional*, defaults to 1): The number of images to generate per prompt. eta (`float`, *optional*, defaults to 0.0): Corresponds to parameter eta (η) from the [DDIM](https://arxiv.org/abs/2010.02502) paper. Only applies to the [`~schedulers.DDIMScheduler`], and is ignored in other schedulers. generator (`torch.Generator` or `List[torch.Generator]`, *optional*): A [`torch.Generator`](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation deterministic. latents (`torch.FloatTensor`, *optional*): Pre-generated noisy latents sampled from a Gaussian distribution, to be used as inputs for image generation. Can be used to tweak the same generation with different prompts. If not provided, a latents tensor is generated by sampling using the supplied random `generator`. prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, text embeddings are generated from the `prompt` input argument. negative_prompt_embeds (`torch.FloatTensor`, *optional*): Pre-generated negative text embeddings. Can be used to easily tweak text inputs (prompt weighting). If not provided, `negative_prompt_embeds` are generated from the `negative_prompt` input argument. ip_adapter_image: (`PipelineImageInput`, *optional*): Optional image input to work with IP Adapters. ip_adapter_image_embeds (`List[torch.FloatTensor]`, *optional*): Pre-generated image embeddings for IP-Adapter. It should be a list of length same as number of IP-adapters. Each element should be a tensor of shape `(batch_size, num_images, emb_dim)`. It should contain the negative image embedding if `do_classifier_free_guidance` is set to `True`. If not provided, embeddings are computed from the `ip_adapter_image` input argument. output_type (`str`, *optional*, defaults to `"pil"`): The output format of the generated image. Choose between `PIL.Image` or `np.array`. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a plain tuple. callback (`Callable`, *optional*): A function that calls every `callback_steps` steps during inference. The function is called with the following arguments: `callback(step: int, timestep: int, latents: torch.FloatTensor)`. callback_steps (`int`, *optional*, defaults to 1): The frequency at which the `callback` function is called. If not specified, the callback is called at every step. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the [`AttentionProcessor`] as defined in [`self.processor`](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). brushnet_conditioning_scale (`float` or `List[float]`, *optional*, defaults to 1.0): The outputs of the BrushNet are multiplied by `brushnet_conditioning_scale` before they are added to the residual in the original `unet`. If multiple BrushNets are specified in `init`, you can set the corresponding scale as a list. guess_mode (`bool`, *optional*, defaults to `False`): The BrushNet encoder tries to recognize the content of the input image even if you remove all prompts. A `guidance_scale` value between 3.0 and 5.0 is recommended. control_guidance_start (`float` or `List[float]`, *optional*, defaults to 0.0): The percentage of total steps at which the BrushNet starts applying. control_guidance_end (`float` or `List[float]`, *optional*, defaults to 1.0): The percentage of total steps at which the BrushNet stops applying. clip_skip (`int`, *optional*): Number of layers to be skipped from CLIP while computing the prompt embeddings. A value of 1 means that the output of the pre-final layer will be used for computing the prompt embeddings. callback_on_step_end (`Callable`, *optional*): A function that calls at the end of each denoising steps during the inference. The function is called with the following arguments: `callback_on_step_end(self: DiffusionPipeline, step: int, timestep: int, callback_kwargs: Dict)`. `callback_kwargs` will include a list of all tensors as specified by `callback_on_step_end_tensor_inputs`. callback_on_step_end_tensor_inputs (`List`, *optional*): The list of tensor inputs for the `callback_on_step_end` function. The tensors specified in the list will be passed as `callback_kwargs` argument. You will only be able to include variables listed in the `._callback_tensor_inputs` attribute of your pipeine class. Examples: Returns: [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] or `tuple`: If `return_dict` is `True`, [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] is returned, otherwise a `tuple` is returned where the first element is a list with the generated images and the second element is a list of `bool`s indicating whether the corresponding generated image contains "not-safe-for-work" (nsfw) content. """ callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) if callback is not None: deprecate( "callback", "1.0.0", "Passing `callback` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) if callback_steps is not None: deprecate( "callback_steps", "1.0.0", "Passing `callback_steps` as an input argument to `__call__` is deprecated, consider using `callback_on_step_end`", ) brushnet = self.brushnet._orig_mod if is_compiled_module(self.brushnet) else self.brushnet # align format for control guidance if not isinstance(control_guidance_start, list) and isinstance(control_guidance_end, list): control_guidance_start = len(control_guidance_end) * [control_guidance_start] elif not isinstance(control_guidance_end, list) and isinstance(control_guidance_start, list): control_guidance_end = len(control_guidance_start) * [control_guidance_end] elif not isinstance(control_guidance_start, list) and not isinstance(control_guidance_end, list): control_guidance_start, control_guidance_end = ( [control_guidance_start], [control_guidance_end], ) # 1. Check inputs. Raise error if not correct # self.check_inputs( # prompt, # images, # masks, # callback_steps, # negative_prompt, # prompt_embeds, # negative_prompt_embeds, # ip_adapter_image, # ip_adapter_image_embeds, # brushnet_conditioning_scale, # control_guidance_start, # control_guidance_end, # callback_on_step_end_tensor_inputs, # ) self._guidance_scale = guidance_scale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs # 2. Define call parameters if prompt is not None and isinstance(prompt, str): batch_size = 1 elif prompt is not None and isinstance(prompt, list): batch_size = len(prompt) else: batch_size = prompt_embeds.shape[0] device = self._execution_device global_pool_conditions = ( brushnet.config.global_pool_conditions if isinstance(brushnet, BrushNetModel) else brushnet.nets[0].config.global_pool_conditions ) guess_mode = guess_mode or global_pool_conditions video_length = len(images) if prompt is not None: # 3. Encode input prompt text_encoder_lora_scale = ( self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None ) if self.text_encoder.device != device: self.text_encoder.to(device) prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, device, num_images_per_prompt, self.do_classifier_free_guidance, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=text_encoder_lora_scale, clip_skip=self.clip_skip, ) self.text_encoder.to("cpu") else: prompt_embeds= prompt_embeds.to(self.unet.device,self.unet.dtype) print(prompt_embeds.shape) # For classifier free guidance, we need to do two forward passes. # Here we concatenate the unconditional and text embeddings into a single batch # to avoid doing two forward passes if self.do_classifier_free_guidance: prompt_embeds = torch.cat([negative_prompt_embeds, prompt_embeds]) if ip_adapter_image is not None or ip_adapter_image_embeds is not None: image_embeds = self.prepare_ip_adapter_image_embeds( ip_adapter_image, ip_adapter_image_embeds, device, batch_size * num_images_per_prompt, self.do_classifier_free_guidance, ) # 4. Prepare image if isinstance(brushnet, BrushNetModel): images = self.prepare_image( images=images, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=brushnet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, guess_mode=guess_mode, ) original_masks = self.prepare_image( images=masks, width=width, height=height, batch_size=batch_size * num_images_per_prompt, num_images_per_prompt=num_images_per_prompt, device=device, dtype=brushnet.dtype, do_classifier_free_guidance=self.do_classifier_free_guidance, guess_mode=guess_mode, ) original_masks_new = [] for original_mask in original_masks: original_mask=(original_mask.sum(1)[:,None,:,:] < 0).to(images[0].dtype) original_masks_new.append(original_mask) original_masks = original_masks_new height, width = images[0].shape[-2:] else: assert False # 5. Prepare timesteps timesteps, num_inference_steps = retrieve_timesteps(self.scheduler, num_inference_steps, device, timesteps) self._num_timesteps = len(timesteps) if self.vae.device != device: self.vae.to(device) # 6. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents, noise = self.prepare_latents( batch_size * num_images_per_prompt, num_channels_latents, num_frames, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6.1 prepare condition latents images = torch.cat(images) images = images.to(dtype=images[0].dtype) conditioning_latents = [] num=4 for i in range(0, images.shape[0], num): conditioning_latents.append(self.vae.encode(images[i : i + num]).latent_dist.sample()) conditioning_latents = torch.cat(conditioning_latents, dim=0) self.vae.to("cpu") conditioning_latents = conditioning_latents * self.vae.config.scaling_factor #[(f c h w],c2=4 original_masks = torch.cat(original_masks) masks = torch.nn.functional.interpolate( original_masks, size=( latents.shape[-2], latents.shape[-1] ) ) ##[ f c h w],c=1 conditioning_latents=torch.concat([conditioning_latents,masks],1) # 6.5 Optionally get Guidance Scale Embedding timestep_cond = None if self.unet.config.time_cond_proj_dim is not None: guidance_scale_tensor = torch.tensor(self.guidance_scale - 1).repeat(batch_size * num_images_per_prompt) timestep_cond = self.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.unet.config.time_cond_proj_dim ).to(device=device, dtype=latents.dtype) # 7. Prepare extra step kwargs. TODO: Logic should ideally just be moved out of the pipeline extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) # 7.1 Add image embeds for IP-Adapter added_cond_kwargs = ( {"image_embeds": image_embeds} if ip_adapter_image is not None or ip_adapter_image_embeds is not None else None ) # 7.2 Create tensor stating which brushnets to keep brushnet_keep = [] for i in range(len(timesteps)): keeps = [ 1.0 - float(i / len(timesteps) < s or (i + 1) / len(timesteps) > e) for s, e in zip(control_guidance_start, control_guidance_end) ] brushnet_keep.append(keeps[0] if isinstance(brushnet, BrushNetModel) else keeps) overlap = num_frames//4 context_list, context_list_swap = get_frames_context_swap(video_length, overlap=overlap, num_frames_per_clip=num_frames) scheduler_status = [copy.deepcopy(self.scheduler.__dict__)] * len(context_list) scheduler_status_swap = [copy.deepcopy(self.scheduler.__dict__)] * len(context_list_swap) count = torch.zeros_like(latents) value = torch.zeros_like(latents) # 8. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order is_unet_compiled = is_compiled_module(self.unet) is_brushnet_compiled = is_compiled_module(self.brushnet) is_torch_higher_equal_2_1 = is_torch_version(">=", "2.1") with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): count.zero_() value.zero_() ## swap if (i%2==1): context_list_choose = context_list_swap scheduler_status_choose = scheduler_status_swap else: context_list_choose = context_list scheduler_status_choose = scheduler_status for j, context in enumerate(context_list_choose): self.scheduler.__dict__.update(scheduler_status_choose[j]) latents_j = latents[context, :, :, :] # Relevant thread: # https://dev-discuss.pytorch.org/t/cudagraphs-in-pytorch-2-0/1428 if (is_unet_compiled and is_brushnet_compiled) and is_torch_higher_equal_2_1: torch._inductor.cudagraph_mark_step_begin() # expand the latents if we are doing classifier free guidance latent_model_input = torch.cat([latents_j] * 2) if self.do_classifier_free_guidance else latents_j latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # brushnet(s) inference if guess_mode and self.do_classifier_free_guidance: # Infer BrushNet only for the conditional batch. control_model_input = latents_j control_model_input = self.scheduler.scale_model_input(control_model_input, t) brushnet_prompt_embeds = prompt_embeds.chunk(2)[1] brushnet_prompt_embeds = rearrange(repeat(brushnet_prompt_embeds, "b c d -> b t c d", t=num_frames), 'b t c d -> (b t) c d') else: control_model_input = latent_model_input brushnet_prompt_embeds = prompt_embeds if self.do_classifier_free_guidance: neg_brushnet_prompt_embeds, brushnet_prompt_embeds = brushnet_prompt_embeds.chunk(2) brushnet_prompt_embeds = rearrange(repeat(brushnet_prompt_embeds, "b c d -> b t c d", t=num_frames), 'b t c d -> (b t) c d') neg_brushnet_prompt_embeds = rearrange(repeat(neg_brushnet_prompt_embeds, "b c d -> b t c d", t=num_frames), 'b t c d -> (b t) c d') brushnet_prompt_embeds = torch.cat([neg_brushnet_prompt_embeds, brushnet_prompt_embeds]) else: brushnet_prompt_embeds = rearrange(repeat(brushnet_prompt_embeds, "b c d -> b t c d", t=num_frames), 'b t c d -> (b t) c d') if isinstance(brushnet_keep[i], list): cond_scale = [c * s for c, s in zip(brushnet_conditioning_scale, brushnet_keep[i])] else: brushnet_cond_scale = brushnet_conditioning_scale if isinstance(brushnet_cond_scale, list): brushnet_cond_scale = brushnet_cond_scale[0] cond_scale = brushnet_cond_scale * brushnet_keep[i] down_block_res_samples, mid_block_res_sample, up_block_res_samples = self.brushnet( control_model_input, t, encoder_hidden_states=brushnet_prompt_embeds, brushnet_cond=torch.cat([conditioning_latents[context, :, :, :]]*2) if self.do_classifier_free_guidance else conditioning_latents[context, :, :, :], conditioning_scale=cond_scale, guess_mode=guess_mode, return_dict=False, ) if guess_mode and self.do_classifier_free_guidance: # Infered BrushNet only for the conditional batch. # To apply the output of BrushNet to both the unconditional and conditional batches, # add 0 to the unconditional batch to keep it unchanged. down_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_block_res_samples] mid_block_res_sample = torch.cat([torch.zeros_like(mid_block_res_sample), mid_block_res_sample]) up_block_res_samples = [torch.cat([torch.zeros_like(d), d]) for d in up_block_res_samples] # predict the noise residual noise_pred = self.unet( latent_model_input, t, encoder_hidden_states=prompt_embeds, timestep_cond=timestep_cond, cross_attention_kwargs=self.cross_attention_kwargs, down_block_add_samples=down_block_res_samples, mid_block_add_sample=mid_block_res_sample, up_block_add_samples=up_block_res_samples, added_cond_kwargs=added_cond_kwargs, return_dict=False, num_frames=num_frames, )[0] # perform guidance if self.do_classifier_free_guidance: noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 latents_j = self.scheduler.step(noise_pred, t, latents_j, **extra_step_kwargs, return_dict=False)[0] count[context, ...] += 1 if j==0: value[context, ...] += latents_j else: overlap_index_list = [index for index, value in enumerate(count[context, 0, 0, 0]) if value > 1] overlap_cur = len(overlap_index_list) ratio_next = torch.linspace(0, 1, overlap_cur+2)[1:-1] ratio_pre = 1-ratio_next for i_overlap in overlap_index_list: value[context[i_overlap], ...] = value[context[i_overlap], ...]*ratio_pre[i_overlap] + latents_j[i_overlap, ...]*ratio_next[i_overlap] value[context[i_overlap:num_frames], ...] = latents_j[i_overlap:num_frames, ...] latents = value.clone() if callback_on_step_end is not None: callback_kwargs = {} for k in callback_on_step_end_tensor_inputs: callback_kwargs[k] = locals()[k] callback_outputs = callback_on_step_end(self, i, t, callback_kwargs) latents = callback_outputs.pop("latents", latents) prompt_embeds = callback_outputs.pop("prompt_embeds", prompt_embeds) negative_prompt_embeds = callback_outputs.pop("negative_prompt_embeds", negative_prompt_embeds) # call the callback, if provided if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % self.scheduler.order == 0): progress_bar.update() if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, latents) # If we do sequential model offloading, let's offload unet and brushnet # manually for max memory savings if hasattr(self, "final_offload_hook") and self.final_offload_hook is not None: self.unet.to("cpu") self.brushnet.to("cpu") torch.cuda.empty_cache() if output_type == "latent": image = latents has_nsfw_concept = None return DiffuEraserPipelineOutput(frames=image, nsfw_content_detected=has_nsfw_concept) video_tensor = self.decode_latents(latents, weight_dtype=prompt_embeds.dtype) if output_type == "pt": video = video_tensor else: video = [] for i in range(video_tensor.shape[0]): video.append(self.image_processor.postprocess(video_tensor[i:i+1], output_type=output_type)[0]) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (video, has_nsfw_concept) return DiffuEraserPipelineOutput(frames=video, latents=latents) ================================================ FILE: libs/transformer_temporal.py ================================================ # Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass from typing import Any, Dict, Optional import torch from torch import nn from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.utils import BaseOutput from diffusers.models.attention import BasicTransformerBlock, TemporalBasicTransformerBlock from diffusers.models.embeddings import TimestepEmbedding, Timesteps from diffusers.models.modeling_utils import ModelMixin from diffusers.models.resnet import AlphaBlender @dataclass class TransformerTemporalModelOutput(BaseOutput): """ The output of [`TransformerTemporalModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size x num_frames, num_channels, height, width)`): The hidden states output conditioned on `encoder_hidden_states` input. """ sample: torch.FloatTensor class TransformerTemporalModel(ModelMixin, ConfigMixin): """ A Transformer model for video-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): The number of channels in the input and output (specify if the input is **continuous**). num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. attention_bias (`bool`, *optional*): Configure if the `TransformerBlock` attention should contain a bias parameter. sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**). This is fixed during training since it is used to learn a number of position embeddings. activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward. See `diffusers.models.activations.get_activation` for supported activation functions. norm_elementwise_affine (`bool`, *optional*): Configure if the `TransformerBlock` should use learnable elementwise affine parameters for normalization. double_self_attention (`bool`, *optional*): Configure if each `TransformerBlock` should contain two self-attention layers. positional_embeddings: (`str`, *optional*): The type of positional embeddings to apply to the sequence input before passing use. num_positional_embeddings: (`int`, *optional*): The maximum length of the sequence over which to apply positional embeddings. """ @register_to_config def __init__( self, num_attention_heads: int = 16, attention_head_dim: int = 88, in_channels: Optional[int] = None, out_channels: Optional[int] = None, num_layers: int = 1, dropout: float = 0.0, norm_num_groups: int = 32, cross_attention_dim: Optional[int] = None, attention_bias: bool = False, sample_size: Optional[int] = None, activation_fn: str = "geglu", norm_elementwise_affine: bool = True, double_self_attention: bool = True, positional_embeddings: Optional[str] = None, num_positional_embeddings: Optional[int] = None, ): super().__init__() self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim inner_dim = num_attention_heads * attention_head_dim self.in_channels = in_channels self.norm = torch.nn.GroupNorm(num_groups=norm_num_groups, num_channels=in_channels, eps=1e-6, affine=True) self.proj_in = nn.Linear(in_channels, inner_dim) # 3. Define transformers blocks self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, dropout=dropout, cross_attention_dim=cross_attention_dim, activation_fn=activation_fn, attention_bias=attention_bias, double_self_attention=double_self_attention, norm_elementwise_affine=norm_elementwise_affine, positional_embeddings=positional_embeddings, num_positional_embeddings=num_positional_embeddings, ) for d in range(num_layers) ] ) self.proj_out = nn.Linear(inner_dim, in_channels) def forward( self, hidden_states: torch.FloatTensor, timestep: Optional[torch.LongTensor] = None, num_frames: int = 1, encoder_hidden_states: Optional[torch.LongTensor] = None, class_labels: torch.LongTensor = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ) -> TransformerTemporalModelOutput: """ The [`TransformerTemporal`] forward method. Args: hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous): Input hidden_states. encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*): Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention. timestep ( `torch.LongTensor`, *optional*): Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`. class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*): Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in `AdaLayerZeroNorm`. num_frames (`int`, *optional*, defaults to 1): The number of frames to be processed per batch. This is used to reshape the hidden states. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. Returns: [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`: If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is returned, otherwise a `tuple` where the first element is the sample tensor. """ # 1. Input batch_frames, channel, height, width = hidden_states.shape batch_size = batch_frames // num_frames residual = hidden_states hidden_states = hidden_states[None, :].reshape(batch_size, num_frames, channel, height, width) hidden_states = hidden_states.permute(0, 2, 1, 3, 4) hidden_states = self.norm(hidden_states) hidden_states = hidden_states.permute(0, 3, 4, 2, 1).reshape(batch_size * height * width, num_frames, channel) hidden_states = self.proj_in(hidden_states) # 2. Blocks for block in self.transformer_blocks: hidden_states = block( hidden_states, encoder_hidden_states=encoder_hidden_states, timestep=timestep, cross_attention_kwargs=cross_attention_kwargs, class_labels=class_labels, ) # 3. Output hidden_states = self.proj_out(hidden_states) hidden_states = ( hidden_states[None, None, :] .reshape(batch_size, height, width, num_frames, channel) .permute(0, 3, 4, 1, 2) .contiguous() ) hidden_states = hidden_states.reshape(batch_frames, channel, height, width) output = hidden_states + residual return output class TransformerSpatioTemporalModel(nn.Module): """ A Transformer model for video-like data. Parameters: num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention. attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head. in_channels (`int`, *optional*): The number of channels in the input and output (specify if the input is **continuous**). out_channels (`int`, *optional*): The number of channels in the output (specify if the input is **continuous**). num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use. cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use. """ def __init__( self, num_attention_heads: int = 16, attention_head_dim: int = 88, in_channels: int = 320, out_channels: Optional[int] = None, num_layers: int = 1, cross_attention_dim: Optional[int] = None, ): super().__init__() self.num_attention_heads = num_attention_heads self.attention_head_dim = attention_head_dim inner_dim = num_attention_heads * attention_head_dim self.inner_dim = inner_dim # 2. Define input layers self.in_channels = in_channels self.norm = torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6) self.proj_in = nn.Linear(in_channels, inner_dim) # 3. Define transformers blocks self.transformer_blocks = nn.ModuleList( [ BasicTransformerBlock( inner_dim, num_attention_heads, attention_head_dim, cross_attention_dim=cross_attention_dim, ) for d in range(num_layers) ] ) time_mix_inner_dim = inner_dim self.temporal_transformer_blocks = nn.ModuleList( [ TemporalBasicTransformerBlock( inner_dim, time_mix_inner_dim, num_attention_heads, attention_head_dim, cross_attention_dim=cross_attention_dim, ) for _ in range(num_layers) ] ) time_embed_dim = in_channels * 4 self.time_pos_embed = TimestepEmbedding(in_channels, time_embed_dim, out_dim=in_channels) self.time_proj = Timesteps(in_channels, True, 0) self.time_mixer = AlphaBlender(alpha=0.5, merge_strategy="learned_with_images") # 4. Define output layers self.out_channels = in_channels if out_channels is None else out_channels # TODO: should use out_channels for continuous projections self.proj_out = nn.Linear(inner_dim, in_channels) self.gradient_checkpointing = False def forward( self, hidden_states: torch.Tensor, encoder_hidden_states: Optional[torch.Tensor] = None, image_only_indicator: Optional[torch.Tensor] = None, return_dict: bool = True, ): """ Args: hidden_states (`torch.FloatTensor` of shape `(batch size, channel, height, width)`): Input hidden_states. num_frames (`int`): The number of frames to be processed per batch. This is used to reshape the hidden states. encoder_hidden_states ( `torch.LongTensor` of shape `(batch size, encoder_hidden_states dim)`, *optional*): Conditional embeddings for cross attention layer. If not given, cross-attention defaults to self-attention. image_only_indicator (`torch.LongTensor` of shape `(batch size, num_frames)`, *optional*): A tensor indicating whether the input contains only images. 1 indicates that the input contains only images, 0 indicates that the input contains video frames. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.transformer_temporal.TransformerTemporalModelOutput`] instead of a plain tuple. Returns: [`~models.transformer_temporal.TransformerTemporalModelOutput`] or `tuple`: If `return_dict` is True, an [`~models.transformer_temporal.TransformerTemporalModelOutput`] is returned, otherwise a `tuple` where the first element is the sample tensor. """ # 1. Input batch_frames, _, height, width = hidden_states.shape num_frames = image_only_indicator.shape[-1] batch_size = batch_frames // num_frames time_context = encoder_hidden_states time_context_first_timestep = time_context[None, :].reshape( batch_size, num_frames, -1, time_context.shape[-1] )[:, 0] time_context = time_context_first_timestep[None, :].broadcast_to( height * width, batch_size, 1, time_context.shape[-1] ) time_context = time_context.reshape(height * width * batch_size, 1, time_context.shape[-1]) residual = hidden_states hidden_states = self.norm(hidden_states) inner_dim = hidden_states.shape[1] hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch_frames, height * width, inner_dim) hidden_states = self.proj_in(hidden_states) num_frames_emb = torch.arange(num_frames, device=hidden_states.device) num_frames_emb = num_frames_emb.repeat(batch_size, 1) num_frames_emb = num_frames_emb.reshape(-1) t_emb = self.time_proj(num_frames_emb) # `Timesteps` does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=hidden_states.dtype) emb = self.time_pos_embed(t_emb) emb = emb[:, None, :] # 2. Blocks for block, temporal_block in zip(self.transformer_blocks, self.temporal_transformer_blocks): if self.training and self.gradient_checkpointing: hidden_states = torch.utils.checkpoint.checkpoint( block, hidden_states, None, encoder_hidden_states, None, use_reentrant=False, ) else: hidden_states = block( hidden_states, encoder_hidden_states=encoder_hidden_states, ) hidden_states_mix = hidden_states hidden_states_mix = hidden_states_mix + emb hidden_states_mix = temporal_block( hidden_states_mix, num_frames=num_frames, encoder_hidden_states=time_context, ) hidden_states = self.time_mixer( x_spatial=hidden_states, x_temporal=hidden_states_mix, image_only_indicator=image_only_indicator, ) # 3. Output hidden_states = self.proj_out(hidden_states) hidden_states = hidden_states.reshape(batch_frames, height, width, inner_dim).permute(0, 3, 1, 2).contiguous() output = hidden_states + residual if not return_dict: return (output,) return TransformerTemporalModelOutput(sample=output) ================================================ FILE: libs/unet_2d_blocks.py ================================================ # Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Dict, Optional, Tuple, Union import numpy as np import torch import torch.nn.functional as F from torch import nn from diffusers.utils import is_torch_version, logging from diffusers.utils.torch_utils import apply_freeu from diffusers.models.activations import get_activation from diffusers.models.attention_processor import Attention, AttnAddedKVProcessor, AttnAddedKVProcessor2_0 from diffusers.models.normalization import AdaGroupNorm from diffusers.models.resnet import ( Downsample2D, FirDownsample2D, FirUpsample2D, KDownsample2D, KUpsample2D, ResnetBlock2D, ResnetBlockCondNorm2D, Upsample2D, ) from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel from diffusers.models.transformers.transformer_2d import Transformer2DModel logger = logging.get_logger(__name__) # pylint: disable=invalid-name def get_down_block( down_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channels: int, add_downsample: bool, resnet_eps: float, resnet_act_fn: str, transformer_layers_per_block: int = 1, num_attention_heads: Optional[int] = None, resnet_groups: Optional[int] = None, cross_attention_dim: Optional[int] = None, downsample_padding: Optional[int] = None, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", attention_type: str = "default", resnet_skip_time_act: bool = False, resnet_out_scale_factor: float = 1.0, cross_attention_norm: Optional[str] = None, attention_head_dim: Optional[int] = None, downsample_type: Optional[str] = None, dropout: float = 0.0, ): # If attn head dim is not defined, we default it to the number of heads if attention_head_dim is None: logger.warn( f"It is recommended to provide `attention_head_dim` when calling `get_down_block`. Defaulting `attention_head_dim` to {num_attention_heads}." ) attention_head_dim = num_attention_heads down_block_type = down_block_type[7:] if down_block_type.startswith("UNetRes") else down_block_type if down_block_type == "DownBlock2D": return DownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "ResnetDownsampleBlock2D": return ResnetDownsampleBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, output_scale_factor=resnet_out_scale_factor, ) elif down_block_type == "AttnDownBlock2D": if add_downsample is False: downsample_type = None else: downsample_type = downsample_type or "conv" # default to 'conv' return AttnDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, attention_head_dim=attention_head_dim, resnet_time_scale_shift=resnet_time_scale_shift, downsample_type=downsample_type, ) elif down_block_type == "CrossAttnDownBlock2D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock2D") return CrossAttnDownBlock2D( num_layers=num_layers, transformer_layers_per_block=transformer_layers_per_block, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, attention_type=attention_type, ) elif down_block_type == "SimpleCrossAttnDownBlock2D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnDownBlock2D") return SimpleCrossAttnDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, attention_head_dim=attention_head_dim, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, output_scale_factor=resnet_out_scale_factor, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, ) elif down_block_type == "SkipDownBlock2D": return SkipDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "AttnSkipDownBlock2D": return AttnSkipDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, attention_head_dim=attention_head_dim, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "DownEncoderBlock2D": return DownEncoderBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, dropout=dropout, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "AttnDownEncoderBlock2D": return AttnDownEncoderBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, dropout=dropout, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, attention_head_dim=attention_head_dim, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "KDownBlock2D": return KDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, ) elif down_block_type == "KCrossAttnDownBlock2D": return KCrossAttnDownBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, dropout=dropout, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, cross_attention_dim=cross_attention_dim, attention_head_dim=attention_head_dim, add_self_attention=True if not add_downsample else False, ) raise ValueError(f"{down_block_type} does not exist.") def get_mid_block( mid_block_type: str, temb_channels: int, in_channels: int, resnet_eps: float, resnet_act_fn: str, resnet_groups: int, output_scale_factor: float = 1.0, transformer_layers_per_block: int = 1, num_attention_heads: Optional[int] = None, cross_attention_dim: Optional[int] = None, dual_cross_attention: bool = False, use_linear_projection: bool = False, mid_block_only_cross_attention: bool = False, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", attention_type: str = "default", resnet_skip_time_act: bool = False, cross_attention_norm: Optional[str] = None, attention_head_dim: Optional[int] = 1, dropout: float = 0.0, ): if mid_block_type == "UNetMidBlock2DCrossAttn": return UNetMidBlock2DCrossAttn( transformer_layers_per_block=transformer_layers_per_block, in_channels=in_channels, temb_channels=temb_channels, dropout=dropout, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, output_scale_factor=output_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, resnet_groups=resnet_groups, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, attention_type=attention_type, ) elif mid_block_type == "UNetMidBlock2DSimpleCrossAttn": return UNetMidBlock2DSimpleCrossAttn( in_channels=in_channels, temb_channels=temb_channels, dropout=dropout, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, output_scale_factor=output_scale_factor, cross_attention_dim=cross_attention_dim, attention_head_dim=attention_head_dim, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, only_cross_attention=mid_block_only_cross_attention, cross_attention_norm=cross_attention_norm, ) elif mid_block_type == "UNetMidBlock2D": return UNetMidBlock2D( in_channels=in_channels, temb_channels=temb_channels, dropout=dropout, num_layers=0, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, output_scale_factor=output_scale_factor, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, add_attention=False, ) elif mid_block_type == "MidBlock2D": return MidBlock2D( in_channels=in_channels, temb_channels=temb_channels, dropout=dropout, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, output_scale_factor=output_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, resnet_groups=resnet_groups, use_linear_projection=use_linear_projection, ) elif mid_block_type == "MidBlock2D": return MidBlock2D( in_channels=in_channels, temb_channels=temb_channels, dropout=dropout, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, output_scale_factor=output_scale_factor, resnet_time_scale_shift=resnet_time_scale_shift, resnet_groups=resnet_groups, use_linear_projection=use_linear_projection, ) elif mid_block_type is None: return None else: raise ValueError(f"unknown mid_block_type : {mid_block_type}") def get_up_block( up_block_type: str, num_layers: int, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, add_upsample: bool, resnet_eps: float, resnet_act_fn: str, resolution_idx: Optional[int] = None, transformer_layers_per_block: int = 1, num_attention_heads: Optional[int] = None, resnet_groups: Optional[int] = None, cross_attention_dim: Optional[int] = None, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", attention_type: str = "default", resnet_skip_time_act: bool = False, resnet_out_scale_factor: float = 1.0, cross_attention_norm: Optional[str] = None, attention_head_dim: Optional[int] = None, upsample_type: Optional[str] = None, dropout: float = 0.0, ) -> nn.Module: # If attn head dim is not defined, we default it to the number of heads if attention_head_dim is None: logger.warn( f"It is recommended to provide `attention_head_dim` when calling `get_up_block`. Defaulting `attention_head_dim` to {num_attention_heads}." ) attention_head_dim = num_attention_heads up_block_type = up_block_type[7:] if up_block_type.startswith("UNetRes") else up_block_type if up_block_type == "UpBlock2D": return UpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "ResnetUpsampleBlock2D": return ResnetUpsampleBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, output_scale_factor=resnet_out_scale_factor, ) elif up_block_type == "CrossAttnUpBlock2D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock2D") return CrossAttnUpBlock2D( num_layers=num_layers, transformer_layers_per_block=transformer_layers_per_block, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, attention_type=attention_type, ) elif up_block_type == "SimpleCrossAttnUpBlock2D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for SimpleCrossAttnUpBlock2D") return SimpleCrossAttnUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, attention_head_dim=attention_head_dim, resnet_time_scale_shift=resnet_time_scale_shift, skip_time_act=resnet_skip_time_act, output_scale_factor=resnet_out_scale_factor, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, ) elif up_block_type == "AttnUpBlock2D": if add_upsample is False: upsample_type = None else: upsample_type = upsample_type or "conv" # default to 'conv' return AttnUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, attention_head_dim=attention_head_dim, resnet_time_scale_shift=resnet_time_scale_shift, upsample_type=upsample_type, ) elif up_block_type == "SkipUpBlock2D": return SkipUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "AttnSkipUpBlock2D": return AttnSkipUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, attention_head_dim=attention_head_dim, resnet_time_scale_shift=resnet_time_scale_shift, ) elif up_block_type == "UpDecoderBlock2D": return UpDecoderBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, temb_channels=temb_channels, ) elif up_block_type == "AttnUpDecoderBlock2D": return AttnUpDecoderBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, attention_head_dim=attention_head_dim, resnet_time_scale_shift=resnet_time_scale_shift, temb_channels=temb_channels, ) elif up_block_type == "KUpBlock2D": return KUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, ) elif up_block_type == "KCrossAttnUpBlock2D": return KCrossAttnUpBlock2D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, resolution_idx=resolution_idx, dropout=dropout, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, cross_attention_dim=cross_attention_dim, attention_head_dim=attention_head_dim, ) raise ValueError(f"{up_block_type} does not exist.") class AutoencoderTinyBlock(nn.Module): """ Tiny Autoencoder block used in [`AutoencoderTiny`]. It is a mini residual module consisting of plain conv + ReLU blocks. Args: in_channels (`int`): The number of input channels. out_channels (`int`): The number of output channels. act_fn (`str`): ` The activation function to use. Supported values are `"swish"`, `"mish"`, `"gelu"`, and `"relu"`. Returns: `torch.FloatTensor`: A tensor with the same shape as the input tensor, but with the number of channels equal to `out_channels`. """ def __init__(self, in_channels: int, out_channels: int, act_fn: str): super().__init__() act_fn = get_activation(act_fn) self.conv = nn.Sequential( nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), act_fn, nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), act_fn, nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), ) self.skip = ( nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False) if in_channels != out_channels else nn.Identity() ) self.fuse = nn.ReLU() def forward(self, x: torch.FloatTensor) -> torch.FloatTensor: return self.fuse(self.conv(x) + self.skip(x)) class UNetMidBlock2D(nn.Module): """ A 2D UNet mid-block [`UNetMidBlock2D`] with multiple residual blocks and optional attention blocks. Args: in_channels (`int`): The number of input channels. temb_channels (`int`): The number of temporal embedding channels. dropout (`float`, *optional*, defaults to 0.0): The dropout rate. num_layers (`int`, *optional*, defaults to 1): The number of residual blocks. resnet_eps (`float`, *optional*, 1e-6 ): The epsilon value for the resnet blocks. resnet_time_scale_shift (`str`, *optional*, defaults to `default`): The type of normalization to apply to the time embeddings. This can help to improve the performance of the model on tasks with long-range temporal dependencies. resnet_act_fn (`str`, *optional*, defaults to `swish`): The activation function for the resnet blocks. resnet_groups (`int`, *optional*, defaults to 32): The number of groups to use in the group normalization layers of the resnet blocks. attn_groups (`Optional[int]`, *optional*, defaults to None): The number of groups for the attention blocks. resnet_pre_norm (`bool`, *optional*, defaults to `True`): Whether to use pre-normalization for the resnet blocks. add_attention (`bool`, *optional*, defaults to `True`): Whether to add attention blocks. attention_head_dim (`int`, *optional*, defaults to 1): Dimension of a single attention head. The number of attention heads is determined based on this value and the number of input channels. output_scale_factor (`float`, *optional*, defaults to 1.0): The output scale factor. Returns: `torch.FloatTensor`: The output of the last residual block, which is a tensor of shape `(batch_size, in_channels, height, width)`. """ def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", # default, spatial resnet_act_fn: str = "swish", resnet_groups: int = 32, attn_groups: Optional[int] = None, resnet_pre_norm: bool = True, add_attention: bool = True, attention_head_dim: int = 1, output_scale_factor: float = 1.0, ): super().__init__() resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) self.add_attention = add_attention if attn_groups is None: attn_groups = resnet_groups if resnet_time_scale_shift == "default" else None # there is always at least one resnet if resnet_time_scale_shift == "spatial": resnets = [ ResnetBlockCondNorm2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm="spatial", non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, ) ] else: resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ] attentions = [] if attention_head_dim is None: logger.warn( f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {in_channels}." ) attention_head_dim = in_channels for _ in range(num_layers): if self.add_attention: attentions.append( Attention( in_channels, heads=in_channels // attention_head_dim, dim_head=attention_head_dim, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=attn_groups, spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) else: attentions.append(None) if resnet_time_scale_shift == "spatial": resnets.append( ResnetBlockCondNorm2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm="spatial", non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, ) ) else: resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward(self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None) -> torch.FloatTensor: hidden_states = self.resnets[0](hidden_states, temb) for attn, resnet in zip(self.attentions, self.resnets[1:]): if attn is not None: hidden_states = attn(hidden_states, temb=temb) hidden_states = resnet(hidden_states, temb) return hidden_states class UNetMidBlock2DCrossAttn(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, transformer_layers_per_block: Union[int, Tuple[int]] = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, num_attention_heads: int = 1, output_scale_factor: float = 1.0, cross_attention_dim: int = 1280, dual_cross_attention: bool = False, use_linear_projection: bool = False, upcast_attention: bool = False, attention_type: str = "default", ): super().__init__() self.has_cross_attention = True self.num_attention_heads = num_attention_heads resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) # support for variable transformer layers per block if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * num_layers # there is always at least one resnet resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ] attentions = [] for i in range(num_layers): if not dual_cross_attention: attentions.append( Transformer2DModel( num_attention_heads, in_channels // num_attention_heads, in_channels=in_channels, num_layers=transformer_layers_per_block[i], cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, attention_type=attention_type, ) ) else: attentions.append( DualTransformer2DModel( num_attention_heads, in_channels // num_attention_heads, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale) for attn, resnet in zip(self.attentions, self.resnets[1:]): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) else: hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] hidden_states = resnet(hidden_states, temb, scale=lora_scale) return hidden_states class UNetMidBlock2DSimpleCrossAttn(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attention_head_dim: int = 1, output_scale_factor: float = 1.0, cross_attention_dim: int = 1280, skip_time_act: bool = False, only_cross_attention: bool = False, cross_attention_norm: Optional[str] = None, ): super().__init__() self.has_cross_attention = True self.attention_head_dim = attention_head_dim resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) self.num_heads = in_channels // self.attention_head_dim # there is always at least one resnet resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ] attentions = [] for _ in range(num_layers): processor = ( AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() ) attentions.append( Attention( query_dim=in_channels, cross_attention_dim=in_channels, heads=self.num_heads, dim_head=self.attention_head_dim, added_kv_proj_dim=cross_attention_dim, norm_num_groups=resnet_groups, bias=True, upcast_softmax=True, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, processor=processor, ) ) resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} lora_scale = cross_attention_kwargs.get("scale", 1.0) if attention_mask is None: # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask. mask = None if encoder_hidden_states is None else encoder_attention_mask else: # when attention_mask is defined: we don't even check for encoder_attention_mask. # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks. # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask. # then we can simplify this whole if/else block to: # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask mask = attention_mask hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale) for attn, resnet in zip(self.attentions, self.resnets[1:]): # attn hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=mask, **cross_attention_kwargs, ) # resnet hidden_states = resnet(hidden_states, temb, scale=lora_scale) return hidden_states class MidBlock2D(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, use_linear_projection: bool = False, ): super().__init__() self.has_cross_attention = False resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) # there is always at least one resnet resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ] for i in range(num_layers): resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: lora_scale = 1.0 hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale) for resnet in self.resnets[1:]: if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) else: hidden_states = resnet(hidden_states, temb, scale=lora_scale) return hidden_states class AttnDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attention_head_dim: int = 1, output_scale_factor: float = 1.0, downsample_padding: int = 1, downsample_type: str = "conv", ): super().__init__() resnets = [] attentions = [] self.downsample_type = downsample_type if attention_head_dim is None: logger.warn( f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." ) attention_head_dim = out_channels for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) attentions.append( Attention( out_channels, heads=out_channels // attention_head_dim, dim_head=attention_head_dim, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=resnet_groups, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if downsample_type == "conv": self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) elif downsample_type == "resnet": self.downsamplers = nn.ModuleList( [ ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, down=True, ) ] ) else: self.downsamplers = None def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, upsample_size: Optional[int] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} lora_scale = cross_attention_kwargs.get("scale", 1.0) output_states = () for resnet, attn in zip(self.resnets, self.attentions): cross_attention_kwargs.update({"scale": lora_scale}) hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = attn(hidden_states, **cross_attention_kwargs) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: if self.downsample_type == "resnet": hidden_states = downsampler(hidden_states, temb=temb, scale=lora_scale) else: hidden_states = downsampler(hidden_states, scale=lora_scale) output_states += (hidden_states,) return hidden_states, output_states class CrossAttnDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, transformer_layers_per_block: Union[int, Tuple[int]] = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, num_attention_heads: int = 1, cross_attention_dim: int = 1280, output_scale_factor: float = 1.0, downsample_padding: int = 1, add_downsample: bool = True, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, attention_type: str = "default", ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.num_attention_heads = num_attention_heads if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * num_layers for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if not dual_cross_attention: attentions.append( Transformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=transformer_layers_per_block[i], cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, attention_type=attention_type, ) ) else: attentions.append( DualTransformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, additional_residuals: Optional[torch.FloatTensor] = None, down_block_add_samples: Optional[torch.FloatTensor] = None, ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: output_states = () lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 blocks = list(zip(self.resnets, self.attentions)) for i, (resnet, attn) in enumerate(blocks): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] else: hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] # apply additional residuals to the output of the last pair of resnet and attention blocks if i == len(blocks) - 1 and additional_residuals is not None: hidden_states = hidden_states + additional_residuals if down_block_add_samples is not None: hidden_states = hidden_states + down_block_add_samples.pop(0) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states, scale=lora_scale) if down_block_add_samples is not None: hidden_states = hidden_states + down_block_add_samples.pop(0) # todo: add before or after output_states = output_states + (hidden_states,) return hidden_states, output_states class DownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, add_downsample: bool = True, downsample_padding: int = 1, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0, down_block_add_samples: Optional[torch.FloatTensor] = None, ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: output_states = () for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb, scale=scale) if down_block_add_samples is not None: hidden_states = hidden_states + down_block_add_samples.pop(0) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states, scale=scale) if down_block_add_samples is not None: hidden_states = hidden_states + down_block_add_samples.pop(0) # todo: add before or after output_states = output_states + (hidden_states,) return hidden_states, output_states class DownEncoderBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, add_downsample: bool = True, downsample_padding: int = 1, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels if resnet_time_scale_shift == "spatial": resnets.append( ResnetBlockCondNorm2D( in_channels=in_channels, out_channels=out_channels, temb_channels=None, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm="spatial", non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, ) ) else: resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=None, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None def forward(self, hidden_states: torch.FloatTensor, scale: float = 1.0) -> torch.FloatTensor: for resnet in self.resnets: hidden_states = resnet(hidden_states, temb=None, scale=scale) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states, scale) return hidden_states class AttnDownEncoderBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attention_head_dim: int = 1, output_scale_factor: float = 1.0, add_downsample: bool = True, downsample_padding: int = 1, ): super().__init__() resnets = [] attentions = [] if attention_head_dim is None: logger.warn( f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." ) attention_head_dim = out_channels for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels if resnet_time_scale_shift == "spatial": resnets.append( ResnetBlockCondNorm2D( in_channels=in_channels, out_channels=out_channels, temb_channels=None, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm="spatial", non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, ) ) else: resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=None, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) attentions.append( Attention( out_channels, heads=out_channels // attention_head_dim, dim_head=attention_head_dim, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=resnet_groups, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op" ) ] ) else: self.downsamplers = None def forward(self, hidden_states: torch.FloatTensor, scale: float = 1.0) -> torch.FloatTensor: for resnet, attn in zip(self.resnets, self.attentions): hidden_states = resnet(hidden_states, temb=None, scale=scale) cross_attention_kwargs = {"scale": scale} hidden_states = attn(hidden_states, **cross_attention_kwargs) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states, scale) return hidden_states class AttnSkipDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_pre_norm: bool = True, attention_head_dim: int = 1, output_scale_factor: float = np.sqrt(2.0), add_downsample: bool = True, ): super().__init__() self.attentions = nn.ModuleList([]) self.resnets = nn.ModuleList([]) if attention_head_dim is None: logger.warn( f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." ) attention_head_dim = out_channels for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels self.resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(in_channels // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.attentions.append( Attention( out_channels, heads=out_channels // attention_head_dim, dim_head=attention_head_dim, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=32, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) if add_downsample: self.resnet_down = ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, use_in_shortcut=True, down=True, kernel="fir", ) self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)]) self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1)) else: self.resnet_down = None self.downsamplers = None self.skip_conv = None def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, skip_sample: Optional[torch.FloatTensor] = None, scale: float = 1.0, ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...], torch.FloatTensor]: output_states = () for resnet, attn in zip(self.resnets, self.attentions): hidden_states = resnet(hidden_states, temb, scale=scale) cross_attention_kwargs = {"scale": scale} hidden_states = attn(hidden_states, **cross_attention_kwargs) output_states += (hidden_states,) if self.downsamplers is not None: hidden_states = self.resnet_down(hidden_states, temb, scale=scale) for downsampler in self.downsamplers: skip_sample = downsampler(skip_sample) hidden_states = self.skip_conv(skip_sample) + hidden_states output_states += (hidden_states,) return hidden_states, output_states, skip_sample class SkipDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_pre_norm: bool = True, output_scale_factor: float = np.sqrt(2.0), add_downsample: bool = True, downsample_padding: int = 1, ): super().__init__() self.resnets = nn.ModuleList([]) for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels self.resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(in_channels // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if add_downsample: self.resnet_down = ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, use_in_shortcut=True, down=True, kernel="fir", ) self.downsamplers = nn.ModuleList([FirDownsample2D(out_channels, out_channels=out_channels)]) self.skip_conv = nn.Conv2d(3, out_channels, kernel_size=(1, 1), stride=(1, 1)) else: self.resnet_down = None self.downsamplers = None self.skip_conv = None def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, skip_sample: Optional[torch.FloatTensor] = None, scale: float = 1.0, ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...], torch.FloatTensor]: output_states = () for resnet in self.resnets: hidden_states = resnet(hidden_states, temb, scale) output_states += (hidden_states,) if self.downsamplers is not None: hidden_states = self.resnet_down(hidden_states, temb, scale) for downsampler in self.downsamplers: skip_sample = downsampler(skip_sample) hidden_states = self.skip_conv(skip_sample) + hidden_states output_states += (hidden_states,) return hidden_states, output_states, skip_sample class ResnetDownsampleBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, add_downsample: bool = True, skip_time_act: bool = False, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, down=True, ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0 ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: output_states = () for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb, scale) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states, temb, scale) output_states = output_states + (hidden_states,) return hidden_states, output_states class SimpleCrossAttnDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attention_head_dim: int = 1, cross_attention_dim: int = 1280, output_scale_factor: float = 1.0, add_downsample: bool = True, skip_time_act: bool = False, only_cross_attention: bool = False, cross_attention_norm: Optional[str] = None, ): super().__init__() self.has_cross_attention = True resnets = [] attentions = [] self.attention_head_dim = attention_head_dim self.num_heads = out_channels // self.attention_head_dim for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) processor = ( AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() ) attentions.append( Attention( query_dim=out_channels, cross_attention_dim=out_channels, heads=self.num_heads, dim_head=attention_head_dim, added_kv_proj_dim=cross_attention_dim, norm_num_groups=resnet_groups, bias=True, upcast_softmax=True, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, processor=processor, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, down=True, ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: output_states = () cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} lora_scale = cross_attention_kwargs.get("scale", 1.0) if attention_mask is None: # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask. mask = None if encoder_hidden_states is None else encoder_attention_mask else: # when attention_mask is defined: we don't even check for encoder_attention_mask. # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks. # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask. # then we can simplify this whole if/else block to: # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask mask = attention_mask for resnet, attn in zip(self.resnets, self.attentions): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=mask, **cross_attention_kwargs, ) else: hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=mask, **cross_attention_kwargs, ) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states, temb, scale=lora_scale) output_states = output_states + (hidden_states,) return hidden_states, output_states class KDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 4, resnet_eps: float = 1e-5, resnet_act_fn: str = "gelu", resnet_group_size: int = 32, add_downsample: bool = False, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels groups = in_channels // resnet_group_size groups_out = out_channels // resnet_group_size resnets.append( ResnetBlockCondNorm2D( in_channels=in_channels, out_channels=out_channels, dropout=dropout, temb_channels=temb_channels, groups=groups, groups_out=groups_out, eps=resnet_eps, non_linearity=resnet_act_fn, time_embedding_norm="ada_group", conv_shortcut_bias=False, ) ) self.resnets = nn.ModuleList(resnets) if add_downsample: # YiYi's comments- might be able to use FirDownsample2D, look into details later self.downsamplers = nn.ModuleList([KDownsample2D()]) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0 ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: output_states = () for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb, scale) output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) return hidden_states, output_states class KCrossAttnDownBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, cross_attention_dim: int, dropout: float = 0.0, num_layers: int = 4, resnet_group_size: int = 32, add_downsample: bool = True, attention_head_dim: int = 64, add_self_attention: bool = False, resnet_eps: float = 1e-5, resnet_act_fn: str = "gelu", ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels groups = in_channels // resnet_group_size groups_out = out_channels // resnet_group_size resnets.append( ResnetBlockCondNorm2D( in_channels=in_channels, out_channels=out_channels, dropout=dropout, temb_channels=temb_channels, groups=groups, groups_out=groups_out, eps=resnet_eps, non_linearity=resnet_act_fn, time_embedding_norm="ada_group", conv_shortcut_bias=False, ) ) attentions.append( KAttentionBlock( out_channels, out_channels // attention_head_dim, attention_head_dim, cross_attention_dim=cross_attention_dim, temb_channels=temb_channels, attention_bias=True, add_self_attention=add_self_attention, cross_attention_norm="layer_norm", group_size=resnet_group_size, ) ) self.resnets = nn.ModuleList(resnets) self.attentions = nn.ModuleList(attentions) if add_downsample: self.downsamplers = nn.ModuleList([KDownsample2D()]) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: output_states = () lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 for resnet, attn in zip(self.resnets, self.attentions): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, emb=temb, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, encoder_attention_mask=encoder_attention_mask, ) else: hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, emb=temb, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, encoder_attention_mask=encoder_attention_mask, ) if self.downsamplers is None: output_states += (None,) else: output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) return hidden_states, output_states class AttnUpBlock2D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, resolution_idx: int = None, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attention_head_dim: int = 1, output_scale_factor: float = 1.0, upsample_type: str = "conv", ): super().__init__() resnets = [] attentions = [] self.upsample_type = upsample_type if attention_head_dim is None: logger.warn( f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `in_channels`: {out_channels}." ) attention_head_dim = out_channels for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) attentions.append( Attention( out_channels, heads=out_channels // attention_head_dim, dim_head=attention_head_dim, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=resnet_groups, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if upsample_type == "conv": self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) elif upsample_type == "resnet": self.upsamplers = nn.ModuleList( [ ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, up=True, ) ] ) else: self.upsamplers = None self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, upsample_size: Optional[int] = None, scale: float = 1.0, ) -> torch.FloatTensor: for resnet, attn in zip(self.resnets, self.attentions): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) hidden_states = resnet(hidden_states, temb, scale=scale) cross_attention_kwargs = {"scale": scale} hidden_states = attn(hidden_states, **cross_attention_kwargs) if self.upsamplers is not None: for upsampler in self.upsamplers: if self.upsample_type == "resnet": hidden_states = upsampler(hidden_states, temb=temb, scale=scale) else: hidden_states = upsampler(hidden_states, scale=scale) return hidden_states class CrossAttnUpBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, transformer_layers_per_block: Union[int, Tuple[int]] = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, num_attention_heads: int = 1, cross_attention_dim: int = 1280, output_scale_factor: float = 1.0, add_upsample: bool = True, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, attention_type: str = "default", ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.num_attention_heads = num_attention_heads if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * num_layers for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if not dual_cross_attention: attentions.append( Transformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=transformer_layers_per_block[i], cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, attention_type=attention_type, ) ) else: attentions.append( DualTransformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, upsample_size: Optional[int] = None, attention_mask: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, return_res_samples: Optional[bool]=False, up_block_add_samples: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 is_freeu_enabled = ( getattr(self, "s1", None) and getattr(self, "s2", None) and getattr(self, "b1", None) and getattr(self, "b2", None) ) if return_res_samples: output_states=() for resnet, attn in zip(self.resnets, self.attentions): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] # FreeU: Only operate on the first two stages if is_freeu_enabled: hidden_states, res_hidden_states = apply_freeu( self.resolution_idx, hidden_states, res_hidden_states, s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2, ) hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] else: hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] if return_res_samples: output_states = output_states + (hidden_states,) if up_block_add_samples is not None: hidden_states = hidden_states + up_block_add_samples.pop(0) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size, scale=lora_scale) if return_res_samples: output_states = output_states + (hidden_states,) if up_block_add_samples is not None: hidden_states = hidden_states + up_block_add_samples.pop(0) if return_res_samples: return hidden_states, output_states else: return hidden_states class UpBlock2D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, add_upsample: bool = True, ): super().__init__() resnets = [] for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, upsample_size: Optional[int] = None, scale: float = 1.0, return_res_samples: Optional[bool]=False, up_block_add_samples: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: is_freeu_enabled = ( getattr(self, "s1", None) and getattr(self, "s2", None) and getattr(self, "b1", None) and getattr(self, "b2", None) ) if return_res_samples: output_states = () for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] # FreeU: Only operate on the first two stages if is_freeu_enabled: hidden_states, res_hidden_states = apply_freeu( self.resolution_idx, hidden_states, res_hidden_states, s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2, ) hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb, scale=scale) if return_res_samples: output_states = output_states + (hidden_states,) if up_block_add_samples is not None: hidden_states = hidden_states + up_block_add_samples.pop(0) # todo: add before or after if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size, scale=scale) if return_res_samples: output_states = output_states + (hidden_states,) if up_block_add_samples is not None: hidden_states = hidden_states + up_block_add_samples.pop(0) # todo: add before or after if return_res_samples: return hidden_states, output_states else: return hidden_states class UpDecoderBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", # default, spatial resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, add_upsample: bool = True, temb_channels: Optional[int] = None, ): super().__init__() resnets = [] for i in range(num_layers): input_channels = in_channels if i == 0 else out_channels if resnet_time_scale_shift == "spatial": resnets.append( ResnetBlockCondNorm2D( in_channels=input_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm="spatial", non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, ) ) else: resnets.append( ResnetBlock2D( in_channels=input_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0 ) -> torch.FloatTensor: for resnet in self.resnets: hidden_states = resnet(hidden_states, temb=temb, scale=scale) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states class AttnUpDecoderBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attention_head_dim: int = 1, output_scale_factor: float = 1.0, add_upsample: bool = True, temb_channels: Optional[int] = None, ): super().__init__() resnets = [] attentions = [] if attention_head_dim is None: logger.warn( f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}." ) attention_head_dim = out_channels for i in range(num_layers): input_channels = in_channels if i == 0 else out_channels if resnet_time_scale_shift == "spatial": resnets.append( ResnetBlockCondNorm2D( in_channels=input_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm="spatial", non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, ) ) else: resnets.append( ResnetBlock2D( in_channels=input_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) attentions.append( Attention( out_channels, heads=out_channels // attention_head_dim, dim_head=attention_head_dim, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=resnet_groups if resnet_time_scale_shift != "spatial" else None, spatial_norm_dim=temb_channels if resnet_time_scale_shift == "spatial" else None, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0 ) -> torch.FloatTensor: for resnet, attn in zip(self.resnets, self.attentions): hidden_states = resnet(hidden_states, temb=temb, scale=scale) cross_attention_kwargs = {"scale": scale} hidden_states = attn(hidden_states, temb=temb, **cross_attention_kwargs) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, scale=scale) return hidden_states class AttnSkipUpBlock2D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_pre_norm: bool = True, attention_head_dim: int = 1, output_scale_factor: float = np.sqrt(2.0), add_upsample: bool = True, ): super().__init__() self.attentions = nn.ModuleList([]) self.resnets = nn.ModuleList([]) for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels self.resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(resnet_in_channels + res_skip_channels // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if attention_head_dim is None: logger.warn( f"It is not recommend to pass `attention_head_dim=None`. Defaulting `attention_head_dim` to `out_channels`: {out_channels}." ) attention_head_dim = out_channels self.attentions.append( Attention( out_channels, heads=out_channels // attention_head_dim, dim_head=attention_head_dim, rescale_output_factor=output_scale_factor, eps=resnet_eps, norm_num_groups=32, residual_connection=True, bias=True, upcast_softmax=True, _from_deprecated_attn_block=True, ) ) self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels) if add_upsample: self.resnet_up = ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(out_channels // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, use_in_shortcut=True, up=True, kernel="fir", ) self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.skip_norm = torch.nn.GroupNorm( num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True ) self.act = nn.SiLU() else: self.resnet_up = None self.skip_conv = None self.skip_norm = None self.act = None self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, skip_sample=None, scale: float = 1.0, ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) hidden_states = resnet(hidden_states, temb, scale=scale) cross_attention_kwargs = {"scale": scale} hidden_states = self.attentions[0](hidden_states, **cross_attention_kwargs) if skip_sample is not None: skip_sample = self.upsampler(skip_sample) else: skip_sample = 0 if self.resnet_up is not None: skip_sample_states = self.skip_norm(hidden_states) skip_sample_states = self.act(skip_sample_states) skip_sample_states = self.skip_conv(skip_sample_states) skip_sample = skip_sample + skip_sample_states hidden_states = self.resnet_up(hidden_states, temb, scale=scale) return hidden_states, skip_sample class SkipUpBlock2D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_pre_norm: bool = True, output_scale_factor: float = np.sqrt(2.0), add_upsample: bool = True, upsample_padding: int = 1, ): super().__init__() self.resnets = nn.ModuleList([]) for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels self.resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min((resnet_in_channels + res_skip_channels) // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) self.upsampler = FirUpsample2D(in_channels, out_channels=out_channels) if add_upsample: self.resnet_up = ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=min(out_channels // 4, 32), groups_out=min(out_channels // 4, 32), dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, use_in_shortcut=True, up=True, kernel="fir", ) self.skip_conv = nn.Conv2d(out_channels, 3, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) self.skip_norm = torch.nn.GroupNorm( num_groups=min(out_channels // 4, 32), num_channels=out_channels, eps=resnet_eps, affine=True ) self.act = nn.SiLU() else: self.resnet_up = None self.skip_conv = None self.skip_norm = None self.act = None self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, skip_sample=None, scale: float = 1.0, ) -> Tuple[torch.FloatTensor, torch.FloatTensor]: for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) hidden_states = resnet(hidden_states, temb, scale=scale) if skip_sample is not None: skip_sample = self.upsampler(skip_sample) else: skip_sample = 0 if self.resnet_up is not None: skip_sample_states = self.skip_norm(hidden_states) skip_sample_states = self.act(skip_sample_states) skip_sample_states = self.skip_conv(skip_sample_states) skip_sample = skip_sample + skip_sample_states hidden_states = self.resnet_up(hidden_states, temb, scale=scale) return hidden_states, skip_sample class ResnetUpsampleBlock2D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, add_upsample: bool = True, skip_time_act: bool = False, ): super().__init__() resnets = [] for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList( [ ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, up=True, ) ] ) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, upsample_size: Optional[int] = None, scale: float = 1.0, ) -> torch.FloatTensor: for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb, scale=scale) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, temb, scale=scale) return hidden_states class SimpleCrossAttnUpBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, attention_head_dim: int = 1, cross_attention_dim: int = 1280, output_scale_factor: float = 1.0, add_upsample: bool = True, skip_time_act: bool = False, only_cross_attention: bool = False, cross_attention_norm: Optional[str] = None, ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.attention_head_dim = attention_head_dim self.num_heads = out_channels // self.attention_head_dim for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, ) ) processor = ( AttnAddedKVProcessor2_0() if hasattr(F, "scaled_dot_product_attention") else AttnAddedKVProcessor() ) attentions.append( Attention( query_dim=out_channels, cross_attention_dim=out_channels, heads=self.num_heads, dim_head=self.attention_head_dim, added_kv_proj_dim=cross_attention_dim, norm_num_groups=resnet_groups, bias=True, upcast_softmax=True, only_cross_attention=only_cross_attention, cross_attention_norm=cross_attention_norm, processor=processor, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList( [ ResnetBlock2D( in_channels=out_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, skip_time_act=skip_time_act, up=True, ) ] ) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, upsample_size: Optional[int] = None, attention_mask: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} lora_scale = cross_attention_kwargs.get("scale", 1.0) if attention_mask is None: # if encoder_hidden_states is defined: we are doing cross-attn, so we should use cross-attn mask. mask = None if encoder_hidden_states is None else encoder_attention_mask else: # when attention_mask is defined: we don't even check for encoder_attention_mask. # this is to maintain compatibility with UnCLIP, which uses 'attention_mask' param for cross-attn masks. # TODO: UnCLIP should express cross-attn mask via encoder_attention_mask param instead of via attention_mask. # then we can simplify this whole if/else block to: # mask = attention_mask if encoder_hidden_states is None else encoder_attention_mask mask = attention_mask for resnet, attn in zip(self.resnets, self.attentions): # resnet # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward hidden_states = torch.utils.checkpoint.checkpoint(create_custom_forward(resnet), hidden_states, temb) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=mask, **cross_attention_kwargs, ) else: hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=mask, **cross_attention_kwargs, ) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, temb, scale=lora_scale) return hidden_states class KUpBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, resolution_idx: int, dropout: float = 0.0, num_layers: int = 5, resnet_eps: float = 1e-5, resnet_act_fn: str = "gelu", resnet_group_size: Optional[int] = 32, add_upsample: bool = True, ): super().__init__() resnets = [] k_in_channels = 2 * out_channels k_out_channels = in_channels num_layers = num_layers - 1 for i in range(num_layers): in_channels = k_in_channels if i == 0 else out_channels groups = in_channels // resnet_group_size groups_out = out_channels // resnet_group_size resnets.append( ResnetBlockCondNorm2D( in_channels=in_channels, out_channels=k_out_channels if (i == num_layers - 1) else out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=groups, groups_out=groups_out, dropout=dropout, non_linearity=resnet_act_fn, time_embedding_norm="ada_group", conv_shortcut_bias=False, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([KUpsample2D()]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, upsample_size: Optional[int] = None, scale: float = 1.0, ) -> torch.FloatTensor: res_hidden_states_tuple = res_hidden_states_tuple[-1] if res_hidden_states_tuple is not None: hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1) for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) else: hidden_states = resnet(hidden_states, temb, scale=scale) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states class KCrossAttnUpBlock2D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, resolution_idx: int, dropout: float = 0.0, num_layers: int = 4, resnet_eps: float = 1e-5, resnet_act_fn: str = "gelu", resnet_group_size: int = 32, attention_head_dim: int = 1, # attention dim_head cross_attention_dim: int = 768, add_upsample: bool = True, upcast_attention: bool = False, ): super().__init__() resnets = [] attentions = [] is_first_block = in_channels == out_channels == temb_channels is_middle_block = in_channels != out_channels add_self_attention = True if is_first_block else False self.has_cross_attention = True self.attention_head_dim = attention_head_dim # in_channels, and out_channels for the block (k-unet) k_in_channels = out_channels if is_first_block else 2 * out_channels k_out_channels = in_channels num_layers = num_layers - 1 for i in range(num_layers): in_channels = k_in_channels if i == 0 else out_channels groups = in_channels // resnet_group_size groups_out = out_channels // resnet_group_size if is_middle_block and (i == num_layers - 1): conv_2d_out_channels = k_out_channels else: conv_2d_out_channels = None resnets.append( ResnetBlockCondNorm2D( in_channels=in_channels, out_channels=out_channels, conv_2d_out_channels=conv_2d_out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=groups, groups_out=groups_out, dropout=dropout, non_linearity=resnet_act_fn, time_embedding_norm="ada_group", conv_shortcut_bias=False, ) ) attentions.append( KAttentionBlock( k_out_channels if (i == num_layers - 1) else out_channels, k_out_channels // attention_head_dim if (i == num_layers - 1) else out_channels // attention_head_dim, attention_head_dim, cross_attention_dim=cross_attention_dim, temb_channels=temb_channels, attention_bias=True, add_self_attention=add_self_attention, cross_attention_norm="layer_norm", upcast_attention=upcast_attention, ) ) self.resnets = nn.ModuleList(resnets) self.attentions = nn.ModuleList(attentions) if add_upsample: self.upsamplers = nn.ModuleList([KUpsample2D()]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, upsample_size: Optional[int] = None, attention_mask: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: res_hidden_states_tuple = res_hidden_states_tuple[-1] if res_hidden_states_tuple is not None: hidden_states = torch.cat([hidden_states, res_hidden_states_tuple], dim=1) lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 for resnet, attn in zip(self.resnets, self.attentions): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, emb=temb, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, encoder_attention_mask=encoder_attention_mask, ) else: hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, emb=temb, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, encoder_attention_mask=encoder_attention_mask, ) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states # can potentially later be renamed to `No-feed-forward` attention class KAttentionBlock(nn.Module): r""" A basic Transformer block. Parameters: dim (`int`): The number of channels in the input and output. num_attention_heads (`int`): The number of heads to use for multi-head attention. attention_head_dim (`int`): The number of channels in each head. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. cross_attention_dim (`int`, *optional*): The size of the encoder_hidden_states vector for cross attention. attention_bias (`bool`, *optional*, defaults to `False`): Configure if the attention layers should contain a bias parameter. upcast_attention (`bool`, *optional*, defaults to `False`): Set to `True` to upcast the attention computation to `float32`. temb_channels (`int`, *optional*, defaults to 768): The number of channels in the token embedding. add_self_attention (`bool`, *optional*, defaults to `False`): Set to `True` to add self-attention to the block. cross_attention_norm (`str`, *optional*, defaults to `None`): The type of normalization to use for the cross attention. Can be `None`, `layer_norm`, or `group_norm`. group_size (`int`, *optional*, defaults to 32): The number of groups to separate the channels into for group normalization. """ def __init__( self, dim: int, num_attention_heads: int, attention_head_dim: int, dropout: float = 0.0, cross_attention_dim: Optional[int] = None, attention_bias: bool = False, upcast_attention: bool = False, temb_channels: int = 768, # for ada_group_norm add_self_attention: bool = False, cross_attention_norm: Optional[str] = None, group_size: int = 32, ): super().__init__() self.add_self_attention = add_self_attention # 1. Self-Attn if add_self_attention: self.norm1 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size)) self.attn1 = Attention( query_dim=dim, heads=num_attention_heads, dim_head=attention_head_dim, dropout=dropout, bias=attention_bias, cross_attention_dim=None, cross_attention_norm=None, ) # 2. Cross-Attn self.norm2 = AdaGroupNorm(temb_channels, dim, max(1, dim // group_size)) self.attn2 = Attention( query_dim=dim, cross_attention_dim=cross_attention_dim, heads=num_attention_heads, dim_head=attention_head_dim, dropout=dropout, bias=attention_bias, upcast_attention=upcast_attention, cross_attention_norm=cross_attention_norm, ) def _to_3d(self, hidden_states: torch.FloatTensor, height: int, weight: int) -> torch.FloatTensor: return hidden_states.permute(0, 2, 3, 1).reshape(hidden_states.shape[0], height * weight, -1) def _to_4d(self, hidden_states: torch.FloatTensor, height: int, weight: int) -> torch.FloatTensor: return hidden_states.permute(0, 2, 1).reshape(hidden_states.shape[0], -1, height, weight) def forward( self, hidden_states: torch.FloatTensor, encoder_hidden_states: Optional[torch.FloatTensor] = None, # TODO: mark emb as non-optional (self.norm2 requires it). # requires assessing impact of change to positional param interface. emb: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: cross_attention_kwargs = cross_attention_kwargs if cross_attention_kwargs is not None else {} # 1. Self-Attention if self.add_self_attention: norm_hidden_states = self.norm1(hidden_states, emb) height, weight = norm_hidden_states.shape[2:] norm_hidden_states = self._to_3d(norm_hidden_states, height, weight) attn_output = self.attn1( norm_hidden_states, encoder_hidden_states=None, attention_mask=attention_mask, **cross_attention_kwargs, ) attn_output = self._to_4d(attn_output, height, weight) hidden_states = attn_output + hidden_states # 2. Cross-Attention/None norm_hidden_states = self.norm2(hidden_states, emb) height, weight = norm_hidden_states.shape[2:] norm_hidden_states = self._to_3d(norm_hidden_states, height, weight) attn_output = self.attn2( norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask if encoder_hidden_states is None else encoder_attention_mask, **cross_attention_kwargs, ) attn_output = self._to_4d(attn_output, height, weight) hidden_states = attn_output + hidden_states return hidden_states ================================================ FILE: libs/unet_2d_condition.py ================================================ # Copyright 2024 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn import torch.utils.checkpoint from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.loaders import PeftAdapterMixin, UNet2DConditionLoadersMixin from diffusers.utils import USE_PEFT_BACKEND, BaseOutput, deprecate, logging, scale_lora_layers, unscale_lora_layers from diffusers.models.activations import get_activation from diffusers.models.attention_processor import ( ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, Attention, AttentionProcessor, AttnAddedKVProcessor, AttnProcessor, ) from diffusers.models.embeddings import ( GaussianFourierProjection, GLIGENTextBoundingboxProjection, ImageHintTimeEmbedding, ImageProjection, ImageTimeEmbedding, TextImageProjection, TextImageTimeEmbedding, TextTimeEmbedding, TimestepEmbedding, Timesteps, ) from diffusers.models.modeling_utils import ModelMixin from .unet_2d_blocks import ( get_down_block, get_mid_block, get_up_block, ) logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class UNet2DConditionOutput(BaseOutput): """ The output of [`UNet2DConditionModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): The hidden states output conditioned on `encoder_hidden_states` input. Output of last layer of model. """ sample: torch.FloatTensor = None class UNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin, PeftAdapterMixin): r""" A conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). Parameters: sample_size (`int` or `Tuple[int, int]`, *optional*, defaults to `None`): Height and width of input/output sample. in_channels (`int`, *optional*, defaults to 4): Number of channels in the input sample. out_channels (`int`, *optional*, defaults to 4): Number of channels in the output. center_input_sample (`bool`, *optional*, defaults to `False`): Whether to center the input sample. flip_sin_to_cos (`bool`, *optional*, defaults to `False`): Whether to flip the sin to cos in the time embedding. freq_shift (`int`, *optional*, defaults to 0): The frequency shift to apply to the time embedding. down_block_types (`Tuple[str]`, *optional*, defaults to `("CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D")`): The tuple of downsample blocks to use. mid_block_type (`str`, *optional*, defaults to `"UNetMidBlock2DCrossAttn"`): Block type for middle of UNet, it can be one of `UNetMidBlock2DCrossAttn`, `UNetMidBlock2D`, or `UNetMidBlock2DSimpleCrossAttn`. If `None`, the mid block layer is skipped. up_block_types (`Tuple[str]`, *optional*, defaults to `("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")`): The tuple of upsample blocks to use. only_cross_attention(`bool` or `Tuple[bool]`, *optional*, default to `False`): Whether to include self-attention in the basic transformer blocks, see [`~models.attention.BasicTransformerBlock`]. block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each block. layers_per_block (`int`, *optional*, defaults to 2): The number of layers per block. downsample_padding (`int`, *optional*, defaults to 1): The padding to use for the downsampling convolution. mid_block_scale_factor (`float`, *optional*, defaults to 1.0): The scale factor to use for the mid block. dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use. act_fn (`str`, *optional*, defaults to `"silu"`): The activation function to use. norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use for the normalization. If `None`, normalization and activation layers is skipped in post-processing. norm_eps (`float`, *optional*, defaults to 1e-5): The epsilon to use for the normalization. cross_attention_dim (`int` or `Tuple[int]`, *optional*, defaults to 1280): The dimension of the cross attention features. transformer_layers_per_block (`int`, `Tuple[int]`, or `Tuple[Tuple]` , *optional*, defaults to 1): The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`]. Only relevant for [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. reverse_transformer_layers_per_block : (`Tuple[Tuple]`, *optional*, defaults to None): The number of transformer blocks of type [`~models.attention.BasicTransformerBlock`], in the upsampling blocks of the U-Net. Only relevant if `transformer_layers_per_block` is of type `Tuple[Tuple]` and for [`~models.unet_2d_blocks.CrossAttnDownBlock2D`], [`~models.unet_2d_blocks.CrossAttnUpBlock2D`], [`~models.unet_2d_blocks.UNetMidBlock2DCrossAttn`]. encoder_hid_dim (`int`, *optional*, defaults to None): If `encoder_hid_dim_type` is defined, `encoder_hidden_states` will be projected from `encoder_hid_dim` dimension to `cross_attention_dim`. encoder_hid_dim_type (`str`, *optional*, defaults to `None`): If given, the `encoder_hidden_states` and potentially other embeddings are down-projected to text embeddings of dimension `cross_attention` according to `encoder_hid_dim_type`. attention_head_dim (`int`, *optional*, defaults to 8): The dimension of the attention heads. num_attention_heads (`int`, *optional*): The number of attention heads. If not defined, defaults to `attention_head_dim` resnet_time_scale_shift (`str`, *optional*, defaults to `"default"`): Time scale shift config for ResNet blocks (see [`~models.resnet.ResnetBlock2D`]). Choose from `default` or `scale_shift`. class_embed_type (`str`, *optional*, defaults to `None`): The type of class embedding to use which is ultimately summed with the time embeddings. Choose from `None`, `"timestep"`, `"identity"`, `"projection"`, or `"simple_projection"`. addition_embed_type (`str`, *optional*, defaults to `None`): Configures an optional embedding which will be summed with the time embeddings. Choose from `None` or "text". "text" will use the `TextTimeEmbedding` layer. addition_time_embed_dim: (`int`, *optional*, defaults to `None`): Dimension for the timestep embeddings. num_class_embeds (`int`, *optional*, defaults to `None`): Input dimension of the learnable embedding matrix to be projected to `time_embed_dim`, when performing class conditioning with `class_embed_type` equal to `None`. time_embedding_type (`str`, *optional*, defaults to `positional`): The type of position embedding to use for timesteps. Choose from `positional` or `fourier`. time_embedding_dim (`int`, *optional*, defaults to `None`): An optional override for the dimension of the projected time embedding. time_embedding_act_fn (`str`, *optional*, defaults to `None`): Optional activation function to use only once on the time embeddings before they are passed to the rest of the UNet. Choose from `silu`, `mish`, `gelu`, and `swish`. timestep_post_act (`str`, *optional*, defaults to `None`): The second activation function to use in timestep embedding. Choose from `silu`, `mish` and `gelu`. time_cond_proj_dim (`int`, *optional*, defaults to `None`): The dimension of `cond_proj` layer in the timestep embedding. conv_in_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_in` layer. conv_out_kernel (`int`, *optional*, default to `3`): The kernel size of `conv_out` layer. projection_class_embeddings_input_dim (`int`, *optional*): The dimension of the `class_labels` input when `class_embed_type="projection"`. Required when `class_embed_type="projection"`. class_embeddings_concat (`bool`, *optional*, defaults to `False`): Whether to concatenate the time embeddings with the class embeddings. mid_block_only_cross_attention (`bool`, *optional*, defaults to `None`): Whether to use cross attention with the mid block when using the `UNetMidBlock2DSimpleCrossAttn`. If `only_cross_attention` is given as a single boolean and `mid_block_only_cross_attention` is `None`, the `only_cross_attention` value is used as the value for `mid_block_only_cross_attention`. Default to `False` otherwise. """ _supports_gradient_checkpointing = True @register_to_config def __init__( self, sample_size: Optional[int] = None, in_channels: int = 4, out_channels: int = 4, center_input_sample: bool = False, flip_sin_to_cos: bool = True, freq_shift: int = 0, down_block_types: Tuple[str] = ( "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "CrossAttnDownBlock2D", "DownBlock2D", ), mid_block_type: Optional[str] = "UNetMidBlock2DCrossAttn", up_block_types: Tuple[str] = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D"), only_cross_attention: Union[bool, Tuple[bool]] = False, block_out_channels: Tuple[int] = (320, 640, 1280, 1280), layers_per_block: Union[int, Tuple[int]] = 2, downsample_padding: int = 1, mid_block_scale_factor: float = 1, dropout: float = 0.0, act_fn: str = "silu", norm_num_groups: Optional[int] = 32, norm_eps: float = 1e-5, cross_attention_dim: Union[int, Tuple[int]] = 1280, transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple]] = 1, reverse_transformer_layers_per_block: Optional[Tuple[Tuple[int]]] = None, encoder_hid_dim: Optional[int] = None, encoder_hid_dim_type: Optional[str] = None, attention_head_dim: Union[int, Tuple[int]] = 8, num_attention_heads: Optional[Union[int, Tuple[int]]] = None, dual_cross_attention: bool = False, use_linear_projection: bool = False, class_embed_type: Optional[str] = None, addition_embed_type: Optional[str] = None, addition_time_embed_dim: Optional[int] = None, num_class_embeds: Optional[int] = None, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", resnet_skip_time_act: bool = False, resnet_out_scale_factor: float = 1.0, time_embedding_type: str = "positional", time_embedding_dim: Optional[int] = None, time_embedding_act_fn: Optional[str] = None, timestep_post_act: Optional[str] = None, time_cond_proj_dim: Optional[int] = None, conv_in_kernel: int = 3, conv_out_kernel: int = 3, projection_class_embeddings_input_dim: Optional[int] = None, attention_type: str = "default", class_embeddings_concat: bool = False, mid_block_only_cross_attention: Optional[bool] = None, cross_attention_norm: Optional[str] = None, addition_embed_type_num_heads: int = 64, ): super().__init__() self.sample_size = sample_size if num_attention_heads is not None: raise ValueError( "At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19." ) # If `num_attention_heads` is not defined (which is the case for most models) # it will default to `attention_head_dim`. This looks weird upon first reading it and it is. # The reason for this behavior is to correct for incorrectly named variables that were introduced # when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131 # Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking # which is why we correct for the naming here. num_attention_heads = num_attention_heads or attention_head_dim # Check inputs self._check_config( down_block_types=down_block_types, up_block_types=up_block_types, only_cross_attention=only_cross_attention, block_out_channels=block_out_channels, layers_per_block=layers_per_block, cross_attention_dim=cross_attention_dim, transformer_layers_per_block=transformer_layers_per_block, reverse_transformer_layers_per_block=reverse_transformer_layers_per_block, attention_head_dim=attention_head_dim, num_attention_heads=num_attention_heads, ) # input conv_in_padding = (conv_in_kernel - 1) // 2 self.conv_in = nn.Conv2d( in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding ) # time time_embed_dim, timestep_input_dim = self._set_time_proj( time_embedding_type, block_out_channels=block_out_channels, flip_sin_to_cos=flip_sin_to_cos, freq_shift=freq_shift, time_embedding_dim=time_embedding_dim, ) self.time_embedding = TimestepEmbedding( timestep_input_dim, time_embed_dim, act_fn=act_fn, post_act_fn=timestep_post_act, cond_proj_dim=time_cond_proj_dim, ) self._set_encoder_hid_proj( encoder_hid_dim_type, cross_attention_dim=cross_attention_dim, encoder_hid_dim=encoder_hid_dim, ) # class embedding self._set_class_embedding( class_embed_type, act_fn=act_fn, num_class_embeds=num_class_embeds, projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, time_embed_dim=time_embed_dim, timestep_input_dim=timestep_input_dim, ) self._set_add_embedding( addition_embed_type, addition_embed_type_num_heads=addition_embed_type_num_heads, addition_time_embed_dim=addition_time_embed_dim, cross_attention_dim=cross_attention_dim, encoder_hid_dim=encoder_hid_dim, flip_sin_to_cos=flip_sin_to_cos, freq_shift=freq_shift, projection_class_embeddings_input_dim=projection_class_embeddings_input_dim, time_embed_dim=time_embed_dim, ) if time_embedding_act_fn is None: self.time_embed_act = None else: self.time_embed_act = get_activation(time_embedding_act_fn) self.down_blocks = nn.ModuleList([]) self.up_blocks = nn.ModuleList([]) if isinstance(only_cross_attention, bool): if mid_block_only_cross_attention is None: mid_block_only_cross_attention = only_cross_attention only_cross_attention = [only_cross_attention] * len(down_block_types) if mid_block_only_cross_attention is None: mid_block_only_cross_attention = False if isinstance(num_attention_heads, int): num_attention_heads = (num_attention_heads,) * len(down_block_types) if isinstance(attention_head_dim, int): attention_head_dim = (attention_head_dim,) * len(down_block_types) if isinstance(cross_attention_dim, int): cross_attention_dim = (cross_attention_dim,) * len(down_block_types) if isinstance(layers_per_block, int): layers_per_block = [layers_per_block] * len(down_block_types) if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * len(down_block_types) if class_embeddings_concat: # The time embeddings are concatenated with the class embeddings. The dimension of the # time embeddings passed to the down, middle, and up blocks is twice the dimension of the # regular time embeddings blocks_time_embed_dim = time_embed_dim * 2 else: blocks_time_embed_dim = time_embed_dim # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block[i], transformer_layers_per_block=transformer_layers_per_block[i], in_channels=input_channel, out_channels=output_channel, temb_channels=blocks_time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim[i], num_attention_heads=num_attention_heads[i], downsample_padding=downsample_padding, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, attention_type=attention_type, resnet_skip_time_act=resnet_skip_time_act, resnet_out_scale_factor=resnet_out_scale_factor, cross_attention_norm=cross_attention_norm, attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, dropout=dropout, ) self.down_blocks.append(down_block) # mid self.mid_block = get_mid_block( mid_block_type, temb_channels=blocks_time_embed_dim, in_channels=block_out_channels[-1], resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, output_scale_factor=mid_block_scale_factor, transformer_layers_per_block=transformer_layers_per_block[-1], num_attention_heads=num_attention_heads[-1], cross_attention_dim=cross_attention_dim[-1], dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, mid_block_only_cross_attention=mid_block_only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, attention_type=attention_type, resnet_skip_time_act=resnet_skip_time_act, cross_attention_norm=cross_attention_norm, attention_head_dim=attention_head_dim[-1], dropout=dropout, ) # count how many layers upsample the images self.num_upsamplers = 0 # up reversed_block_out_channels = list(reversed(block_out_channels)) reversed_num_attention_heads = list(reversed(num_attention_heads)) reversed_layers_per_block = list(reversed(layers_per_block)) reversed_cross_attention_dim = list(reversed(cross_attention_dim)) reversed_transformer_layers_per_block = ( list(reversed(transformer_layers_per_block)) if reverse_transformer_layers_per_block is None else reverse_transformer_layers_per_block ) only_cross_attention = list(reversed(only_cross_attention)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): is_final_block = i == len(block_out_channels) - 1 prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] # add upsample block for all BUT final layer if not is_final_block: add_upsample = True self.num_upsamplers += 1 else: add_upsample = False up_block = get_up_block( up_block_type, num_layers=reversed_layers_per_block[i] + 1, transformer_layers_per_block=reversed_transformer_layers_per_block[i], in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=blocks_time_embed_dim, add_upsample=add_upsample, resnet_eps=norm_eps, resnet_act_fn=act_fn, resolution_idx=i, resnet_groups=norm_num_groups, cross_attention_dim=reversed_cross_attention_dim[i], num_attention_heads=reversed_num_attention_heads[i], dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention[i], upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, attention_type=attention_type, resnet_skip_time_act=resnet_skip_time_act, resnet_out_scale_factor=resnet_out_scale_factor, cross_attention_norm=cross_attention_norm, attention_head_dim=attention_head_dim[i] if attention_head_dim[i] is not None else output_channel, dropout=dropout, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out if norm_num_groups is not None: self.conv_norm_out = nn.GroupNorm( num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps ) self.conv_act = get_activation(act_fn) else: self.conv_norm_out = None self.conv_act = None conv_out_padding = (conv_out_kernel - 1) // 2 self.conv_out = nn.Conv2d( block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding ) self._set_pos_net_if_use_gligen(attention_type=attention_type, cross_attention_dim=cross_attention_dim) def _check_config( self, down_block_types: Tuple[str], up_block_types: Tuple[str], only_cross_attention: Union[bool, Tuple[bool]], block_out_channels: Tuple[int], layers_per_block: Union[int, Tuple[int]], cross_attention_dim: Union[int, Tuple[int]], transformer_layers_per_block: Union[int, Tuple[int], Tuple[Tuple[int]]], reverse_transformer_layers_per_block: bool, attention_head_dim: int, num_attention_heads: Optional[Union[int, Tuple[int]]], ): if len(down_block_types) != len(up_block_types): raise ValueError( f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." ) if len(block_out_channels) != len(down_block_types): raise ValueError( f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) if not isinstance(only_cross_attention, bool) and len(only_cross_attention) != len(down_block_types): raise ValueError( f"Must provide the same number of `only_cross_attention` as `down_block_types`. `only_cross_attention`: {only_cross_attention}. `down_block_types`: {down_block_types}." ) if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): raise ValueError( f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." ) if not isinstance(attention_head_dim, int) and len(attention_head_dim) != len(down_block_types): raise ValueError( f"Must provide the same number of `attention_head_dim` as `down_block_types`. `attention_head_dim`: {attention_head_dim}. `down_block_types`: {down_block_types}." ) if isinstance(cross_attention_dim, list) and len(cross_attention_dim) != len(down_block_types): raise ValueError( f"Must provide the same number of `cross_attention_dim` as `down_block_types`. `cross_attention_dim`: {cross_attention_dim}. `down_block_types`: {down_block_types}." ) if not isinstance(layers_per_block, int) and len(layers_per_block) != len(down_block_types): raise ValueError( f"Must provide the same number of `layers_per_block` as `down_block_types`. `layers_per_block`: {layers_per_block}. `down_block_types`: {down_block_types}." ) if isinstance(transformer_layers_per_block, list) and reverse_transformer_layers_per_block is None: for layer_number_per_block in transformer_layers_per_block: if isinstance(layer_number_per_block, list): raise ValueError("Must provide 'reverse_transformer_layers_per_block` if using asymmetrical UNet.") def _set_time_proj( self, time_embedding_type: str, block_out_channels: int, flip_sin_to_cos: bool, freq_shift: float, time_embedding_dim: int, ) -> Tuple[int, int]: if time_embedding_type == "fourier": time_embed_dim = time_embedding_dim or block_out_channels[0] * 2 if time_embed_dim % 2 != 0: raise ValueError(f"`time_embed_dim` should be divisible by 2, but is {time_embed_dim}.") self.time_proj = GaussianFourierProjection( time_embed_dim // 2, set_W_to_weight=False, log=False, flip_sin_to_cos=flip_sin_to_cos ) timestep_input_dim = time_embed_dim elif time_embedding_type == "positional": time_embed_dim = time_embedding_dim or block_out_channels[0] * 4 self.time_proj = Timesteps(block_out_channels[0], flip_sin_to_cos, freq_shift) timestep_input_dim = block_out_channels[0] else: raise ValueError( f"{time_embedding_type} does not exist. Please make sure to use one of `fourier` or `positional`." ) return time_embed_dim, timestep_input_dim def _set_encoder_hid_proj( self, encoder_hid_dim_type: Optional[str], cross_attention_dim: Union[int, Tuple[int]], encoder_hid_dim: Optional[int], ): if encoder_hid_dim_type is None and encoder_hid_dim is not None: encoder_hid_dim_type = "text_proj" self.register_to_config(encoder_hid_dim_type=encoder_hid_dim_type) logger.info("encoder_hid_dim_type defaults to 'text_proj' as `encoder_hid_dim` is defined.") if encoder_hid_dim is None and encoder_hid_dim_type is not None: raise ValueError( f"`encoder_hid_dim` has to be defined when `encoder_hid_dim_type` is set to {encoder_hid_dim_type}." ) if encoder_hid_dim_type == "text_proj": self.encoder_hid_proj = nn.Linear(encoder_hid_dim, cross_attention_dim) elif encoder_hid_dim_type == "text_image_proj": # image_embed_dim DOESN'T have to be `cross_attention_dim`. To not clutter the __init__ too much # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use # case when `addition_embed_type == "text_image_proj"` (Kadinsky 2.1)` self.encoder_hid_proj = TextImageProjection( text_embed_dim=encoder_hid_dim, image_embed_dim=cross_attention_dim, cross_attention_dim=cross_attention_dim, ) elif encoder_hid_dim_type == "image_proj": # Kandinsky 2.2 self.encoder_hid_proj = ImageProjection( image_embed_dim=encoder_hid_dim, cross_attention_dim=cross_attention_dim, ) elif encoder_hid_dim_type is not None: raise ValueError( f"encoder_hid_dim_type: {encoder_hid_dim_type} must be None, 'text_proj' or 'text_image_proj'." ) else: self.encoder_hid_proj = None def _set_class_embedding( self, class_embed_type: Optional[str], act_fn: str, num_class_embeds: Optional[int], projection_class_embeddings_input_dim: Optional[int], time_embed_dim: int, timestep_input_dim: int, ): if class_embed_type is None and num_class_embeds is not None: self.class_embedding = nn.Embedding(num_class_embeds, time_embed_dim) elif class_embed_type == "timestep": self.class_embedding = TimestepEmbedding(timestep_input_dim, time_embed_dim, act_fn=act_fn) elif class_embed_type == "identity": self.class_embedding = nn.Identity(time_embed_dim, time_embed_dim) elif class_embed_type == "projection": if projection_class_embeddings_input_dim is None: raise ValueError( "`class_embed_type`: 'projection' requires `projection_class_embeddings_input_dim` be set" ) # The projection `class_embed_type` is the same as the timestep `class_embed_type` except # 1. the `class_labels` inputs are not first converted to sinusoidal embeddings # 2. it projects from an arbitrary input dimension. # # Note that `TimestepEmbedding` is quite general, being mainly linear layers and activations. # When used for embedding actual timesteps, the timesteps are first converted to sinusoidal embeddings. # As a result, `TimestepEmbedding` can be passed arbitrary vectors. self.class_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) elif class_embed_type == "simple_projection": if projection_class_embeddings_input_dim is None: raise ValueError( "`class_embed_type`: 'simple_projection' requires `projection_class_embeddings_input_dim` be set" ) self.class_embedding = nn.Linear(projection_class_embeddings_input_dim, time_embed_dim) else: self.class_embedding = None def _set_add_embedding( self, addition_embed_type: str, addition_embed_type_num_heads: int, addition_time_embed_dim: Optional[int], flip_sin_to_cos: bool, freq_shift: float, cross_attention_dim: Optional[int], encoder_hid_dim: Optional[int], projection_class_embeddings_input_dim: Optional[int], time_embed_dim: int, ): if addition_embed_type == "text": if encoder_hid_dim is not None: text_time_embedding_from_dim = encoder_hid_dim else: text_time_embedding_from_dim = cross_attention_dim self.add_embedding = TextTimeEmbedding( text_time_embedding_from_dim, time_embed_dim, num_heads=addition_embed_type_num_heads ) elif addition_embed_type == "text_image": # text_embed_dim and image_embed_dim DON'T have to be `cross_attention_dim`. To not clutter the __init__ too much # they are set to `cross_attention_dim` here as this is exactly the required dimension for the currently only use # case when `addition_embed_type == "text_image"` (Kadinsky 2.1)` self.add_embedding = TextImageTimeEmbedding( text_embed_dim=cross_attention_dim, image_embed_dim=cross_attention_dim, time_embed_dim=time_embed_dim ) elif addition_embed_type == "text_time": self.add_time_proj = Timesteps(addition_time_embed_dim, flip_sin_to_cos, freq_shift) self.add_embedding = TimestepEmbedding(projection_class_embeddings_input_dim, time_embed_dim) elif addition_embed_type == "image": # Kandinsky 2.2 self.add_embedding = ImageTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) elif addition_embed_type == "image_hint": # Kandinsky 2.2 ControlNet self.add_embedding = ImageHintTimeEmbedding(image_embed_dim=encoder_hid_dim, time_embed_dim=time_embed_dim) elif addition_embed_type is not None: raise ValueError(f"addition_embed_type: {addition_embed_type} must be None, 'text' or 'text_image'.") def _set_pos_net_if_use_gligen(self, attention_type: str, cross_attention_dim: int): if attention_type in ["gated", "gated-text-image"]: positive_len = 768 if isinstance(cross_attention_dim, int): positive_len = cross_attention_dim elif isinstance(cross_attention_dim, tuple) or isinstance(cross_attention_dim, list): positive_len = cross_attention_dim[0] feature_type = "text-only" if attention_type == "gated" else "text-image" self.position_net = GLIGENTextBoundingboxProjection( positive_len=positive_len, out_dim=cross_attention_dim, feature_type=feature_type ) @property def attn_processors(self) -> Dict[str, AttentionProcessor]: r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. """ # set recursively processors = {} def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): if hasattr(module, "get_processor"): processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): fn_recursive_add_processors(name, module, processors) return processors def set_attn_processor(self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]]): r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor for **all** `Attention` layers. If `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors. """ count = len(self.attn_processors.keys()) if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" f" number of attention layers: {count}. Please make sure to pass {count} processor classes." ) def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor) else: module.set_processor(processor.pop(f"{name}.processor")) for sub_name, child in module.named_children(): fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) def set_default_attn_processor(self): """ Disables custom attention processors and sets the default attention implementation. """ if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnAddedKVProcessor() elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnProcessor() else: raise ValueError( f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" ) self.set_attn_processor(processor) def set_attention_slice(self, slice_size: Union[str, int, List[int]] = "auto"): r""" Enable sliced attention computation. When this option is enabled, the attention module splits the input tensor in slices to compute attention in several steps. This is useful for saving some memory in exchange for a small decrease in speed. Args: slice_size (`str` or `int` or `list(int)`, *optional*, defaults to `"auto"`): When `"auto"`, input to the attention heads is halved, so attention is computed in two steps. If `"max"`, maximum amount of memory is saved by running only one slice at a time. If a number is provided, uses as many slices as `attention_head_dim // slice_size`. In this case, `attention_head_dim` must be a multiple of `slice_size`. """ sliceable_head_dims = [] def fn_recursive_retrieve_sliceable_dims(module: torch.nn.Module): if hasattr(module, "set_attention_slice"): sliceable_head_dims.append(module.sliceable_head_dim) for child in module.children(): fn_recursive_retrieve_sliceable_dims(child) # retrieve number of attention layers for module in self.children(): fn_recursive_retrieve_sliceable_dims(module) num_sliceable_layers = len(sliceable_head_dims) if slice_size == "auto": # half the attention head size is usually a good trade-off between # speed and memory slice_size = [dim // 2 for dim in sliceable_head_dims] elif slice_size == "max": # make smallest slice possible slice_size = num_sliceable_layers * [1] slice_size = num_sliceable_layers * [slice_size] if not isinstance(slice_size, list) else slice_size if len(slice_size) != len(sliceable_head_dims): raise ValueError( f"You have provided {len(slice_size)}, but {self.config} has {len(sliceable_head_dims)} different" f" attention layers. Make sure to match `len(slice_size)` to be {len(sliceable_head_dims)}." ) for i in range(len(slice_size)): size = slice_size[i] dim = sliceable_head_dims[i] if size is not None and size > dim: raise ValueError(f"size {size} has to be smaller or equal to {dim}.") # Recursively walk through all the children. # Any children which exposes the set_attention_slice method # gets the message def fn_recursive_set_attention_slice(module: torch.nn.Module, slice_size: List[int]): if hasattr(module, "set_attention_slice"): module.set_attention_slice(slice_size.pop()) for child in module.children(): fn_recursive_set_attention_slice(child, slice_size) reversed_slice_size = list(reversed(slice_size)) for module in self.children(): fn_recursive_set_attention_slice(module, reversed_slice_size) def _set_gradient_checkpointing(self, module, value=False): if hasattr(module, "gradient_checkpointing"): module.gradient_checkpointing = value def enable_freeu(self, s1: float, s2: float, b1: float, b2: float): r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. The suffixes after the scaling factors represent the stage blocks where they are being applied. Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. Args: s1 (`float`): Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process. s2 (`float`): Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process. b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. """ for i, upsample_block in enumerate(self.up_blocks): setattr(upsample_block, "s1", s1) setattr(upsample_block, "s2", s2) setattr(upsample_block, "b1", b1) setattr(upsample_block, "b2", b2) def disable_freeu(self): """Disables the FreeU mechanism.""" freeu_keys = {"s1", "s2", "b1", "b2"} for i, upsample_block in enumerate(self.up_blocks): for k in freeu_keys: if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None: setattr(upsample_block, k, None) def fuse_qkv_projections(self): """ Enables fused QKV projections. For self-attention modules, all projection matrices (i.e., query, key, value) are fused. For cross-attention modules, key and value projection matrices are fused. This API is 🧪 experimental. """ self.original_attn_processors = None for _, attn_processor in self.attn_processors.items(): if "Added" in str(attn_processor.__class__.__name__): raise ValueError("`fuse_qkv_projections()` is not supported for models having added KV projections.") self.original_attn_processors = self.attn_processors for module in self.modules(): if isinstance(module, Attention): module.fuse_projections(fuse=True) def unfuse_qkv_projections(self): """Disables the fused QKV projection if enabled. This API is 🧪 experimental. """ if self.original_attn_processors is not None: self.set_attn_processor(self.original_attn_processors) def unload_lora(self): """Unloads LoRA weights.""" deprecate( "unload_lora", "0.28.0", "Calling `unload_lora()` is deprecated and will be removed in a future version. Please install `peft` and then call `disable_adapters().", ) for module in self.modules(): if hasattr(module, "set_lora_layer"): module.set_lora_layer(None) def get_time_embed( self, sample: torch.Tensor, timestep: Union[torch.Tensor, float, int] ) -> Optional[torch.Tensor]: timesteps = timestep if not torch.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) is_mps = sample.device.type == "mps" if isinstance(timestep, float): dtype = torch.float32 if is_mps else torch.float64 else: dtype = torch.int32 if is_mps else torch.int64 timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps.expand(sample.shape[0]) t_emb = self.time_proj(timesteps) # `Timesteps` does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=sample.dtype) return t_emb def get_class_embed(self, sample: torch.Tensor, class_labels: Optional[torch.Tensor]) -> Optional[torch.Tensor]: class_emb = None if self.class_embedding is not None: if class_labels is None: raise ValueError("class_labels should be provided when num_class_embeds > 0") if self.config.class_embed_type == "timestep": class_labels = self.time_proj(class_labels) # `Timesteps` does not contain any weights and will always return f32 tensors # there might be better ways to encapsulate this. class_labels = class_labels.to(dtype=sample.dtype) class_emb = self.class_embedding(class_labels).to(dtype=sample.dtype) return class_emb def get_aug_embed( self, emb: torch.Tensor, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] ) -> Optional[torch.Tensor]: aug_emb = None if self.config.addition_embed_type == "text": aug_emb = self.add_embedding(encoder_hidden_states) elif self.config.addition_embed_type == "text_image": # Kandinsky 2.1 - style if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'text_image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" ) image_embs = added_cond_kwargs.get("image_embeds") text_embs = added_cond_kwargs.get("text_embeds", encoder_hidden_states) aug_emb = self.add_embedding(text_embs, image_embs) elif self.config.addition_embed_type == "text_time": # SDXL - style if "text_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `text_embeds` to be passed in `added_cond_kwargs`" ) text_embeds = added_cond_kwargs.get("text_embeds") if "time_ids" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'text_time' which requires the keyword argument `time_ids` to be passed in `added_cond_kwargs`" ) time_ids = added_cond_kwargs.get("time_ids") time_embeds = self.add_time_proj(time_ids.flatten()) time_embeds = time_embeds.reshape((text_embeds.shape[0], -1)) add_embeds = torch.concat([text_embeds, time_embeds], dim=-1) add_embeds = add_embeds.to(emb.dtype) aug_emb = self.add_embedding(add_embeds) elif self.config.addition_embed_type == "image": # Kandinsky 2.2 - style if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'image' which requires the keyword argument `image_embeds` to be passed in `added_cond_kwargs`" ) image_embs = added_cond_kwargs.get("image_embeds") aug_emb = self.add_embedding(image_embs) elif self.config.addition_embed_type == "image_hint": # Kandinsky 2.2 - style if "image_embeds" not in added_cond_kwargs or "hint" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `addition_embed_type` set to 'image_hint' which requires the keyword arguments `image_embeds` and `hint` to be passed in `added_cond_kwargs`" ) image_embs = added_cond_kwargs.get("image_embeds") hint = added_cond_kwargs.get("hint") aug_emb = self.add_embedding(image_embs, hint) return aug_emb def process_encoder_hidden_states( self, encoder_hidden_states: torch.Tensor, added_cond_kwargs: Dict[str, Any] ) -> torch.Tensor: if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_proj": encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states) elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "text_image_proj": # Kadinsky 2.1 - style if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'text_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" ) image_embeds = added_cond_kwargs.get("image_embeds") encoder_hidden_states = self.encoder_hid_proj(encoder_hidden_states, image_embeds) elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "image_proj": # Kandinsky 2.2 - style if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" ) image_embeds = added_cond_kwargs.get("image_embeds") encoder_hidden_states = self.encoder_hid_proj(image_embeds) elif self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" ) image_embeds = added_cond_kwargs.get("image_embeds") image_embeds = self.encoder_hid_proj(image_embeds) encoder_hidden_states = (encoder_hidden_states, image_embeds) return encoder_hidden_states def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, class_labels: Optional[torch.Tensor] = None, timestep_cond: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, mid_block_additional_residual: Optional[torch.Tensor] = None, down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, encoder_attention_mask: Optional[torch.Tensor] = None, return_dict: bool = True, down_block_add_samples: Optional[Tuple[torch.Tensor]] = None, mid_block_add_sample: Optional[Tuple[torch.Tensor]] = None, up_block_add_samples: Optional[Tuple[torch.Tensor]] = None, features_adapter: Optional[torch.Tensor] = None, ) -> Union[UNet2DConditionOutput, Tuple]: r""" The [`UNet2DConditionModel`] forward method. Args: sample (`torch.FloatTensor`): The noisy input tensor with the following shape `(batch, channel, height, width)`. timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. encoder_hidden_states (`torch.FloatTensor`): The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. class_labels (`torch.Tensor`, *optional*, defaults to `None`): Optional class labels for conditioning. Their embeddings will be summed with the timestep embeddings. timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed through the `self.time_embedding` layer to obtain the timestep embeddings. attention_mask (`torch.Tensor`, *optional*, defaults to `None`): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). added_cond_kwargs: (`dict`, *optional*): A kwargs dictionary containing additional embeddings that if specified are added to the embeddings that are passed along to the UNet blocks. down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): A tuple of tensors that if specified are added to the residuals of down unet blocks. mid_block_additional_residual: (`torch.Tensor`, *optional*): A tensor that if specified is added to the residual of the middle unet block. encoder_attention_mask (`torch.Tensor`): A cross-attention mask of shape `(batch, sequence_length)` is applied to `encoder_hidden_states`. If `True` the mask is kept, otherwise if `False` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] instead of a plain tuple. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the [`AttnProcessor`]. added_cond_kwargs: (`dict`, *optional*): A kwargs dictionary containin additional embeddings that if specified are added to the embeddings that are passed along to the UNet blocks. down_block_additional_residuals (`tuple` of `torch.Tensor`, *optional*): additional residuals to be added to UNet long skip connections from down blocks to up blocks for example from ControlNet side model(s) mid_block_additional_residual (`torch.Tensor`, *optional*): additional residual to be added to UNet mid block output, for example from ControlNet side model down_intrablock_additional_residuals (`tuple` of `torch.Tensor`, *optional*): additional residuals to be added within UNet down blocks, for example from T2I-Adapter side model(s) features_adapter (`torch.FloatTensor`, *optional*): (batch, channels, num_frames, height, width) adapter features tensor Returns: [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] or `tuple`: If `return_dict` is True, an [`~models.unets.unet_2d_condition.UNet2DConditionOutput`] is returned, otherwise a `tuple` is returned where the first element is the sample tensor. """ # By default samples have to be AT least a multiple of the overall upsampling factor. # The overall upsampling factor is equal to 2 ** (# num of upsampling layers). # However, the upsampling interpolation output size can be forced to fit any upsampling size # on the fly if necessary. default_overall_up_factor = 2**self.num_upsamplers # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` forward_upsample_size = False upsample_size = None for dim in sample.shape[-2:]: if dim % default_overall_up_factor != 0: # Forward upsample size to force interpolation output size. forward_upsample_size = True break # ensure attention_mask is a bias, and give it a singleton query_tokens dimension # expects mask of shape: # [batch, key_tokens] # adds singleton query_tokens dimension: # [batch, 1, key_tokens] # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes: # [batch, heads, query_tokens, key_tokens] (e.g. torch sdp attn) # [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn) if attention_mask is not None: # assume that mask is expressed as: # (1 = keep, 0 = discard) # convert mask into a bias that can be added to attention scores: # (keep = +0, discard = -10000.0) attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # convert encoder_attention_mask to a bias the same way we do for attention_mask if encoder_attention_mask is not None: encoder_attention_mask = (1 - encoder_attention_mask.to(sample.dtype)) * -10000.0 encoder_attention_mask = encoder_attention_mask.unsqueeze(1) # 0. center input if necessary if self.config.center_input_sample: sample = 2 * sample - 1.0 # 1. time t_emb = self.get_time_embed(sample=sample, timestep=timestep) emb = self.time_embedding(t_emb, timestep_cond) aug_emb = None class_emb = self.get_class_embed(sample=sample, class_labels=class_labels) if class_emb is not None: if self.config.class_embeddings_concat: emb = torch.cat([emb, class_emb], dim=-1) else: emb = emb + class_emb aug_emb = self.get_aug_embed( emb=emb, encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs ) if self.config.addition_embed_type == "image_hint": aug_emb, hint = aug_emb sample = torch.cat([sample, hint], dim=1) emb = emb + aug_emb if aug_emb is not None else emb if self.time_embed_act is not None: emb = self.time_embed_act(emb) encoder_hidden_states = self.process_encoder_hidden_states( encoder_hidden_states=encoder_hidden_states, added_cond_kwargs=added_cond_kwargs ) # 2. pre-process sample = self.conv_in(sample) # 2.5 GLIGEN position net if cross_attention_kwargs is not None and cross_attention_kwargs.get("gligen", None) is not None: cross_attention_kwargs = cross_attention_kwargs.copy() gligen_args = cross_attention_kwargs.pop("gligen") cross_attention_kwargs["gligen"] = {"objs": self.position_net(**gligen_args)} # 3. down lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 if USE_PEFT_BACKEND: # weight the lora layers by setting `lora_scale` for each PEFT layer scale_lora_layers(self, lora_scale) is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets is_adapter = down_intrablock_additional_residuals is not None # maintain backward compatibility for legacy usage, where # T2I-Adapter and ControlNet both use down_block_additional_residuals arg # but can only use one or the other is_brushnet = down_block_add_samples is not None and mid_block_add_sample is not None and up_block_add_samples is not None if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None: deprecate( "T2I should not use down_block_additional_residuals", "1.3.0", "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \ and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \ for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ", standard_warn=False, ) down_intrablock_additional_residuals = down_block_additional_residuals is_adapter = True down_block_res_samples = (sample,) if is_brushnet: sample = sample + down_block_add_samples.pop(0) adapter_idx = 0 for downsample_block in self.down_blocks: if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: # For t2i-adapter CrossAttnDownBlock2D additional_residuals = {} if is_adapter and len(down_intrablock_additional_residuals) > 0: additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0) if is_brushnet and len(down_block_add_samples)>0: additional_residuals["down_block_add_samples"] = [down_block_add_samples.pop(0) for _ in range(len(downsample_block.resnets)+(downsample_block.downsamplers !=None))] sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, encoder_attention_mask=encoder_attention_mask, **additional_residuals, ) else: additional_residuals = {} if is_brushnet and len(down_block_add_samples)>0: additional_residuals["down_block_add_samples"] = [down_block_add_samples.pop(0) for _ in range(len(downsample_block.resnets)+(downsample_block.downsamplers !=None))] sample, res_samples = downsample_block(hidden_states=sample, temb=emb, scale=lora_scale, **additional_residuals) if is_adapter and len(down_intrablock_additional_residuals) > 0: sample += down_intrablock_additional_residuals.pop(0) if features_adapter is not None: sample += features_adapter[adapter_idx] adapter_idx += 1 down_block_res_samples += res_samples if features_adapter is not None: assert len(features_adapter) == adapter_idx, "Wrong features_adapter" if is_controlnet: new_down_block_res_samples = () for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): down_block_res_sample = down_block_res_sample + down_block_additional_residual new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples # 4. mid if self.mid_block is not None: if hasattr(self.mid_block, "has_cross_attention") and self.mid_block.has_cross_attention: sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, encoder_attention_mask=encoder_attention_mask, ) else: sample = self.mid_block(sample, emb) # To support T2I-Adapter-XL if ( is_adapter and len(down_intrablock_additional_residuals) > 0 and sample.shape == down_intrablock_additional_residuals[0].shape ): sample += down_intrablock_additional_residuals.pop(0) if is_controlnet: sample = sample + mid_block_additional_residual if is_brushnet: sample = sample + mid_block_add_sample # 5. up for i, upsample_block in enumerate(self.up_blocks): is_final_block = i == len(self.up_blocks) - 1 res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # if we have not reached the final block and need to forward the # upsample size, we do it here if not is_final_block and forward_upsample_size: upsample_size = down_block_res_samples[-1].shape[2:] if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: additional_residuals = {} if is_brushnet and len(up_block_add_samples)>0: additional_residuals["up_block_add_samples"] = [up_block_add_samples.pop(0) for _ in range(len(upsample_block.resnets)+(upsample_block.upsamplers !=None))] sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, upsample_size=upsample_size, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, **additional_residuals, ) else: additional_residuals = {} if is_brushnet and len(up_block_add_samples)>0: additional_residuals["up_block_add_samples"] = [up_block_add_samples.pop(0) for _ in range(len(upsample_block.resnets)+(upsample_block.upsamplers !=None))] sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, scale=lora_scale, **additional_residuals, ) # 6. post-process if self.conv_norm_out: sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) if USE_PEFT_BACKEND: # remove `lora_scale` from each PEFT layer unscale_lora_layers(self, lora_scale) if not return_dict: return (sample,) return UNet2DConditionOutput(sample=sample) ================================================ FILE: libs/unet_3d_blocks.py ================================================ # Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Dict, Optional, Tuple, Union import torch from torch import nn from diffusers.utils import is_torch_version from diffusers.utils.torch_utils import apply_freeu from diffusers.models.attention import Attention from diffusers.models.transformers.dual_transformer_2d import DualTransformer2DModel from diffusers.models.resnet import ( Downsample2D, ResnetBlock2D, SpatioTemporalResBlock, TemporalConvLayer, Upsample2D, ) from diffusers.models.transformers.transformer_2d import Transformer2DModel from diffusers.models.transformers.transformer_temporal import ( TransformerSpatioTemporalModel, ) from .transformer_temporal import TransformerTemporalModel def get_down_block( down_block_type: str, num_layers: int, in_channels: int, out_channels: int, temb_channels: int, add_downsample: bool, resnet_eps: float, resnet_act_fn: str, num_attention_heads: int, resnet_groups: Optional[int] = None, cross_attention_dim: Optional[int] = None, downsample_padding: Optional[int] = None, dual_cross_attention: bool = False, use_linear_projection: bool = True, only_cross_attention: bool = False, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", temporal_num_attention_heads: int = 8, temporal_max_seq_length: int = 32, transformer_layers_per_block: int = 1, ) -> Union[ "DownBlock3D", "CrossAttnDownBlock3D", "DownBlockMotion", "CrossAttnDownBlockMotion", "DownBlockSpatioTemporal", "CrossAttnDownBlockSpatioTemporal", ]: if down_block_type == "DownBlock3D": return DownBlock3D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, ) elif down_block_type == "CrossAttnDownBlock3D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlock3D") return CrossAttnDownBlock3D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, ) if down_block_type == "DownBlockMotion": return DownBlockMotion( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, resnet_time_scale_shift=resnet_time_scale_shift, temporal_num_attention_heads=temporal_num_attention_heads, temporal_max_seq_length=temporal_max_seq_length, ) elif down_block_type == "CrossAttnDownBlockMotion": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockMotion") return CrossAttnDownBlockMotion( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, downsample_padding=downsample_padding, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, temporal_num_attention_heads=temporal_num_attention_heads, temporal_max_seq_length=temporal_max_seq_length, ) elif down_block_type == "DownBlockSpatioTemporal": # added for SDV return DownBlockSpatioTemporal( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, add_downsample=add_downsample, ) elif down_block_type == "CrossAttnDownBlockSpatioTemporal": # added for SDV if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnDownBlockSpatioTemporal") return CrossAttnDownBlockSpatioTemporal( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, num_layers=num_layers, transformer_layers_per_block=transformer_layers_per_block, add_downsample=add_downsample, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, ) raise ValueError(f"{down_block_type} does not exist.") def get_up_block( up_block_type: str, num_layers: int, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, add_upsample: bool, resnet_eps: float, resnet_act_fn: str, num_attention_heads: int, resolution_idx: Optional[int] = None, resnet_groups: Optional[int] = None, cross_attention_dim: Optional[int] = None, dual_cross_attention: bool = False, use_linear_projection: bool = True, only_cross_attention: bool = False, upcast_attention: bool = False, resnet_time_scale_shift: str = "default", temporal_num_attention_heads: int = 8, temporal_cross_attention_dim: Optional[int] = None, temporal_max_seq_length: int = 32, transformer_layers_per_block: int = 1, dropout: float = 0.0, ) -> Union[ "UpBlock3D", "CrossAttnUpBlock3D", "UpBlockMotion", "CrossAttnUpBlockMotion", "UpBlockSpatioTemporal", "CrossAttnUpBlockSpatioTemporal", ]: if up_block_type == "UpBlock3D": return UpBlock3D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, resolution_idx=resolution_idx, ) elif up_block_type == "CrossAttnUpBlock3D": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlock3D") return CrossAttnUpBlock3D( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, resolution_idx=resolution_idx, ) if up_block_type == "UpBlockMotion": return UpBlockMotion( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, resnet_time_scale_shift=resnet_time_scale_shift, resolution_idx=resolution_idx, temporal_num_attention_heads=temporal_num_attention_heads, temporal_max_seq_length=temporal_max_seq_length, ) elif up_block_type == "CrossAttnUpBlockMotion": if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockMotion") return CrossAttnUpBlockMotion( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, add_upsample=add_upsample, resnet_eps=resnet_eps, resnet_act_fn=resnet_act_fn, resnet_groups=resnet_groups, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, dual_cross_attention=dual_cross_attention, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, resnet_time_scale_shift=resnet_time_scale_shift, resolution_idx=resolution_idx, temporal_num_attention_heads=temporal_num_attention_heads, temporal_max_seq_length=temporal_max_seq_length, ) elif up_block_type == "UpBlockSpatioTemporal": # added for SDV return UpBlockSpatioTemporal( num_layers=num_layers, in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, resolution_idx=resolution_idx, add_upsample=add_upsample, ) elif up_block_type == "CrossAttnUpBlockSpatioTemporal": # added for SDV if cross_attention_dim is None: raise ValueError("cross_attention_dim must be specified for CrossAttnUpBlockSpatioTemporal") return CrossAttnUpBlockSpatioTemporal( in_channels=in_channels, out_channels=out_channels, prev_output_channel=prev_output_channel, temb_channels=temb_channels, num_layers=num_layers, transformer_layers_per_block=transformer_layers_per_block, add_upsample=add_upsample, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads, resolution_idx=resolution_idx, ) raise ValueError(f"{up_block_type} does not exist.") class UNetMidBlock3DCrossAttn(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, num_attention_heads: int = 1, output_scale_factor: float = 1.0, cross_attention_dim: int = 1280, dual_cross_attention: bool = False, use_linear_projection: bool = True, upcast_attention: bool = False, ): super().__init__() self.has_cross_attention = True self.num_attention_heads = num_attention_heads resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) # there is always at least one resnet resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ] temp_convs = [ TemporalConvLayer( in_channels, in_channels, dropout=0.1, norm_num_groups=resnet_groups, ) ] attentions = [] temp_attentions = [] for _ in range(num_layers): attentions.append( Transformer2DModel( in_channels // num_attention_heads, num_attention_heads, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, ) ) temp_attentions.append( TransformerTemporalModel( in_channels // num_attention_heads, num_attention_heads, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) temp_convs.append( TemporalConvLayer( in_channels, in_channels, dropout=0.1, norm_num_groups=resnet_groups, ) ) self.resnets = nn.ModuleList(resnets) self.temp_convs = nn.ModuleList(temp_convs) self.attentions = nn.ModuleList(attentions) self.temp_attentions = nn.ModuleList(temp_attentions) def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, num_frames: int = 1, cross_attention_kwargs: Optional[Dict[str, Any]] = None, ) -> torch.FloatTensor: hidden_states = self.resnets[0](hidden_states, temb) hidden_states = self.temp_convs[0](hidden_states, num_frames=num_frames) for attn, temp_attn, resnet, temp_conv in zip( self.attentions, self.temp_attentions, self.resnets[1:], self.temp_convs[1:] ): hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] hidden_states = temp_attn( hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] hidden_states = resnet(hidden_states, temb) hidden_states = temp_conv(hidden_states, num_frames=num_frames) return hidden_states class CrossAttnDownBlock3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, num_attention_heads: int = 1, cross_attention_dim: int = 1280, output_scale_factor: float = 1.0, downsample_padding: int = 1, add_downsample: bool = True, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, ): super().__init__() resnets = [] attentions = [] temp_attentions = [] temp_convs = [] self.has_cross_attention = True self.num_attention_heads = num_attention_heads for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) temp_convs.append( TemporalConvLayer( out_channels, out_channels, dropout=0.1, norm_num_groups=resnet_groups, ) ) attentions.append( Transformer2DModel( out_channels // num_attention_heads, num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, ) ) temp_attentions.append( TransformerTemporalModel( out_channels // num_attention_heads, num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.resnets = nn.ModuleList(resnets) self.temp_convs = nn.ModuleList(temp_convs) self.attentions = nn.ModuleList(attentions) self.temp_attentions = nn.ModuleList(temp_attentions) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op", ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, num_frames: int = 1, cross_attention_kwargs: Dict[str, Any] = None, ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: # TODO(Patrick, William) - attention mask is not used output_states = () for resnet, temp_conv, attn, temp_attn in zip( self.resnets, self.temp_convs, self.attentions, self.temp_attentions ): hidden_states = resnet(hidden_states, temb) hidden_states = temp_conv(hidden_states, num_frames=num_frames) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] hidden_states = temp_attn( hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states += (hidden_states,) return hidden_states, output_states class DownBlock3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, add_downsample: bool = True, downsample_padding: int = 1, ): super().__init__() resnets = [] temp_convs = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) temp_convs.append( TemporalConvLayer( out_channels, out_channels, dropout=0.1, norm_num_groups=resnet_groups, ) ) self.resnets = nn.ModuleList(resnets) self.temp_convs = nn.ModuleList(temp_convs) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op", ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, num_frames: int = 1, ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: output_states = () for resnet, temp_conv in zip(self.resnets, self.temp_convs): hidden_states = resnet(hidden_states, temb) hidden_states = temp_conv(hidden_states, num_frames=num_frames) output_states += (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states += (hidden_states,) return hidden_states, output_states class CrossAttnUpBlock3D(nn.Module): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, num_attention_heads: int = 1, cross_attention_dim: int = 1280, output_scale_factor: float = 1.0, add_upsample: bool = True, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, resolution_idx: Optional[int] = None, ): super().__init__() resnets = [] temp_convs = [] attentions = [] temp_attentions = [] self.has_cross_attention = True self.num_attention_heads = num_attention_heads for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) temp_convs.append( TemporalConvLayer( out_channels, out_channels, dropout=0.1, norm_num_groups=resnet_groups, ) ) attentions.append( Transformer2DModel( out_channels // num_attention_heads, num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, ) ) temp_attentions.append( TransformerTemporalModel( out_channels // num_attention_heads, num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) self.resnets = nn.ModuleList(resnets) self.temp_convs = nn.ModuleList(temp_convs) self.attentions = nn.ModuleList(attentions) self.temp_attentions = nn.ModuleList(temp_attentions) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, upsample_size: Optional[int] = None, attention_mask: Optional[torch.FloatTensor] = None, num_frames: int = 1, cross_attention_kwargs: Dict[str, Any] = None, ) -> torch.FloatTensor: is_freeu_enabled = ( getattr(self, "s1", None) and getattr(self, "s2", None) and getattr(self, "b1", None) and getattr(self, "b2", None) ) # TODO(Patrick, William) - attention mask is not used for resnet, temp_conv, attn, temp_attn in zip( self.resnets, self.temp_convs, self.attentions, self.temp_attentions ): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] # FreeU: Only operate on the first two stages if is_freeu_enabled: hidden_states, res_hidden_states = apply_freeu( self.resolution_idx, hidden_states, res_hidden_states, s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2, ) hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) hidden_states = resnet(hidden_states, temb) hidden_states = temp_conv(hidden_states, num_frames=num_frames) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] hidden_states = temp_attn( hidden_states, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, return_dict=False, )[0] if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states class UpBlock3D(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, add_upsample: bool = True, resolution_idx: Optional[int] = None, ): super().__init__() resnets = [] temp_convs = [] for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) temp_convs.append( TemporalConvLayer( out_channels, out_channels, dropout=0.1, norm_num_groups=resnet_groups, ) ) self.resnets = nn.ModuleList(resnets) self.temp_convs = nn.ModuleList(temp_convs) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, upsample_size: Optional[int] = None, num_frames: int = 1, ) -> torch.FloatTensor: is_freeu_enabled = ( getattr(self, "s1", None) and getattr(self, "s2", None) and getattr(self, "b1", None) and getattr(self, "b2", None) ) for resnet, temp_conv in zip(self.resnets, self.temp_convs): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] # FreeU: Only operate on the first two stages if is_freeu_enabled: hidden_states, res_hidden_states = apply_freeu( self.resolution_idx, hidden_states, res_hidden_states, s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2, ) hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) hidden_states = resnet(hidden_states, temb) hidden_states = temp_conv(hidden_states, num_frames=num_frames) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size) return hidden_states class DownBlockMotion(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, add_downsample: bool = True, downsample_padding: int = 1, temporal_num_attention_heads: int = 1, temporal_cross_attention_dim: Optional[int] = None, temporal_max_seq_length: int = 32, ): super().__init__() resnets = [] motion_modules = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) motion_modules.append( TransformerTemporalModel( num_attention_heads=temporal_num_attention_heads, in_channels=out_channels, norm_num_groups=resnet_groups, cross_attention_dim=temporal_cross_attention_dim, attention_bias=False, activation_fn="geglu", positional_embeddings="sinusoidal", num_positional_embeddings=temporal_max_seq_length, attention_head_dim=out_channels // temporal_num_attention_heads, ) ) self.resnets = nn.ModuleList(resnets) self.motion_modules = nn.ModuleList(motion_modules) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op", ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, down_block_add_samples: Optional[torch.FloatTensor] = None, temb: Optional[torch.FloatTensor] = None, scale: float = 1.0, num_frames: int = 1, ) -> Union[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: output_states = () blocks = zip(self.resnets, self.motion_modules) for resnet, motion_module in blocks: if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) if down_block_add_samples is not None: hidden_states = hidden_states + down_block_add_samples.pop(0) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, num_frames, **ckpt_kwargs, ) else: hidden_states = resnet(hidden_states, temb, scale=scale) if down_block_add_samples is not None: hidden_states = hidden_states + down_block_add_samples.pop(0) hidden_states = motion_module(hidden_states, num_frames=num_frames) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states, scale=scale) if down_block_add_samples is not None: hidden_states = hidden_states + down_block_add_samples.pop(0) output_states = output_states + (hidden_states,) return hidden_states, output_states class CrossAttnDownBlockMotion(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, transformer_layers_per_block: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, num_attention_heads: int = 1, cross_attention_dim: int = 1280, output_scale_factor: float = 1.0, downsample_padding: int = 1, add_downsample: bool = True, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, attention_type: str = "default", temporal_cross_attention_dim: Optional[int] = None, temporal_num_attention_heads: int = 8, temporal_max_seq_length: int = 32, ): super().__init__() resnets = [] attentions = [] motion_modules = [] self.has_cross_attention = True self.num_attention_heads = num_attention_heads for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if not dual_cross_attention: attentions.append( Transformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=transformer_layers_per_block, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, attention_type=attention_type, ) ) else: attentions.append( DualTransformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) motion_modules.append( TransformerTemporalModel( num_attention_heads=temporal_num_attention_heads, in_channels=out_channels, norm_num_groups=resnet_groups, cross_attention_dim=temporal_cross_attention_dim, attention_bias=False, activation_fn="geglu", positional_embeddings="sinusoidal", num_positional_embeddings=temporal_max_seq_length, attention_head_dim=out_channels // temporal_num_attention_heads, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) self.motion_modules = nn.ModuleList(motion_modules) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=downsample_padding, name="op", ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, num_frames: int = 1, encoder_attention_mask: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, additional_residuals: Optional[torch.FloatTensor] = None, down_block_add_samples: Optional[torch.FloatTensor] = None, ): output_states = () lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 blocks = list(zip(self.resnets, self.attentions, self.motion_modules)) for i, (resnet, attn, motion_module) in enumerate(blocks): if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] # apply additional residuals to the output of the last pair of resnet and attention blocks if i == len(blocks) - 1 and additional_residuals is not None: hidden_states = hidden_states + additional_residuals if down_block_add_samples is not None: hidden_states = hidden_states + down_block_add_samples.pop(0) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, num_frames, **ckpt_kwargs, ) else: hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] # apply additional residuals to the output of the last pair of resnet and attention blocks if i == len(blocks) - 1 and additional_residuals is not None: hidden_states = hidden_states + additional_residuals if down_block_add_samples is not None: hidden_states = hidden_states + down_block_add_samples.pop(0) hidden_states = motion_module( hidden_states, num_frames=num_frames, ) # # apply additional residuals to the output of the last pair of resnet and attention blocks # if i == len(blocks) - 1 and additional_residuals is not None: # hidden_states = hidden_states + additional_residuals output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states, scale=lora_scale) if down_block_add_samples is not None: hidden_states = hidden_states + down_block_add_samples.pop(0) output_states = output_states + (hidden_states,) return hidden_states, output_states class CrossAttnUpBlockMotion(nn.Module): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, transformer_layers_per_block: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, num_attention_heads: int = 1, cross_attention_dim: int = 1280, output_scale_factor: float = 1.0, add_upsample: bool = True, dual_cross_attention: bool = False, use_linear_projection: bool = False, only_cross_attention: bool = False, upcast_attention: bool = False, attention_type: str = "default", temporal_cross_attention_dim: Optional[int] = None, temporal_num_attention_heads: int = 8, temporal_max_seq_length: int = 32, ): super().__init__() resnets = [] attentions = [] motion_modules = [] self.has_cross_attention = True self.num_attention_heads = num_attention_heads for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) if not dual_cross_attention: attentions.append( Transformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=transformer_layers_per_block, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, only_cross_attention=only_cross_attention, upcast_attention=upcast_attention, attention_type=attention_type, ) ) else: attentions.append( DualTransformer2DModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) motion_modules.append( TransformerTemporalModel( num_attention_heads=temporal_num_attention_heads, in_channels=out_channels, norm_num_groups=resnet_groups, cross_attention_dim=temporal_cross_attention_dim, attention_bias=False, activation_fn="geglu", positional_embeddings="sinusoidal", num_positional_embeddings=temporal_max_seq_length, attention_head_dim=out_channels // temporal_num_attention_heads, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) self.motion_modules = nn.ModuleList(motion_modules) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, upsample_size: Optional[int] = None, attention_mask: Optional[torch.FloatTensor] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, num_frames: int = 1, up_block_add_samples: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 is_freeu_enabled = ( getattr(self, "s1", None) and getattr(self, "s2", None) and getattr(self, "b1", None) and getattr(self, "b2", None) ) blocks = zip(self.resnets, self.attentions, self.motion_modules) for resnet, attn, motion_module in blocks: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] # FreeU: Only operate on the first two stages if is_freeu_enabled: hidden_states, res_hidden_states = apply_freeu( self.resolution_idx, hidden_states, res_hidden_states, s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2, ) hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] if up_block_add_samples is not None: hidden_states = hidden_states + up_block_add_samples.pop(0) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, num_frames, **ckpt_kwargs, ) else: hidden_states = resnet(hidden_states, temb, scale=lora_scale) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] if up_block_add_samples is not None: hidden_states = hidden_states + up_block_add_samples.pop(0) hidden_states = motion_module( hidden_states, num_frames=num_frames, ) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size, scale=lora_scale) if up_block_add_samples is not None: hidden_states = hidden_states + up_block_add_samples.pop(0) return hidden_states class UpBlockMotion(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, resolution_idx: Optional[int] = None, dropout: float = 0.0, num_layers: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, output_scale_factor: float = 1.0, add_upsample: bool = True, temporal_norm_num_groups: int = 32, temporal_cross_attention_dim: Optional[int] = None, temporal_num_attention_heads: int = 8, temporal_max_seq_length: int = 32, ): super().__init__() resnets = [] motion_modules = [] for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( ResnetBlock2D( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) motion_modules.append( TransformerTemporalModel( num_attention_heads=temporal_num_attention_heads, in_channels=out_channels, norm_num_groups=temporal_norm_num_groups, cross_attention_dim=temporal_cross_attention_dim, attention_bias=False, activation_fn="geglu", positional_embeddings="sinusoidal", num_positional_embeddings=temporal_max_seq_length, attention_head_dim=out_channels // temporal_num_attention_heads, ) ) self.resnets = nn.ModuleList(resnets) self.motion_modules = nn.ModuleList(motion_modules) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, upsample_size=None, scale: float = 1.0, num_frames: int = 1, up_block_add_samples: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: is_freeu_enabled = ( getattr(self, "s1", None) and getattr(self, "s2", None) and getattr(self, "b1", None) and getattr(self, "b2", None) ) blocks = zip(self.resnets, self.motion_modules) for resnet, motion_module in blocks: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] # FreeU: Only operate on the first two stages if is_freeu_enabled: hidden_states, res_hidden_states = apply_freeu( self.resolution_idx, hidden_states, res_hidden_states, s1=self.s1, s2=self.s2, b1=self.b1, b2=self.b2, ) hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, use_reentrant=False, ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb ) if up_block_add_samples is not None: hidden_states = hidden_states + up_block_add_samples.pop(0) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, num_frames, use_reentrant=False, ) else: hidden_states = resnet(hidden_states, temb, scale=scale) if up_block_add_samples is not None: hidden_states = hidden_states + up_block_add_samples.pop(0) hidden_states = motion_module(hidden_states, num_frames=num_frames) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states, upsample_size, scale=scale) if up_block_add_samples is not None: hidden_states = hidden_states + up_block_add_samples.pop(0) return hidden_states class UNetMidBlockCrossAttnMotion(nn.Module): def __init__( self, in_channels: int, temb_channels: int, dropout: float = 0.0, num_layers: int = 1, transformer_layers_per_block: int = 1, resnet_eps: float = 1e-6, resnet_time_scale_shift: str = "default", resnet_act_fn: str = "swish", resnet_groups: int = 32, resnet_pre_norm: bool = True, num_attention_heads: int = 1, output_scale_factor: float = 1.0, cross_attention_dim: int = 1280, dual_cross_attention: float = False, use_linear_projection: float = False, upcast_attention: float = False, attention_type: str = "default", temporal_num_attention_heads: int = 1, temporal_cross_attention_dim: Optional[int] = None, temporal_max_seq_length: int = 32, ): super().__init__() self.has_cross_attention = True self.num_attention_heads = num_attention_heads resnet_groups = resnet_groups if resnet_groups is not None else min(in_channels // 4, 32) # there is always at least one resnet resnets = [ ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ] attentions = [] motion_modules = [] for _ in range(num_layers): if not dual_cross_attention: attentions.append( Transformer2DModel( num_attention_heads, in_channels // num_attention_heads, in_channels=in_channels, num_layers=transformer_layers_per_block, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, use_linear_projection=use_linear_projection, upcast_attention=upcast_attention, attention_type=attention_type, ) ) else: attentions.append( DualTransformer2DModel( num_attention_heads, in_channels // num_attention_heads, in_channels=in_channels, num_layers=1, cross_attention_dim=cross_attention_dim, norm_num_groups=resnet_groups, ) ) resnets.append( ResnetBlock2D( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=resnet_eps, groups=resnet_groups, dropout=dropout, time_embedding_norm=resnet_time_scale_shift, non_linearity=resnet_act_fn, output_scale_factor=output_scale_factor, pre_norm=resnet_pre_norm, ) ) motion_modules.append( TransformerTemporalModel( num_attention_heads=temporal_num_attention_heads, attention_head_dim=in_channels // temporal_num_attention_heads, in_channels=in_channels, norm_num_groups=resnet_groups, cross_attention_dim=temporal_cross_attention_dim, attention_bias=False, positional_embeddings="sinusoidal", num_positional_embeddings=temporal_max_seq_length, activation_fn="geglu", ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) self.motion_modules = nn.ModuleList(motion_modules) self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, attention_mask: Optional[torch.FloatTensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, encoder_attention_mask: Optional[torch.FloatTensor] = None, num_frames: int = 1, mid_block_add_sample: Optional[Tuple[torch.Tensor]] = None, ) -> torch.FloatTensor: lora_scale = cross_attention_kwargs.get("scale", 1.0) if cross_attention_kwargs is not None else 1.0 hidden_states = self.resnets[0](hidden_states, temb, scale=lora_scale) blocks = zip(self.attentions, self.resnets[1:], self.motion_modules) for attn, resnet, motion_module in blocks: if self.training and self.gradient_checkpointing: def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] ########## hidden_states = resnet(hidden_states, temb, scale=lora_scale) if mid_block_add_sample is not None: hidden_states = hidden_states + mid_block_add_sample ################################################################ hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(motion_module), hidden_states.requires_grad_(), temb, num_frames, **ckpt_kwargs, ) hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, **ckpt_kwargs, ) else: hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, cross_attention_kwargs=cross_attention_kwargs, attention_mask=attention_mask, encoder_attention_mask=encoder_attention_mask, return_dict=False, )[0] ########## hidden_states = resnet(hidden_states, temb, scale=lora_scale) if mid_block_add_sample is not None: hidden_states = hidden_states + mid_block_add_sample ################################################################ hidden_states = motion_module( hidden_states, num_frames=num_frames, ) hidden_states = resnet(hidden_states, temb, scale=lora_scale) return hidden_states class MidBlockTemporalDecoder(nn.Module): def __init__( self, in_channels: int, out_channels: int, attention_head_dim: int = 512, num_layers: int = 1, upcast_attention: bool = False, ): super().__init__() resnets = [] attentions = [] for i in range(num_layers): input_channels = in_channels if i == 0 else out_channels resnets.append( SpatioTemporalResBlock( in_channels=input_channels, out_channels=out_channels, temb_channels=None, eps=1e-6, temporal_eps=1e-5, merge_factor=0.0, merge_strategy="learned", switch_spatial_to_temporal_mix=True, ) ) attentions.append( Attention( query_dim=in_channels, heads=in_channels // attention_head_dim, dim_head=attention_head_dim, eps=1e-6, upcast_attention=upcast_attention, norm_num_groups=32, bias=True, residual_connection=True, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) def forward( self, hidden_states: torch.FloatTensor, image_only_indicator: torch.FloatTensor, ): hidden_states = self.resnets[0]( hidden_states, image_only_indicator=image_only_indicator, ) for resnet, attn in zip(self.resnets[1:], self.attentions): hidden_states = attn(hidden_states) hidden_states = resnet( hidden_states, image_only_indicator=image_only_indicator, ) return hidden_states class UpBlockTemporalDecoder(nn.Module): def __init__( self, in_channels: int, out_channels: int, num_layers: int = 1, add_upsample: bool = True, ): super().__init__() resnets = [] for i in range(num_layers): input_channels = in_channels if i == 0 else out_channels resnets.append( SpatioTemporalResBlock( in_channels=input_channels, out_channels=out_channels, temb_channels=None, eps=1e-6, temporal_eps=1e-5, merge_factor=0.0, merge_strategy="learned", switch_spatial_to_temporal_mix=True, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None def forward( self, hidden_states: torch.FloatTensor, image_only_indicator: torch.FloatTensor, ) -> torch.FloatTensor: for resnet in self.resnets: hidden_states = resnet( hidden_states, image_only_indicator=image_only_indicator, ) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states class UNetMidBlockSpatioTemporal(nn.Module): def __init__( self, in_channels: int, temb_channels: int, num_layers: int = 1, transformer_layers_per_block: Union[int, Tuple[int]] = 1, num_attention_heads: int = 1, cross_attention_dim: int = 1280, ): super().__init__() self.has_cross_attention = True self.num_attention_heads = num_attention_heads # support for variable transformer layers per block if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * num_layers # there is always at least one resnet resnets = [ SpatioTemporalResBlock( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=1e-5, ) ] attentions = [] for i in range(num_layers): attentions.append( TransformerSpatioTemporalModel( num_attention_heads, in_channels // num_attention_heads, in_channels=in_channels, num_layers=transformer_layers_per_block[i], cross_attention_dim=cross_attention_dim, ) ) resnets.append( SpatioTemporalResBlock( in_channels=in_channels, out_channels=in_channels, temb_channels=temb_channels, eps=1e-5, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, image_only_indicator: Optional[torch.Tensor] = None, ) -> torch.FloatTensor: hidden_states = self.resnets[0]( hidden_states, temb, image_only_indicator=image_only_indicator, ) for attn, resnet in zip(self.attentions, self.resnets[1:]): if self.training and self.gradient_checkpointing: # TODO def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, image_only_indicator=image_only_indicator, return_dict=False, )[0] hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, image_only_indicator, **ckpt_kwargs, ) else: hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, image_only_indicator=image_only_indicator, return_dict=False, )[0] hidden_states = resnet( hidden_states, temb, image_only_indicator=image_only_indicator, ) return hidden_states class DownBlockSpatioTemporal(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, num_layers: int = 1, add_downsample: bool = True, ): super().__init__() resnets = [] for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( SpatioTemporalResBlock( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=1e-5, ) ) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, name="op", ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, image_only_indicator: Optional[torch.Tensor] = None, ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: output_states = () for resnet in self.resnets: if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, image_only_indicator, use_reentrant=False, ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, image_only_indicator, ) else: hidden_states = resnet( hidden_states, temb, image_only_indicator=image_only_indicator, ) output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states = output_states + (hidden_states,) return hidden_states, output_states class CrossAttnDownBlockSpatioTemporal(nn.Module): def __init__( self, in_channels: int, out_channels: int, temb_channels: int, num_layers: int = 1, transformer_layers_per_block: Union[int, Tuple[int]] = 1, num_attention_heads: int = 1, cross_attention_dim: int = 1280, add_downsample: bool = True, ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.num_attention_heads = num_attention_heads if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * num_layers for i in range(num_layers): in_channels = in_channels if i == 0 else out_channels resnets.append( SpatioTemporalResBlock( in_channels=in_channels, out_channels=out_channels, temb_channels=temb_channels, eps=1e-6, ) ) attentions.append( TransformerSpatioTemporalModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=transformer_layers_per_block[i], cross_attention_dim=cross_attention_dim, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_downsample: self.downsamplers = nn.ModuleList( [ Downsample2D( out_channels, use_conv=True, out_channels=out_channels, padding=1, name="op", ) ] ) else: self.downsamplers = None self.gradient_checkpointing = False def forward( self, hidden_states: torch.FloatTensor, temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, image_only_indicator: Optional[torch.Tensor] = None, ) -> Tuple[torch.FloatTensor, Tuple[torch.FloatTensor, ...]]: output_states = () blocks = list(zip(self.resnets, self.attentions)) for resnet, attn in blocks: if self.training and self.gradient_checkpointing: # TODO def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, image_only_indicator, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, image_only_indicator=image_only_indicator, return_dict=False, )[0] else: hidden_states = resnet( hidden_states, temb, image_only_indicator=image_only_indicator, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, image_only_indicator=image_only_indicator, return_dict=False, )[0] output_states = output_states + (hidden_states,) if self.downsamplers is not None: for downsampler in self.downsamplers: hidden_states = downsampler(hidden_states) output_states = output_states + (hidden_states,) return hidden_states, output_states class UpBlockSpatioTemporal(nn.Module): def __init__( self, in_channels: int, prev_output_channel: int, out_channels: int, temb_channels: int, resolution_idx: Optional[int] = None, num_layers: int = 1, resnet_eps: float = 1e-6, add_upsample: bool = True, ): super().__init__() resnets = [] for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( SpatioTemporalResBlock( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, ) ) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, image_only_indicator: Optional[torch.Tensor] = None, ) -> torch.FloatTensor: for resnet in self.resnets: # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: def create_custom_forward(module): def custom_forward(*inputs): return module(*inputs) return custom_forward if is_torch_version(">=", "1.11.0"): hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, image_only_indicator, use_reentrant=False, ) else: hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, image_only_indicator, ) else: hidden_states = resnet( hidden_states, temb, image_only_indicator=image_only_indicator, ) if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states class CrossAttnUpBlockSpatioTemporal(nn.Module): def __init__( self, in_channels: int, out_channels: int, prev_output_channel: int, temb_channels: int, resolution_idx: Optional[int] = None, num_layers: int = 1, transformer_layers_per_block: Union[int, Tuple[int]] = 1, resnet_eps: float = 1e-6, num_attention_heads: int = 1, cross_attention_dim: int = 1280, add_upsample: bool = True, ): super().__init__() resnets = [] attentions = [] self.has_cross_attention = True self.num_attention_heads = num_attention_heads if isinstance(transformer_layers_per_block, int): transformer_layers_per_block = [transformer_layers_per_block] * num_layers for i in range(num_layers): res_skip_channels = in_channels if (i == num_layers - 1) else out_channels resnet_in_channels = prev_output_channel if i == 0 else out_channels resnets.append( SpatioTemporalResBlock( in_channels=resnet_in_channels + res_skip_channels, out_channels=out_channels, temb_channels=temb_channels, eps=resnet_eps, ) ) attentions.append( TransformerSpatioTemporalModel( num_attention_heads, out_channels // num_attention_heads, in_channels=out_channels, num_layers=transformer_layers_per_block[i], cross_attention_dim=cross_attention_dim, ) ) self.attentions = nn.ModuleList(attentions) self.resnets = nn.ModuleList(resnets) if add_upsample: self.upsamplers = nn.ModuleList([Upsample2D(out_channels, use_conv=True, out_channels=out_channels)]) else: self.upsamplers = None self.gradient_checkpointing = False self.resolution_idx = resolution_idx def forward( self, hidden_states: torch.FloatTensor, res_hidden_states_tuple: Tuple[torch.FloatTensor, ...], temb: Optional[torch.FloatTensor] = None, encoder_hidden_states: Optional[torch.FloatTensor] = None, image_only_indicator: Optional[torch.Tensor] = None, ) -> torch.FloatTensor: for resnet, attn in zip(self.resnets, self.attentions): # pop res hidden states res_hidden_states = res_hidden_states_tuple[-1] res_hidden_states_tuple = res_hidden_states_tuple[:-1] hidden_states = torch.cat([hidden_states, res_hidden_states], dim=1) if self.training and self.gradient_checkpointing: # TODO def create_custom_forward(module, return_dict=None): def custom_forward(*inputs): if return_dict is not None: return module(*inputs, return_dict=return_dict) else: return module(*inputs) return custom_forward ckpt_kwargs: Dict[str, Any] = {"use_reentrant": False} if is_torch_version(">=", "1.11.0") else {} hidden_states = torch.utils.checkpoint.checkpoint( create_custom_forward(resnet), hidden_states, temb, image_only_indicator, **ckpt_kwargs, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, image_only_indicator=image_only_indicator, return_dict=False, )[0] else: hidden_states = resnet( hidden_states, temb, image_only_indicator=image_only_indicator, ) hidden_states = attn( hidden_states, encoder_hidden_states=encoder_hidden_states, image_only_indicator=image_only_indicator, return_dict=False, )[0] if self.upsamplers is not None: for upsampler in self.upsamplers: hidden_states = upsampler(hidden_states) return hidden_states ================================================ FILE: libs/unet_motion_model.py ================================================ # Copyright 2023 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from typing import Any, Dict, Optional, Tuple, Union import torch import torch.nn as nn import torch.utils.checkpoint from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.loaders import UNet2DConditionLoadersMixin,PeftAdapterMixin from diffusers.utils import logging, deprecate from diffusers.models.attention_processor import ( ADDED_KV_ATTENTION_PROCESSORS, CROSS_ATTENTION_PROCESSORS, AttentionProcessor, AttnAddedKVProcessor, AttnProcessor, ) # from diffusers.models.controlnet import ControlNetConditioningEmbedding from diffusers.models.embeddings import TimestepEmbedding, Timesteps from diffusers.models.modeling_utils import ModelMixin from diffusers.models.transformers.transformer_temporal import TransformerTemporalModel from diffusers.models.unets.unet_2d_blocks import UNetMidBlock2DCrossAttn from .unet_2d_condition import UNet2DConditionModel from .unet_3d_blocks import ( CrossAttnDownBlockMotion, CrossAttnUpBlockMotion, DownBlockMotion, UNetMidBlockCrossAttnMotion, UpBlockMotion, get_down_block, get_up_block, ) from diffusers.models.unets.unet_3d_condition import UNet3DConditionOutput logger = logging.get_logger(__name__) # pylint: disable=invalid-name class MotionModules(nn.Module): def __init__( self, in_channels: int, layers_per_block: int = 2, num_attention_heads: int = 8, attention_bias: bool = False, cross_attention_dim: Optional[int] = None, activation_fn: str = "geglu", norm_num_groups: int = 32, max_seq_length: int = 32, ): super().__init__() self.motion_modules = nn.ModuleList([]) for i in range(layers_per_block): self.motion_modules.append( TransformerTemporalModel( in_channels=in_channels, norm_num_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, activation_fn=activation_fn, attention_bias=attention_bias, num_attention_heads=num_attention_heads, attention_head_dim=in_channels // num_attention_heads, positional_embeddings="sinusoidal", num_positional_embeddings=max_seq_length, ) ) class MotionAdapter(ModelMixin, ConfigMixin): @register_to_config def __init__( self, block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280), motion_layers_per_block: int = 2, motion_mid_block_layers_per_block: int = 1, motion_num_attention_heads: int = 8, motion_norm_num_groups: int = 32, motion_max_seq_length: int = 32, use_motion_mid_block: bool = True, ): """Container to store AnimateDiff Motion Modules Args: block_out_channels (`Tuple[int]`, *optional*, defaults to `(320, 640, 1280, 1280)`): The tuple of output channels for each UNet block. motion_layers_per_block (`int`, *optional*, defaults to 2): The number of motion layers per UNet block. motion_mid_block_layers_per_block (`int`, *optional*, defaults to 1): The number of motion layers in the middle UNet block. motion_num_attention_heads (`int`, *optional*, defaults to 8): The number of heads to use in each attention layer of the motion module. motion_norm_num_groups (`int`, *optional*, defaults to 32): The number of groups to use in each group normalization layer of the motion module. motion_max_seq_length (`int`, *optional*, defaults to 32): The maximum sequence length to use in the motion module. use_motion_mid_block (`bool`, *optional*, defaults to True): Whether to use a motion module in the middle of the UNet. """ super().__init__() down_blocks = [] up_blocks = [] for i, channel in enumerate(block_out_channels): output_channel = block_out_channels[i] down_blocks.append( MotionModules( in_channels=output_channel, norm_num_groups=motion_norm_num_groups, cross_attention_dim=None, activation_fn="geglu", attention_bias=False, num_attention_heads=motion_num_attention_heads, max_seq_length=motion_max_seq_length, layers_per_block=motion_layers_per_block, ) ) if use_motion_mid_block: self.mid_block = MotionModules( in_channels=block_out_channels[-1], norm_num_groups=motion_norm_num_groups, cross_attention_dim=None, activation_fn="geglu", attention_bias=False, num_attention_heads=motion_num_attention_heads, layers_per_block=motion_mid_block_layers_per_block, max_seq_length=motion_max_seq_length, ) else: self.mid_block = None reversed_block_out_channels = list(reversed(block_out_channels)) output_channel = reversed_block_out_channels[0] for i, channel in enumerate(reversed_block_out_channels): output_channel = reversed_block_out_channels[i] up_blocks.append( MotionModules( in_channels=output_channel, norm_num_groups=motion_norm_num_groups, cross_attention_dim=None, activation_fn="geglu", attention_bias=False, num_attention_heads=motion_num_attention_heads, max_seq_length=motion_max_seq_length, layers_per_block=motion_layers_per_block + 1, ) ) self.down_blocks = nn.ModuleList(down_blocks) self.up_blocks = nn.ModuleList(up_blocks) def forward(self, sample): pass class UNetMotionModel(ModelMixin, ConfigMixin, UNet2DConditionLoadersMixin,PeftAdapterMixin): r""" A modified conditional 2D UNet model that takes a noisy sample, conditional state, and a timestep and returns a sample shaped output. This model inherits from [`ModelMixin`]. Check the superclass documentation for it's generic methods implemented for all models (such as downloading or saving). """ _supports_gradient_checkpointing = True @register_to_config def __init__( self, sample_size: Optional[int] = None, in_channels: int = 4, conditioning_channels: int = 3, out_channels: int = 4, down_block_types: Tuple[str, ...] = ( "CrossAttnDownBlockMotion", "CrossAttnDownBlockMotion", "CrossAttnDownBlockMotion", "DownBlockMotion", ), mid_block_type: Optional[str] = "UNetMidBlockCrossAttnMotion", up_block_types: Tuple[str, ...] = ( "UpBlockMotion", "CrossAttnUpBlockMotion", "CrossAttnUpBlockMotion", "CrossAttnUpBlockMotion", ), block_out_channels: Tuple[int, ...] = (320, 640, 1280, 1280), layers_per_block: int = 2, downsample_padding: int = 1, mid_block_scale_factor: float = 1, act_fn: str = "silu", norm_num_groups: int = 32, norm_eps: float = 1e-5, cross_attention_dim: int = 1280, use_linear_projection: bool = False, num_attention_heads: Union[int, Tuple[int, ...]] = 8, motion_max_seq_length: int = 32, motion_num_attention_heads: int = 8, use_motion_mid_block: int = True, encoder_hid_dim: Optional[int] = None, encoder_hid_dim_type: Optional[str] = None, conditioning_embedding_out_channels: Optional[Tuple[int]] = (16, 32, 96, 256), ): super().__init__() self.sample_size = sample_size # Check inputs if len(down_block_types) != len(up_block_types): raise ValueError( f"Must provide the same number of `down_block_types` as `up_block_types`. `down_block_types`: {down_block_types}. `up_block_types`: {up_block_types}." ) if len(block_out_channels) != len(down_block_types): raise ValueError( f"Must provide the same number of `block_out_channels` as `down_block_types`. `block_out_channels`: {block_out_channels}. `down_block_types`: {down_block_types}." ) if not isinstance(num_attention_heads, int) and len(num_attention_heads) != len(down_block_types): raise ValueError( f"Must provide the same number of `num_attention_heads` as `down_block_types`. `num_attention_heads`: {num_attention_heads}. `down_block_types`: {down_block_types}." ) # input conv_in_kernel = 3 conv_out_kernel = 3 conv_in_padding = (conv_in_kernel - 1) // 2 self.conv_in = nn.Conv2d( in_channels, block_out_channels[0], kernel_size=conv_in_kernel, padding=conv_in_padding ) # time time_embed_dim = block_out_channels[0] * 4 self.time_proj = Timesteps(block_out_channels[0], True, 0) timestep_input_dim = block_out_channels[0] self.time_embedding = TimestepEmbedding( timestep_input_dim, time_embed_dim, act_fn=act_fn, ) if encoder_hid_dim_type is None: self.encoder_hid_proj = None # control net conditioning embedding # self.controlnet_cond_embedding = ControlNetConditioningEmbedding( # conditioning_embedding_channels=block_out_channels[0], # block_out_channels=conditioning_embedding_out_channels, # conditioning_channels=conditioning_channels, # ) # class embedding self.down_blocks = nn.ModuleList([]) self.up_blocks = nn.ModuleList([]) if isinstance(num_attention_heads, int): num_attention_heads = (num_attention_heads,) * len(down_block_types) # down output_channel = block_out_channels[0] for i, down_block_type in enumerate(down_block_types): input_channel = output_channel output_channel = block_out_channels[i] is_final_block = i == len(block_out_channels) - 1 down_block = get_down_block( down_block_type, num_layers=layers_per_block, in_channels=input_channel, out_channels=output_channel, temb_channels=time_embed_dim, add_downsample=not is_final_block, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads[i], downsample_padding=downsample_padding, use_linear_projection=use_linear_projection, dual_cross_attention=False, temporal_num_attention_heads=motion_num_attention_heads, temporal_max_seq_length=motion_max_seq_length, ) self.down_blocks.append(down_block) # mid if use_motion_mid_block: self.mid_block = UNetMidBlockCrossAttnMotion( in_channels=block_out_channels[-1], temb_channels=time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads[-1], resnet_groups=norm_num_groups, dual_cross_attention=False, temporal_num_attention_heads=motion_num_attention_heads, temporal_max_seq_length=motion_max_seq_length, ) else: self.mid_block = UNetMidBlock2DCrossAttn( in_channels=block_out_channels[-1], temb_channels=time_embed_dim, resnet_eps=norm_eps, resnet_act_fn=act_fn, output_scale_factor=mid_block_scale_factor, cross_attention_dim=cross_attention_dim, num_attention_heads=num_attention_heads[-1], resnet_groups=norm_num_groups, dual_cross_attention=False, ) # count how many layers upsample the images self.num_upsamplers = 0 # up reversed_block_out_channels = list(reversed(block_out_channels)) reversed_num_attention_heads = list(reversed(num_attention_heads)) output_channel = reversed_block_out_channels[0] for i, up_block_type in enumerate(up_block_types): is_final_block = i == len(block_out_channels) - 1 prev_output_channel = output_channel output_channel = reversed_block_out_channels[i] input_channel = reversed_block_out_channels[min(i + 1, len(block_out_channels) - 1)] # add upsample block for all BUT final layer if not is_final_block: add_upsample = True self.num_upsamplers += 1 else: add_upsample = False up_block = get_up_block( up_block_type, num_layers=layers_per_block + 1, in_channels=input_channel, out_channels=output_channel, prev_output_channel=prev_output_channel, temb_channels=time_embed_dim, add_upsample=add_upsample, resnet_eps=norm_eps, resnet_act_fn=act_fn, resnet_groups=norm_num_groups, cross_attention_dim=cross_attention_dim, num_attention_heads=reversed_num_attention_heads[i], dual_cross_attention=False, resolution_idx=i, use_linear_projection=use_linear_projection, temporal_num_attention_heads=motion_num_attention_heads, temporal_max_seq_length=motion_max_seq_length, ) self.up_blocks.append(up_block) prev_output_channel = output_channel # out if norm_num_groups is not None: self.conv_norm_out = nn.GroupNorm( num_channels=block_out_channels[0], num_groups=norm_num_groups, eps=norm_eps ) self.conv_act = nn.SiLU() else: self.conv_norm_out = None self.conv_act = None conv_out_padding = (conv_out_kernel - 1) // 2 self.conv_out = nn.Conv2d( block_out_channels[0], out_channels, kernel_size=conv_out_kernel, padding=conv_out_padding ) @classmethod def from_unet2d( cls, unet: UNet2DConditionModel, motion_adapter: Optional[MotionAdapter] = None, load_weights: bool = True, ): has_motion_adapter = motion_adapter is not None # based on https://github.com/guoyww/AnimateDiff/blob/895f3220c06318ea0760131ec70408b466c49333/animatediff/models/unet.py#L459 config = unet.config config["_class_name"] = cls.__name__ down_blocks = [] for down_blocks_type in config["down_block_types"]: if "CrossAttn" in down_blocks_type: down_blocks.append("CrossAttnDownBlockMotion") else: down_blocks.append("DownBlockMotion") config["down_block_types"] = down_blocks up_blocks = [] for down_blocks_type in config["up_block_types"]: if "CrossAttn" in down_blocks_type: up_blocks.append("CrossAttnUpBlockMotion") else: up_blocks.append("UpBlockMotion") config["up_block_types"] = up_blocks if has_motion_adapter: config["motion_num_attention_heads"] = motion_adapter.config["motion_num_attention_heads"] config["motion_max_seq_length"] = motion_adapter.config["motion_max_seq_length"] config["use_motion_mid_block"] = motion_adapter.config["use_motion_mid_block"] # Need this for backwards compatibility with UNet2DConditionModel checkpoints if not config.get("num_attention_heads"): config["num_attention_heads"] = config["attention_head_dim"] model = cls.from_config(config) if not load_weights: return model model.conv_in.load_state_dict(unet.conv_in.state_dict()) model.time_proj.load_state_dict(unet.time_proj.state_dict()) model.time_embedding.load_state_dict(unet.time_embedding.state_dict()) # model.controlnet_cond_embedding.load_state_dict(unet.controlnet_cond_embedding.state_dict()) # pose guider for i, down_block in enumerate(unet.down_blocks): model.down_blocks[i].resnets.load_state_dict(down_block.resnets.state_dict()) if hasattr(model.down_blocks[i], "attentions"): model.down_blocks[i].attentions.load_state_dict(down_block.attentions.state_dict()) if model.down_blocks[i].downsamplers: model.down_blocks[i].downsamplers.load_state_dict(down_block.downsamplers.state_dict()) for i, up_block in enumerate(unet.up_blocks): model.up_blocks[i].resnets.load_state_dict(up_block.resnets.state_dict()) if hasattr(model.up_blocks[i], "attentions"): model.up_blocks[i].attentions.load_state_dict(up_block.attentions.state_dict()) if model.up_blocks[i].upsamplers: model.up_blocks[i].upsamplers.load_state_dict(up_block.upsamplers.state_dict()) model.mid_block.resnets.load_state_dict(unet.mid_block.resnets.state_dict()) model.mid_block.attentions.load_state_dict(unet.mid_block.attentions.state_dict()) if unet.conv_norm_out is not None: model.conv_norm_out.load_state_dict(unet.conv_norm_out.state_dict()) if unet.conv_act is not None: model.conv_act.load_state_dict(unet.conv_act.state_dict()) model.conv_out.load_state_dict(unet.conv_out.state_dict()) if has_motion_adapter: model.load_motion_modules(motion_adapter) # ensure that the Motion UNet is the same dtype as the UNet2DConditionModel model.to(unet.dtype) return model def freeze_unet2d_params(self) -> None: """Freeze the weights of just the UNet2DConditionModel, and leave the motion modules unfrozen for fine tuning. """ # Freeze everything for param in self.parameters(): param.requires_grad = False # Unfreeze Motion Modules for down_block in self.down_blocks: motion_modules = down_block.motion_modules for param in motion_modules.parameters(): param.requires_grad = True for up_block in self.up_blocks: motion_modules = up_block.motion_modules for param in motion_modules.parameters(): param.requires_grad = True if hasattr(self.mid_block, "motion_modules"): motion_modules = self.mid_block.motion_modules for param in motion_modules.parameters(): param.requires_grad = True def load_motion_modules(self, motion_adapter: Optional[MotionAdapter]) -> None: for i, down_block in enumerate(motion_adapter.down_blocks): self.down_blocks[i].motion_modules.load_state_dict(down_block.motion_modules.state_dict()) for i, up_block in enumerate(motion_adapter.up_blocks): self.up_blocks[i].motion_modules.load_state_dict(up_block.motion_modules.state_dict()) # to support older motion modules that don't have a mid_block if hasattr(self.mid_block, "motion_modules"): self.mid_block.motion_modules.load_state_dict(motion_adapter.mid_block.motion_modules.state_dict()) def save_motion_modules( self, save_directory: str, is_main_process: bool = True, safe_serialization: bool = True, variant: Optional[str] = None, push_to_hub: bool = False, **kwargs, ) -> None: state_dict = self.state_dict() # Extract all motion modules motion_state_dict = {} for k, v in state_dict.items(): if "motion_modules" in k: motion_state_dict[k] = v adapter = MotionAdapter( block_out_channels=self.config["block_out_channels"], motion_layers_per_block=self.config["layers_per_block"], motion_norm_num_groups=self.config["norm_num_groups"], motion_num_attention_heads=self.config["motion_num_attention_heads"], motion_max_seq_length=self.config["motion_max_seq_length"], use_motion_mid_block=self.config["use_motion_mid_block"], ) adapter.load_state_dict(motion_state_dict) adapter.save_pretrained( save_directory=save_directory, is_main_process=is_main_process, safe_serialization=safe_serialization, variant=variant, push_to_hub=push_to_hub, **kwargs, ) @property # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.attn_processors def attn_processors(self) -> Dict[str, AttentionProcessor]: r""" Returns: `dict` of attention processors: A dictionary containing all attention processors used in the model with indexed by its weight name. """ # set recursively processors = {} def fn_recursive_add_processors(name: str, module: torch.nn.Module, processors: Dict[str, AttentionProcessor]): if hasattr(module, "get_processor"): processors[f"{name}.processor"] = module.get_processor(return_deprecated_lora=True) for sub_name, child in module.named_children(): fn_recursive_add_processors(f"{name}.{sub_name}", child, processors) return processors for name, module in self.named_children(): fn_recursive_add_processors(name, module, processors) return processors # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_attn_processor def set_attn_processor( self, processor: Union[AttentionProcessor, Dict[str, AttentionProcessor]], _remove_lora=False ): r""" Sets the attention processor to use to compute attention. Parameters: processor (`dict` of `AttentionProcessor` or only `AttentionProcessor`): The instantiated processor class or a dictionary of processor classes that will be set as the processor for **all** `Attention` layers. If `processor` is a dict, the key needs to define the path to the corresponding cross attention processor. This is strongly recommended when setting trainable attention processors. """ count = len(self.attn_processors.keys()) if isinstance(processor, dict) and len(processor) != count: raise ValueError( f"A dict of processors was passed, but the number of processors {len(processor)} does not match the" f" number of attention layers: {count}. Please make sure to pass {count} processor classes." ) def fn_recursive_attn_processor(name: str, module: torch.nn.Module, processor): if hasattr(module, "set_processor"): if not isinstance(processor, dict): module.set_processor(processor, _remove_lora=_remove_lora) else: module.set_processor(processor.pop(f"{name}.processor"), _remove_lora=_remove_lora) for sub_name, child in module.named_children(): fn_recursive_attn_processor(f"{name}.{sub_name}", child, processor) for name, module in self.named_children(): fn_recursive_attn_processor(name, module, processor) # Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.enable_forward_chunking def enable_forward_chunking(self, chunk_size: Optional[int] = None, dim: int = 0) -> None: """ Sets the attention processor to use [feed forward chunking](https://huggingface.co/blog/reformer#2-chunked-feed-forward-layers). Parameters: chunk_size (`int`, *optional*): The chunk size of the feed-forward layers. If not specified, will run feed-forward layer individually over each tensor of dim=`dim`. dim (`int`, *optional*, defaults to `0`): The dimension over which the feed-forward computation should be chunked. Choose between dim=0 (batch) or dim=1 (sequence length). """ if dim not in [0, 1]: raise ValueError(f"Make sure to set `dim` to either 0 or 1, not {dim}") # By default chunk size is 1 chunk_size = chunk_size or 1 def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int): if hasattr(module, "set_chunk_feed_forward"): module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) for child in module.children(): fn_recursive_feed_forward(child, chunk_size, dim) for module in self.children(): fn_recursive_feed_forward(module, chunk_size, dim) # Copied from diffusers.models.unet_3d_condition.UNet3DConditionModel.disable_forward_chunking def disable_forward_chunking(self) -> None: def fn_recursive_feed_forward(module: torch.nn.Module, chunk_size: int, dim: int): if hasattr(module, "set_chunk_feed_forward"): module.set_chunk_feed_forward(chunk_size=chunk_size, dim=dim) for child in module.children(): fn_recursive_feed_forward(child, chunk_size, dim) for module in self.children(): fn_recursive_feed_forward(module, None, 0) # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.set_default_attn_processor def set_default_attn_processor(self) -> None: """ Disables custom attention processors and sets the default attention implementation. """ if all(proc.__class__ in ADDED_KV_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnAddedKVProcessor() elif all(proc.__class__ in CROSS_ATTENTION_PROCESSORS for proc in self.attn_processors.values()): processor = AttnProcessor() else: raise ValueError( f"Cannot call `set_default_attn_processor` when attention processors are of type {next(iter(self.attn_processors.values()))}" ) self.set_attn_processor(processor, _remove_lora=True) def _set_gradient_checkpointing(self, module, value: bool = False) -> None: if isinstance(module, (CrossAttnDownBlockMotion, DownBlockMotion, CrossAttnUpBlockMotion, UpBlockMotion)): module.gradient_checkpointing = value # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.enable_freeu def enable_freeu(self, s1: float, s2: float, b1: float, b2: float) -> None: r"""Enables the FreeU mechanism from https://arxiv.org/abs/2309.11497. The suffixes after the scaling factors represent the stage blocks where they are being applied. Please refer to the [official repository](https://github.com/ChenyangSi/FreeU) for combinations of values that are known to work well for different pipelines such as Stable Diffusion v1, v2, and Stable Diffusion XL. Args: s1 (`float`): Scaling factor for stage 1 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process. s2 (`float`): Scaling factor for stage 2 to attenuate the contributions of the skip features. This is done to mitigate the "oversmoothing effect" in the enhanced denoising process. b1 (`float`): Scaling factor for stage 1 to amplify the contributions of backbone features. b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features. """ for i, upsample_block in enumerate(self.up_blocks): setattr(upsample_block, "s1", s1) setattr(upsample_block, "s2", s2) setattr(upsample_block, "b1", b1) setattr(upsample_block, "b2", b2) # Copied from diffusers.models.unet_2d_condition.UNet2DConditionModel.disable_freeu def disable_freeu(self) -> None: """Disables the FreeU mechanism.""" freeu_keys = {"s1", "s2", "b1", "b2"} for i, upsample_block in enumerate(self.up_blocks): for k in freeu_keys: if hasattr(upsample_block, k) or getattr(upsample_block, k, None) is not None: setattr(upsample_block, k, None) def forward( self, sample: torch.FloatTensor, timestep: Union[torch.Tensor, float, int], encoder_hidden_states: torch.Tensor, # controlnet_cond: torch.FloatTensor, timestep_cond: Optional[torch.Tensor] = None, attention_mask: Optional[torch.Tensor] = None, cross_attention_kwargs: Optional[Dict[str, Any]] = None, added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, down_block_additional_residuals: Optional[Tuple[torch.Tensor]] = None, down_intrablock_additional_residuals: Optional[Tuple[torch.Tensor]] = None, mid_block_additional_residual: Optional[torch.Tensor] = None, return_dict: bool = True, num_frames: int = 24, down_block_add_samples: Optional[Tuple[torch.Tensor]] = None, mid_block_add_sample: Optional[Tuple[torch.Tensor]] = None, up_block_add_samples: Optional[Tuple[torch.Tensor]] = None, ) -> Union[UNet3DConditionOutput, Tuple[torch.Tensor]]: r""" The [`UNetMotionModel`] forward method. Args: sample (`torch.FloatTensor`): The noisy input tensor with the following shape `(batch * num_frames, channel, height, width`. timestep (`torch.FloatTensor` or `float` or `int`): The number of timesteps to denoise an input. encoder_hidden_states (`torch.FloatTensor`): The encoder hidden states with shape `(batch, sequence_length, feature_dim)`. timestep_cond: (`torch.Tensor`, *optional*, defaults to `None`): Conditional embeddings for timestep. If provided, the embeddings will be summed with the samples passed through the `self.time_embedding` layer to obtain the timestep embeddings. attention_mask (`torch.Tensor`, *optional*, defaults to `None`): An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large negative values to the attention scores corresponding to "discard" tokens. cross_attention_kwargs (`dict`, *optional*): A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under `self.processor` in [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py). down_block_additional_residuals: (`tuple` of `torch.Tensor`, *optional*): A tuple of tensors that if specified are added to the residuals of down unet blocks. mid_block_additional_residual: (`torch.Tensor`, *optional*): A tensor that if specified is added to the residual of the middle unet block. return_dict (`bool`, *optional*, defaults to `True`): Whether or not to return a [`~models.unet_3d_condition.UNet3DConditionOutput`] instead of a plain tuple. Returns: [`~models.unet_3d_condition.UNet3DConditionOutput`] or `tuple`: If `return_dict` is True, an [`~models.unet_3d_condition.UNet3DConditionOutput`] is returned, otherwise a `tuple` is returned where the first element is the sample tensor. """ # By default samples have to be AT least a multiple of the overall upsampling factor. # The overall upsampling factor is equal to 2 ** (# num of upsampling layears). # However, the upsampling interpolation output size can be forced to fit any upsampling size # on the fly if necessary. default_overall_up_factor = 2**self.num_upsamplers # upsample size should be forwarded when sample is not a multiple of `default_overall_up_factor` forward_upsample_size = False upsample_size = None if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]): logger.info("Forward upsample size to force interpolation output size.") forward_upsample_size = True # prepare attention_mask if attention_mask is not None: attention_mask = (1 - attention_mask.to(sample.dtype)) * -10000.0 attention_mask = attention_mask.unsqueeze(1) # 1. time timesteps = timestep if not torch.is_tensor(timesteps): # TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can # This would be a good case for the `match` statement (Python 3.10+) is_mps = sample.device.type == "mps" if isinstance(timestep, float): dtype = torch.float32 if is_mps else torch.float64 else: dtype = torch.int32 if is_mps else torch.int64 timesteps = torch.tensor([timesteps], dtype=dtype, device=sample.device) elif len(timesteps.shape) == 0: timesteps = timesteps[None].to(sample.device) # broadcast to batch dimension in a way that's compatible with ONNX/Core ML timesteps = timesteps.expand(sample.shape[0] // num_frames) t_emb = self.time_proj(timesteps) # timesteps does not contain any weights and will always return f32 tensors # but time_embedding might actually be running in fp16. so we need to cast here. # there might be better ways to encapsulate this. t_emb = t_emb.to(dtype=self.dtype) emb = self.time_embedding(t_emb, timestep_cond) emb = emb.repeat_interleave(repeats=num_frames, dim=0) if self.encoder_hid_proj is not None and self.config.encoder_hid_dim_type == "ip_image_proj": if "image_embeds" not in added_cond_kwargs: raise ValueError( f"{self.__class__} has the config param `encoder_hid_dim_type` set to 'ip_image_proj' which requires the keyword argument `image_embeds` to be passed in `added_conditions`" ) image_embeds = added_cond_kwargs.get("image_embeds") image_embeds = self.encoder_hid_proj(image_embeds).to(encoder_hidden_states.dtype) encoder_hidden_states = torch.cat([encoder_hidden_states, image_embeds], dim=1) encoder_hidden_states = encoder_hidden_states.repeat_interleave(repeats=num_frames, dim=0) # 2. pre-process # sample = sample.permute(0, 2, 1, 3, 4).reshape((sample.shape[0] * num_frames, -1) + sample.shape[3:]) # N*T C H W sample = self.conv_in(sample) # controlnet_cond = self.controlnet_cond_embedding(controlnet_cond) # sample += controlnet_cond # 3. down is_controlnet = mid_block_additional_residual is not None and down_block_additional_residuals is not None # using new arg down_intrablock_additional_residuals for T2I-Adapters, to distinguish from controlnets is_adapter = down_intrablock_additional_residuals is not None # maintain backward compatibility for legacy usage, where # T2I-Adapter and ControlNet both use down_block_additional_residuals arg # but can only use one or the other is_brushnet = down_block_add_samples is not None and mid_block_add_sample is not None and up_block_add_samples is not None if not is_adapter and mid_block_additional_residual is None and down_block_additional_residuals is not None: deprecate( "T2I should not use down_block_additional_residuals", "1.3.0", "Passing intrablock residual connections with `down_block_additional_residuals` is deprecated \ and will be removed in diffusers 1.3.0. `down_block_additional_residuals` should only be used \ for ControlNet. Please make sure use `down_intrablock_additional_residuals` instead. ", standard_warn=False, ) down_intrablock_additional_residuals = down_block_additional_residuals is_adapter = True down_block_res_samples = (sample,) if is_brushnet: sample = sample + down_block_add_samples.pop(0) for downsample_block in self.down_blocks: if hasattr(downsample_block, "has_cross_attention") and downsample_block.has_cross_attention: # For t2i-adapter CrossAttnDownBlock2D additional_residuals = {} if is_adapter and len(down_intrablock_additional_residuals) > 0: additional_residuals["additional_residuals"] = down_intrablock_additional_residuals.pop(0) if is_brushnet and len(down_block_add_samples)>0: additional_residuals["down_block_add_samples"] = [down_block_add_samples.pop(0) for _ in range(len(downsample_block.resnets)+(downsample_block.downsamplers !=None))] sample, res_samples = downsample_block( hidden_states=sample, temb=emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, **additional_residuals, ) else: additional_residuals = {} if is_brushnet and len(down_block_add_samples)>0: additional_residuals["down_block_add_samples"] = [down_block_add_samples.pop(0) for _ in range(len(downsample_block.resnets)+(downsample_block.downsamplers !=None))] sample, res_samples = downsample_block(hidden_states=sample, temb=emb, num_frames=num_frames, **additional_residuals,) if is_adapter and len(down_intrablock_additional_residuals) > 0: sample += down_intrablock_additional_residuals.pop(0) down_block_res_samples += res_samples if is_controlnet: new_down_block_res_samples = () for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): down_block_res_sample = down_block_res_sample + down_block_additional_residual new_down_block_res_samples = new_down_block_res_samples + (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples if down_block_additional_residuals is not None: new_down_block_res_samples = () for down_block_res_sample, down_block_additional_residual in zip( down_block_res_samples, down_block_additional_residuals ): down_block_res_sample = down_block_res_sample + down_block_additional_residual new_down_block_res_samples += (down_block_res_sample,) down_block_res_samples = new_down_block_res_samples # 4. mid if self.mid_block is not None: # To support older versions of motion modules that don't have a mid_block if hasattr(self.mid_block, "motion_modules"): sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, mid_block_add_sample=mid_block_add_sample, ) else: sample = self.mid_block( sample, emb, encoder_hidden_states=encoder_hidden_states, attention_mask=attention_mask, cross_attention_kwargs=cross_attention_kwargs, mid_block_add_sample=mid_block_add_sample, ) if is_controlnet: sample = sample + mid_block_additional_residual # if is_brushnet: # sample = sample + mid_block_add_sample if mid_block_additional_residual is not None: sample = sample + mid_block_additional_residual # 5. up for i, upsample_block in enumerate(self.up_blocks): is_final_block = i == len(self.up_blocks) - 1 res_samples = down_block_res_samples[-len(upsample_block.resnets) :] down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)] # if we have not reached the final block and need to forward the # upsample size, we do it here if not is_final_block and forward_upsample_size: upsample_size = down_block_res_samples[-1].shape[2:] if hasattr(upsample_block, "has_cross_attention") and upsample_block.has_cross_attention: additional_residuals = {} if is_brushnet and len(up_block_add_samples)>0: additional_residuals["up_block_add_samples"] = [up_block_add_samples.pop(0) for _ in range(len(upsample_block.resnets)+(upsample_block.upsamplers !=None))] sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, encoder_hidden_states=encoder_hidden_states, upsample_size=upsample_size, attention_mask=attention_mask, num_frames=num_frames, cross_attention_kwargs=cross_attention_kwargs, **additional_residuals, ) else: additional_residuals = {} if is_brushnet and len(up_block_add_samples)>0: additional_residuals["up_block_add_samples"] = [up_block_add_samples.pop(0) for _ in range(len(upsample_block.resnets)+(upsample_block.upsamplers !=None))] sample = upsample_block( hidden_states=sample, temb=emb, res_hidden_states_tuple=res_samples, upsample_size=upsample_size, num_frames=num_frames, **additional_residuals, ) # 6. post-process if self.conv_norm_out: sample = self.conv_norm_out(sample) sample = self.conv_act(sample) sample = self.conv_out(sample) # reshape to (batch, framerate, channel, width, height) # sample = sample[None, :].reshape((-1, num_frames) + sample.shape[1:]) if not return_dict: return (sample,) return UNet3DConditionOutput(sample=sample) ================================================ FILE: libs/v1-inference.yaml ================================================ model: base_learning_rate: 1.0e-04 target: ldm.models.diffusion.ddpm.LatentDiffusion params: linear_start: 0.00085 linear_end: 0.0120 num_timesteps_cond: 1 log_every_t: 200 timesteps: 1000 first_stage_key: "jpg" cond_stage_key: "txt" image_size: 64 channels: 4 cond_stage_trainable: false # Note: different from the one we trained before conditioning_key: crossattn monitor: val/loss_simple_ema scale_factor: 0.18215 use_ema: False scheduler_config: # 10000 warmup steps target: ldm.lr_scheduler.LambdaLinearScheduler params: warm_up_steps: [ 10000 ] cycle_lengths: [ 10000000000000 ] # incredibly large number to prevent corner cases f_start: [ 1.e-6 ] f_max: [ 1. ] f_min: [ 1. ] unet_config: target: ldm.modules.diffusionmodules.openaimodel.UNetModel params: image_size: 32 # unused in_channels: 4 out_channels: 4 model_channels: 320 attention_resolutions: [ 4, 2, 1 ] num_res_blocks: 2 channel_mult: [ 1, 2, 4, 4 ] num_heads: 8 use_spatial_transformer: True transformer_depth: 1 context_dim: 768 use_checkpoint: True legacy: False first_stage_config: target: ldm.models.autoencoder.AutoencoderKL params: embed_dim: 4 monitor: val/rec_loss ddconfig: double_z: true z_channels: 4 resolution: 256 in_channels: 3 out_ch: 3 ch: 128 ch_mult: - 1 - 2 - 4 - 4 num_res_blocks: 2 attn_resolutions: [] dropout: 0.0 lossconfig: target: torch.nn.Identity cond_stage_config: target: ldm.modules.encoders.modules.FrozenCLIPEmbedder ================================================ FILE: node_utils.py ================================================ # !/usr/bin/env python # -*- coding: UTF-8 -*- import os import torch from PIL import Image import numpy as np import cv2 import time from comfy.utils import common_upscale,ProgressBar from huggingface_hub import hf_hub_download import torchvision.transforms as transforms from transformers import AutoModelForImageSegmentation import folder_paths import gc cur_path = os.path.dirname(os.path.abspath(__file__)) device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" def image2masks(repo,video_image): start_time = time.time() model = AutoModelForImageSegmentation.from_pretrained(repo, trust_remote_code=True) torch.set_float32_matmul_precision(['high', 'highest'][0]) model.to(device) model.eval() # Data settings image_size = (1024, 1024) transform_image = transforms.Compose([ transforms.Resize(image_size), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]) masks=[] for img in video_image: input_images = transform_image(img).unsqueeze(0).to('cuda') # Prediction with torch.no_grad(): preds = model(input_images)[-1].sigmoid().cpu() pred = preds[0].squeeze() pred_pil = transforms.ToPILImage()(pred) mask = pred_pil.resize(img.size) #img.putalpha(mask) masks.append(mask.convert('RGB')) end_time = time.time() load_time = end_time - start_time print(f"image2masks infer time: {load_time:.4f} s") model.to('cpu') gc.collect() torch.cuda.empty_cache() return masks def resize_and_center_paste(image_list, target_size=(1024, 1024)): # 定义转换为张量的变换 to_tensor = transforms.ToTensor() # 处理每张图像 images = [] for img in image_list: # 获取原始图像尺寸 img_width, img_height = img.size # 计算缩放比例 scale_factor = target_size[0] / max(img_width, img_height) # 计算新的尺寸 new_width = int(img_width * scale_factor) new_height = int(img_height * scale_factor) # 缩放图像 resized_img = img.resize((new_width, new_height), Image.BICUBIC) # 创建空白画布 canvas = Image.new('RGB', target_size, (0, 0, 0)) # 计算粘贴位置 paste_x = (target_size[0] - new_width) // 2 paste_y = (target_size[1] - new_height) // 2 # 粘贴图像到画布中心 canvas.paste(resized_img, (paste_x, paste_y)) # 转换为张量 tensor_img = to_tensor(canvas) images.append(tensor_img) # 堆叠所有张量 images_tensor = torch.stack(images) return images_tensor def center_paste_and_resize(image_list, target_size=(1024, 1024)): # 定义转换为张量的变换 to_tensor = transforms.ToTensor() # 处理每张图像 images = [] for img in image_list: # 创建空白画布 canvas = Image.new('RGB', target_size, (0, 0, 0)) # 计算粘贴位置 img_width, img_height = img.size paste_x = (target_size[0] - img_width) // 2 paste_y = (target_size[1] - img_height) // 2 # 粘贴图像到画布中心 canvas.paste(img, (paste_x, paste_y)) # 转换为张量 tensor_img = to_tensor(canvas) images.append(tensor_img) # 堆叠所有张量 images_tensor = torch.stack(images) return images_tensor def tensor_to_pil(tensor): image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy() image = Image.fromarray(image_np, mode='RGB') return image def tensor2pil_list(image,width,height): B,_,_,_=image.size() if B==1: ref_image_list=[tensor2pil_upscale(image,width,height)] else: img_list = list(torch.chunk(image, chunks=B)) ref_image_list = [tensor2pil_upscale(img,width,height) for img in img_list] return ref_image_list def tensor2pil_upscale(img_tensor, width, height): samples = img_tensor.movedim(-1, 1) img = common_upscale(samples, width, height, "bilinear", "center") samples = img.movedim(1, -1) img_pil = tensor_to_pil(samples) return img_pil def nomarl_upscale(img, width, height): samples = img.movedim(-1, 1) img = common_upscale(samples, width, height, "bilinear", "center") samples = img.movedim(1, -1) img = tensor_to_pil(samples) return img def tensor2cv(tensor_image): if len(tensor_image.shape)==4:#bhwc to hwc tensor_image=tensor_image.squeeze(0) if tensor_image.is_cuda: tensor_image = tensor_image.cpu().detach() tensor_image=tensor_image.numpy() #反归一化 maxValue=tensor_image.max() tensor_image=tensor_image*255/maxValue img_cv2=np.uint8(tensor_image)#32 to uint8 img_cv2=cv2.cvtColor(img_cv2,cv2.COLOR_RGB2BGR) return img_cv2 def cvargb2tensor(img): assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img)) img = torch.from_numpy(img.transpose((2, 0, 1))) return img.float().div(255).unsqueeze(0) # 255也可以改为256 def cv2tensor(img): assert type(img) == np.ndarray, 'the img type is {}, but ndarry expected'.format(type(img)) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = torch.from_numpy(img.transpose((2, 0, 1))) return img.float().div(255).unsqueeze(0) # 255也可以改为256 def images_generator(img_list: list,): #get img size sizes = {} for image_ in img_list: if isinstance(image_,Image.Image): count = sizes.get(image_.size, 0) sizes[image_.size] = count + 1 elif isinstance(image_,np.ndarray): count = sizes.get(image_.shape[:2][::-1], 0) sizes[image_.shape[:2][::-1]] = count + 1 else: raise "unsupport image list,must be pil or cv2!!!" size = max(sizes.items(), key=lambda x: x[1])[0] yield size[0], size[1] # any to tensor def load_image(img_in): if isinstance(img_in, Image.Image): img_in=img_in.convert("RGB") i = np.array(img_in, dtype=np.float32) i = torch.from_numpy(i).div_(255) if i.shape[0] != size[1] or i.shape[1] != size[0]: i = torch.from_numpy(i).movedim(-1, 0).unsqueeze(0) i = common_upscale(i, size[0], size[1], "lanczos", "center") i = i.squeeze(0).movedim(0, -1).numpy() return i elif isinstance(img_in,np.ndarray): i=cv2.cvtColor(img_in,cv2.COLOR_BGR2RGB).astype(np.float32) i = torch.from_numpy(i).div_(255) #print(i.shape) return i else: raise "unsupport image list,must be pil,cv2 or tensor!!!" total_images = len(img_list) processed_images = 0 pbar = ProgressBar(total_images) images = map(load_image, img_list) try: prev_image = next(images) while True: next_image = next(images) yield prev_image processed_images += 1 pbar.update_absolute(processed_images, total_images) prev_image = next_image except StopIteration: pass if prev_image is not None: yield prev_image def load_images(img_list: list,): gen = images_generator(img_list) (width, height) = next(gen) images = torch.from_numpy(np.fromiter(gen, np.dtype((np.float32, (height, width, 3))))) if len(images) == 0: raise FileNotFoundError(f"No images could be loaded .") return images def tensor2pil(tensor): image_np = tensor.squeeze().mul(255).clamp(0, 255).byte().numpy() image = Image.fromarray(image_np, mode='RGB') return image def pil2narry(img): narry = torch.from_numpy(np.array(img).astype(np.float32) / 255.0).unsqueeze(0) return narry def equalize_lists(list1, list2): """ 比较两个列表的长度，如果不一致，则将较短的列表复制以匹配较长列表的长度。 参数: list1 (list): 第一个列表 list2 (list): 第二个列表 返回: tuple: 包含两个长度相等的列表的元组 """ len1 = len(list1) len2 = len(list2) if len1 == len2: pass elif len1 < len2: print("list1 is shorter than list2, copying list1 to match list2's length.") list1.extend(list1 * ((len2 // len1) + 1)) # 复制list1以匹配list2的长度 list1 = list1[:len2] # 确保长度一致 else: print("list2 is shorter than list1, copying list2 to match list1's length.") list2.extend(list2 * ((len1 // len2) + 1)) # 复制list2以匹配list1的长度 list2 = list2[:len1] # 确保长度一致 return list1, list2 def file_exists(directory, filename): # 构建文件的完整路径 file_path = os.path.join(directory, filename) # 检查文件是否存在 return os.path.isfile(file_path) def download_weights(file_dir,repo_id,subfolder="",pt_name=""): if subfolder: file_path = os.path.join(file_dir,subfolder, pt_name) sub_dir=os.path.join(file_dir,subfolder) if not os.path.exists(sub_dir): os.makedirs(sub_dir) if not os.path.exists(file_path): file_path = hf_hub_download( repo_id=repo_id, subfolder=subfolder, filename=pt_name, local_dir = file_dir, ) return file_path else: file_path = os.path.join(file_dir, pt_name) if not os.path.exists(file_dir): os.makedirs(file_dir) if not os.path.exists(file_path): file_path = hf_hub_download( repo_id=repo_id, filename=pt_name, local_dir=file_dir, ) return file_path ================================================ FILE: propainter/RAFT/__init__.py ================================================ # from .demo import RAFT_infer from .raft import RAFT ================================================ FILE: propainter/RAFT/corr.py ================================================ import torch import torch.nn.functional as F from .utils.utils import bilinear_sampler, coords_grid try: import alt_cuda_corr except: # alt_cuda_corr is not compiled pass class CorrBlock: def __init__(self, fmap1, fmap2, num_levels=4, radius=4): self.num_levels = num_levels self.radius = radius self.corr_pyramid = [] # all pairs correlation corr = CorrBlock.corr(fmap1, fmap2) batch, h1, w1, dim, h2, w2 = corr.shape corr = corr.reshape(batch*h1*w1, dim, h2, w2) self.corr_pyramid.append(corr) for i in range(self.num_levels-1): corr = F.avg_pool2d(corr, 2, stride=2) self.corr_pyramid.append(corr) def __call__(self, coords): r = self.radius coords = coords.permute(0, 2, 3, 1) batch, h1, w1, _ = coords.shape out_pyramid = [] for i in range(self.num_levels): corr = self.corr_pyramid[i] dx = torch.linspace(-r, r, 2*r+1) dy = torch.linspace(-r, r, 2*r+1) delta = torch.stack(torch.meshgrid(dy, dx), axis=-1).to(coords.device) centroid_lvl = coords.reshape(batch*h1*w1, 1, 1, 2) / 2**i delta_lvl = delta.view(1, 2*r+1, 2*r+1, 2) coords_lvl = centroid_lvl + delta_lvl corr = bilinear_sampler(corr, coords_lvl) corr = corr.view(batch, h1, w1, -1) out_pyramid.append(corr) out = torch.cat(out_pyramid, dim=-1) return out.permute(0, 3, 1, 2).contiguous().float() @staticmethod def corr(fmap1, fmap2): batch, dim, ht, wd = fmap1.shape fmap1 = fmap1.view(batch, dim, ht*wd) fmap2 = fmap2.view(batch, dim, ht*wd) corr = torch.matmul(fmap1.transpose(1,2), fmap2) corr = corr.view(batch, ht, wd, 1, ht, wd) return corr / torch.sqrt(torch.tensor(dim).float()) class CorrLayer(torch.autograd.Function): @staticmethod def forward(ctx, fmap1, fmap2, coords, r): fmap1 = fmap1.contiguous() fmap2 = fmap2.contiguous() coords = coords.contiguous() ctx.save_for_backward(fmap1, fmap2, coords) ctx.r = r corr, = correlation_cudaz.forward(fmap1, fmap2, coords, ctx.r) return corr @staticmethod def backward(ctx, grad_corr): fmap1, fmap2, coords = ctx.saved_tensors grad_corr = grad_corr.contiguous() fmap1_grad, fmap2_grad, coords_grad = \ correlation_cudaz.backward(fmap1, fmap2, coords, grad_corr, ctx.r) return fmap1_grad, fmap2_grad, coords_grad, None class AlternateCorrBlock: def __init__(self, fmap1, fmap2, num_levels=4, radius=4): self.num_levels = num_levels self.radius = radius self.pyramid = [(fmap1, fmap2)] for i in range(self.num_levels): fmap1 = F.avg_pool2d(fmap1, 2, stride=2) fmap2 = F.avg_pool2d(fmap2, 2, stride=2) self.pyramid.append((fmap1, fmap2)) def __call__(self, coords): coords = coords.permute(0, 2, 3, 1) B, H, W, _ = coords.shape corr_list = [] for i in range(self.num_levels): r = self.radius fmap1_i = self.pyramid[0][0].permute(0, 2, 3, 1) fmap2_i = self.pyramid[i][1].permute(0, 2, 3, 1) coords_i = (coords / 2**i).reshape(B, 1, H, W, 2).contiguous() corr = alt_cuda_corr(fmap1_i, fmap2_i, coords_i, r) corr_list.append(corr.squeeze(1)) corr = torch.stack(corr_list, dim=1) corr = corr.reshape(B, -1, H, W) return corr / 16.0 ================================================ FILE: propainter/RAFT/datasets.py ================================================ # Data loading based on https://github.com/NVIDIA/flownet2-pytorch import numpy as np import torch import torch.utils.data as data import torch.nn.functional as F import os import math import random from glob import glob import os.path as osp from .utils import frame_utils from .utils.augmentor import FlowAugmentor, SparseFlowAugmentor class FlowDataset(data.Dataset): def __init__(self, aug_params=None, sparse=False): self.augmentor = None self.sparse = sparse if aug_params is not None: if sparse: self.augmentor = SparseFlowAugmentor(**aug_params) else: self.augmentor = FlowAugmentor(**aug_params) self.is_test = False self.init_seed = False self.flow_list = [] self.image_list = [] self.extra_info = [] def __getitem__(self, index): if self.is_test: img1 = frame_utils.read_gen(self.image_list[index][0]) img2 = frame_utils.read_gen(self.image_list[index][1]) img1 = np.array(img1).astype(np.uint8)[..., :3] img2 = np.array(img2).astype(np.uint8)[..., :3] img1 = torch.from_numpy(img1).permute(2, 0, 1).float() img2 = torch.from_numpy(img2).permute(2, 0, 1).float() return img1, img2, self.extra_info[index] if not self.init_seed: worker_info = torch.utils.data.get_worker_info() if worker_info is not None: torch.manual_seed(worker_info.id) np.random.seed(worker_info.id) random.seed(worker_info.id) self.init_seed = True index = index % len(self.image_list) valid = None if self.sparse: flow, valid = frame_utils.readFlowKITTI(self.flow_list[index]) else: flow = frame_utils.read_gen(self.flow_list[index]) img1 = frame_utils.read_gen(self.image_list[index][0]) img2 = frame_utils.read_gen(self.image_list[index][1]) flow = np.array(flow).astype(np.float32) img1 = np.array(img1).astype(np.uint8) img2 = np.array(img2).astype(np.uint8) # grayscale images if len(img1.shape) == 2: img1 = np.tile(img1[...,None], (1, 1, 3)) img2 = np.tile(img2[...,None], (1, 1, 3)) else: img1 = img1[..., :3] img2 = img2[..., :3] if self.augmentor is not None: if self.sparse: img1, img2, flow, valid = self.augmentor(img1, img2, flow, valid) else: img1, img2, flow = self.augmentor(img1, img2, flow) img1 = torch.from_numpy(img1).permute(2, 0, 1).float() img2 = torch.from_numpy(img2).permute(2, 0, 1).float() flow = torch.from_numpy(flow).permute(2, 0, 1).float() if valid is not None: valid = torch.from_numpy(valid) else: valid = (flow[0].abs() < 1000) & (flow[1].abs() < 1000) return img1, img2, flow, valid.float() def __rmul__(self, v): self.flow_list = v * self.flow_list self.image_list = v * self.image_list return self def __len__(self): return len(self.image_list) class MpiSintel(FlowDataset): def __init__(self, aug_params=None, split='training', root='datasets/Sintel', dstype='clean'): super(MpiSintel, self).__init__(aug_params) flow_root = osp.join(root, split, 'flow') image_root = osp.join(root, split, dstype) if split == 'test': self.is_test = True for scene in os.listdir(image_root): image_list = sorted(glob(osp.join(image_root, scene, '*.png'))) for i in range(len(image_list)-1): self.image_list += [ [image_list[i], image_list[i+1]] ] self.extra_info += [ (scene, i) ] # scene and frame_id if split != 'test': self.flow_list += sorted(glob(osp.join(flow_root, scene, '*.flo'))) class FlyingChairs(FlowDataset): def __init__(self, aug_params=None, split='train', root='datasets/FlyingChairs_release/data'): super(FlyingChairs, self).__init__(aug_params) images = sorted(glob(osp.join(root, '*.ppm'))) flows = sorted(glob(osp.join(root, '*.flo'))) assert (len(images)//2 == len(flows)) split_list = np.loadtxt('chairs_split.txt', dtype=np.int32) for i in range(len(flows)): xid = split_list[i] if (split=='training' and xid==1) or (split=='validation' and xid==2): self.flow_list += [ flows[i] ] self.image_list += [ [images[2*i], images[2*i+1]] ] class FlyingThings3D(FlowDataset): def __init__(self, aug_params=None, root='datasets/FlyingThings3D', dstype='frames_cleanpass'): super(FlyingThings3D, self).__init__(aug_params) for cam in ['left']: for direction in ['into_future', 'into_past']: image_dirs = sorted(glob(osp.join(root, dstype, 'TRAIN/*/*'))) image_dirs = sorted([osp.join(f, cam) for f in image_dirs]) flow_dirs = sorted(glob(osp.join(root, 'optical_flow/TRAIN/*/*'))) flow_dirs = sorted([osp.join(f, direction, cam) for f in flow_dirs]) for idir, fdir in zip(image_dirs, flow_dirs): images = sorted(glob(osp.join(idir, '*.png')) ) flows = sorted(glob(osp.join(fdir, '*.pfm')) ) for i in range(len(flows)-1): if direction == 'into_future': self.image_list += [ [images[i], images[i+1]] ] self.flow_list += [ flows[i] ] elif direction == 'into_past': self.image_list += [ [images[i+1], images[i]] ] self.flow_list += [ flows[i+1] ] class KITTI(FlowDataset): def __init__(self, aug_params=None, split='training', root='datasets/KITTI'): super(KITTI, self).__init__(aug_params, sparse=True) if split == 'testing': self.is_test = True root = osp.join(root, split) images1 = sorted(glob(osp.join(root, 'image_2/*_10.png'))) images2 = sorted(glob(osp.join(root, 'image_2/*_11.png'))) for img1, img2 in zip(images1, images2): frame_id = img1.split('/')[-1] self.extra_info += [ [frame_id] ] self.image_list += [ [img1, img2] ] if split == 'training': self.flow_list = sorted(glob(osp.join(root, 'flow_occ/*_10.png'))) class HD1K(FlowDataset): def __init__(self, aug_params=None, root='datasets/HD1k'): super(HD1K, self).__init__(aug_params, sparse=True) seq_ix = 0 while 1: flows = sorted(glob(os.path.join(root, 'hd1k_flow_gt', 'flow_occ/%06d_*.png' % seq_ix))) images = sorted(glob(os.path.join(root, 'hd1k_input', 'image_2/%06d_*.png' % seq_ix))) if len(flows) == 0: break for i in range(len(flows)-1): self.flow_list += [flows[i]] self.image_list += [ [images[i], images[i+1]] ] seq_ix += 1 def fetch_dataloader(args, TRAIN_DS='C+T+K+S+H'): """ Create the data loader for the corresponding trainign set """ if args.stage == 'chairs': aug_params = {'crop_size': args.image_size, 'min_scale': -0.1, 'max_scale': 1.0, 'do_flip': True} train_dataset = FlyingChairs(aug_params, split='training') elif args.stage == 'things': aug_params = {'crop_size': args.image_size, 'min_scale': -0.4, 'max_scale': 0.8, 'do_flip': True} clean_dataset = FlyingThings3D(aug_params, dstype='frames_cleanpass') final_dataset = FlyingThings3D(aug_params, dstype='frames_finalpass') train_dataset = clean_dataset + final_dataset elif args.stage == 'sintel': aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.6, 'do_flip': True} things = FlyingThings3D(aug_params, dstype='frames_cleanpass') sintel_clean = MpiSintel(aug_params, split='training', dstype='clean') sintel_final = MpiSintel(aug_params, split='training', dstype='final') if TRAIN_DS == 'C+T+K+S+H': kitti = KITTI({'crop_size': args.image_size, 'min_scale': -0.3, 'max_scale': 0.5, 'do_flip': True}) hd1k = HD1K({'crop_size': args.image_size, 'min_scale': -0.5, 'max_scale': 0.2, 'do_flip': True}) train_dataset = 100*sintel_clean + 100*sintel_final + 200*kitti + 5*hd1k + things elif TRAIN_DS == 'C+T+K/S': train_dataset = 100*sintel_clean + 100*sintel_final + things elif args.stage == 'kitti': aug_params = {'crop_size': args.image_size, 'min_scale': -0.2, 'max_scale': 0.4, 'do_flip': False} train_dataset = KITTI(aug_params, split='training') train_loader = data.DataLoader(train_dataset, batch_size=args.batch_size, pin_memory=False, shuffle=True, num_workers=4, drop_last=True) print('Training with %d image pairs' % len(train_dataset)) return train_loader ================================================ FILE: propainter/RAFT/demo.py ================================================ import sys import argparse import os import cv2 import glob import numpy as np import torch from PIL import Image from .raft import RAFT from .utils import flow_viz from .utils.utils import InputPadder DEVICE = 'cuda' def load_image(imfile): img = np.array(Image.open(imfile)).astype(np.uint8) img = torch.from_numpy(img).permute(2, 0, 1).float() return img def load_image_list(image_files): images = [] for imfile in sorted(image_files): images.append(load_image(imfile)) images = torch.stack(images, dim=0) images = images.to(DEVICE) padder = InputPadder(images.shape) return padder.pad(images)[0] def viz(img, flo): img = img[0].permute(1,2,0).cpu().numpy() flo = flo[0].permute(1,2,0).cpu().numpy() # map flow to rgb image flo = flow_viz.flow_to_image(flo) # img_flo = np.concatenate([img, flo], axis=0) img_flo = flo cv2.imwrite('/home/chengao/test/flow.png', img_flo[:, :, [2,1,0]]) # cv2.imshow('image', img_flo[:, :, [2,1,0]]/255.0) # cv2.waitKey() def demo(args): model = torch.nn.DataParallel(RAFT(args)) model.load_state_dict(torch.load(args.model)) model = model.module model.to(DEVICE) model.eval() with torch.no_grad(): images = glob.glob(os.path.join(args.path, '*.png')) + \ glob.glob(os.path.join(args.path, '*.jpg')) images = load_image_list(images) for i in range(images.shape[0]-1): image1 = images[i,None] image2 = images[i+1,None] flow_low, flow_up = model(image1, image2, iters=20, test_mode=True) viz(image1, flow_up) def RAFT_infer(args): model = torch.nn.DataParallel(RAFT(args)) model.load_state_dict(torch.load(args.model)) model = model.module model.to(DEVICE) model.eval() return model ================================================ FILE: propainter/RAFT/extractor.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F class ResidualBlock(nn.Module): def __init__(self, in_planes, planes, norm_fn='group', stride=1): super(ResidualBlock, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, stride=stride) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, padding=1) self.relu = nn.ReLU(inplace=True) num_groups = planes // 8 if norm_fn == 'group': self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) if not stride == 1: self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) elif norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(planes) self.norm2 = nn.BatchNorm2d(planes) if not stride == 1: self.norm3 = nn.BatchNorm2d(planes) elif norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(planes) self.norm2 = nn.InstanceNorm2d(planes) if not stride == 1: self.norm3 = nn.InstanceNorm2d(planes) elif norm_fn == 'none': self.norm1 = nn.Sequential() self.norm2 = nn.Sequential() if not stride == 1: self.norm3 = nn.Sequential() if stride == 1: self.downsample = None else: self.downsample = nn.Sequential( nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3) def forward(self, x): y = x y = self.relu(self.norm1(self.conv1(y))) y = self.relu(self.norm2(self.conv2(y))) if self.downsample is not None: x = self.downsample(x) return self.relu(x+y) class BottleneckBlock(nn.Module): def __init__(self, in_planes, planes, norm_fn='group', stride=1): super(BottleneckBlock, self).__init__() self.conv1 = nn.Conv2d(in_planes, planes//4, kernel_size=1, padding=0) self.conv2 = nn.Conv2d(planes//4, planes//4, kernel_size=3, padding=1, stride=stride) self.conv3 = nn.Conv2d(planes//4, planes, kernel_size=1, padding=0) self.relu = nn.ReLU(inplace=True) num_groups = planes // 8 if norm_fn == 'group': self.norm1 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4) self.norm2 = nn.GroupNorm(num_groups=num_groups, num_channels=planes//4) self.norm3 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) if not stride == 1: self.norm4 = nn.GroupNorm(num_groups=num_groups, num_channels=planes) elif norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(planes//4) self.norm2 = nn.BatchNorm2d(planes//4) self.norm3 = nn.BatchNorm2d(planes) if not stride == 1: self.norm4 = nn.BatchNorm2d(planes) elif norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(planes//4) self.norm2 = nn.InstanceNorm2d(planes//4) self.norm3 = nn.InstanceNorm2d(planes) if not stride == 1: self.norm4 = nn.InstanceNorm2d(planes) elif norm_fn == 'none': self.norm1 = nn.Sequential() self.norm2 = nn.Sequential() self.norm3 = nn.Sequential() if not stride == 1: self.norm4 = nn.Sequential() if stride == 1: self.downsample = None else: self.downsample = nn.Sequential( nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm4) def forward(self, x): y = x y = self.relu(self.norm1(self.conv1(y))) y = self.relu(self.norm2(self.conv2(y))) y = self.relu(self.norm3(self.conv3(y))) if self.downsample is not None: x = self.downsample(x) return self.relu(x+y) class BasicEncoder(nn.Module): def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0): super(BasicEncoder, self).__init__() self.norm_fn = norm_fn if self.norm_fn == 'group': self.norm1 = nn.GroupNorm(num_groups=8, num_channels=64) elif self.norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(64) elif self.norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(64) elif self.norm_fn == 'none': self.norm1 = nn.Sequential() self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3) self.relu1 = nn.ReLU(inplace=True) self.in_planes = 64 self.layer1 = self._make_layer(64, stride=1) self.layer2 = self._make_layer(96, stride=2) self.layer3 = self._make_layer(128, stride=2) # output convolution self.conv2 = nn.Conv2d(128, output_dim, kernel_size=1) self.dropout = None if dropout > 0: self.dropout = nn.Dropout2d(p=dropout) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)): if m.weight is not None: nn.init.constant_(m.weight, 1) if m.bias is not None: nn.init.constant_(m.bias, 0) def _make_layer(self, dim, stride=1): layer1 = ResidualBlock(self.in_planes, dim, self.norm_fn, stride=stride) layer2 = ResidualBlock(dim, dim, self.norm_fn, stride=1) layers = (layer1, layer2) self.in_planes = dim return nn.Sequential(*layers) def forward(self, x): # if input is list, combine batch dimension is_list = isinstance(x, tuple) or isinstance(x, list) if is_list: batch_dim = x[0].shape[0] x = torch.cat(x, dim=0) x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.conv2(x) if self.training and self.dropout is not None: x = self.dropout(x) if is_list: x = torch.split(x, [batch_dim, batch_dim], dim=0) return x class SmallEncoder(nn.Module): def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0): super(SmallEncoder, self).__init__() self.norm_fn = norm_fn if self.norm_fn == 'group': self.norm1 = nn.GroupNorm(num_groups=8, num_channels=32) elif self.norm_fn == 'batch': self.norm1 = nn.BatchNorm2d(32) elif self.norm_fn == 'instance': self.norm1 = nn.InstanceNorm2d(32) elif self.norm_fn == 'none': self.norm1 = nn.Sequential() self.conv1 = nn.Conv2d(3, 32, kernel_size=7, stride=2, padding=3) self.relu1 = nn.ReLU(inplace=True) self.in_planes = 32 self.layer1 = self._make_layer(32, stride=1) self.layer2 = self._make_layer(64, stride=2) self.layer3 = self._make_layer(96, stride=2) self.dropout = None if dropout > 0: self.dropout = nn.Dropout2d(p=dropout) self.conv2 = nn.Conv2d(96, output_dim, kernel_size=1) for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)): if m.weight is not None: nn.init.constant_(m.weight, 1) if m.bias is not None: nn.init.constant_(m.bias, 0) def _make_layer(self, dim, stride=1): layer1 = BottleneckBlock(self.in_planes, dim, self.norm_fn, stride=stride) layer2 = BottleneckBlock(dim, dim, self.norm_fn, stride=1) layers = (layer1, layer2) self.in_planes = dim return nn.Sequential(*layers) def forward(self, x): # if input is list, combine batch dimension is_list = isinstance(x, tuple) or isinstance(x, list) if is_list: batch_dim = x[0].shape[0] x = torch.cat(x, dim=0) x = self.conv1(x) x = self.norm1(x) x = self.relu1(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.conv2(x) if self.training and self.dropout is not None: x = self.dropout(x) if is_list: x = torch.split(x, [batch_dim, batch_dim], dim=0) return x ================================================ FILE: propainter/RAFT/raft.py ================================================ import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from .update import BasicUpdateBlock, SmallUpdateBlock from .extractor import BasicEncoder, SmallEncoder from .corr import CorrBlock, AlternateCorrBlock from .utils.utils import bilinear_sampler, coords_grid, upflow8 try: autocast = torch.cuda.amp.autocast except: # dummy autocast for PyTorch < 1.6 class autocast: def __init__(self, enabled): pass def __enter__(self): pass def __exit__(self, *args): pass class RAFT(nn.Module): def __init__(self, args): super(RAFT, self).__init__() self.args = args if args.small: self.hidden_dim = hdim = 96 self.context_dim = cdim = 64 args.corr_levels = 4 args.corr_radius = 3 else: self.hidden_dim = hdim = 128 self.context_dim = cdim = 128 args.corr_levels = 4 args.corr_radius = 4 if 'dropout' not in args._get_kwargs(): args.dropout = 0 if 'alternate_corr' not in args._get_kwargs(): args.alternate_corr = False # feature network, context network, and update block if args.small: self.fnet = SmallEncoder(output_dim=128, norm_fn='instance', dropout=args.dropout) self.cnet = SmallEncoder(output_dim=hdim+cdim, norm_fn='none', dropout=args.dropout) self.update_block = SmallUpdateBlock(self.args, hidden_dim=hdim) else: self.fnet = BasicEncoder(output_dim=256, norm_fn='instance', dropout=args.dropout) self.cnet = BasicEncoder(output_dim=hdim+cdim, norm_fn='batch', dropout=args.dropout) self.update_block = BasicUpdateBlock(self.args, hidden_dim=hdim) def freeze_bn(self): for m in self.modules(): if isinstance(m, nn.BatchNorm2d): m.eval() def initialize_flow(self, img): """ Flow is represented as difference between two coordinate grids flow = coords1 - coords0""" N, C, H, W = img.shape coords0 = coords_grid(N, H//8, W//8).to(img.device) coords1 = coords_grid(N, H//8, W//8).to(img.device) # optical flow computed as difference: flow = coords1 - coords0 return coords0, coords1 def upsample_flow(self, flow, mask): """ Upsample flow field [H/8, W/8, 2] -> [H, W, 2] using convex combination """ N, _, H, W = flow.shape mask = mask.view(N, 1, 9, 8, 8, H, W) mask = torch.softmax(mask, dim=2) up_flow = F.unfold(8 * flow, [3,3], padding=1) up_flow = up_flow.view(N, 2, 9, 1, 1, H, W) up_flow = torch.sum(mask * up_flow, dim=2) up_flow = up_flow.permute(0, 1, 4, 2, 5, 3) return up_flow.reshape(N, 2, 8*H, 8*W) def forward(self, image1, image2, iters=12, flow_init=None, test_mode=True): """ Estimate optical flow between pair of frames """ # image1 = 2 * (image1 / 255.0) - 1.0 # image2 = 2 * (image2 / 255.0) - 1.0 image1 = image1.contiguous() image2 = image2.contiguous() hdim = self.hidden_dim cdim = self.context_dim # run the feature network with autocast(enabled=self.args.mixed_precision): fmap1, fmap2 = self.fnet([image1, image2]) fmap1 = fmap1.float() fmap2 = fmap2.float() if self.args.alternate_corr: corr_fn = AlternateCorrBlock(fmap1, fmap2, radius=self.args.corr_radius) else: corr_fn = CorrBlock(fmap1, fmap2, radius=self.args.corr_radius) # run the context network with autocast(enabled=self.args.mixed_precision): cnet = self.cnet(image1) net, inp = torch.split(cnet, [hdim, cdim], dim=1) net = torch.tanh(net) inp = torch.relu(inp) coords0, coords1 = self.initialize_flow(image1) if flow_init is not None: coords1 = coords1 + flow_init flow_predictions = [] for itr in range(iters): coords1 = coords1.detach() corr = corr_fn(coords1) # index correlation volume flow = coords1 - coords0 with autocast(enabled=self.args.mixed_precision): net, up_mask, delta_flow = self.update_block(net, inp, corr, flow) # F(t+1) = F(t) + \Delta(t) coords1 = coords1 + delta_flow # upsample predictions if up_mask is None: flow_up = upflow8(coords1 - coords0) else: flow_up = self.upsample_flow(coords1 - coords0, up_mask) flow_predictions.append(flow_up) if test_mode: return coords1 - coords0, flow_up return flow_predictions ================================================ FILE: propainter/RAFT/update.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F class FlowHead(nn.Module): def __init__(self, input_dim=128, hidden_dim=256): super(FlowHead, self).__init__() self.conv1 = nn.Conv2d(input_dim, hidden_dim, 3, padding=1) self.conv2 = nn.Conv2d(hidden_dim, 2, 3, padding=1) self.relu = nn.ReLU(inplace=True) def forward(self, x): return self.conv2(self.relu(self.conv1(x))) class ConvGRU(nn.Module): def __init__(self, hidden_dim=128, input_dim=192+128): super(ConvGRU, self).__init__() self.convz = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) self.convr = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) self.convq = nn.Conv2d(hidden_dim+input_dim, hidden_dim, 3, padding=1) def forward(self, h, x): hx = torch.cat([h, x], dim=1) z = torch.sigmoid(self.convz(hx)) r = torch.sigmoid(self.convr(hx)) q = torch.tanh(self.convq(torch.cat([r*h, x], dim=1))) h = (1-z) * h + z * q return h class SepConvGRU(nn.Module): def __init__(self, hidden_dim=128, input_dim=192+128): super(SepConvGRU, self).__init__() self.convz1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) self.convr1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) self.convq1 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (1,5), padding=(0,2)) self.convz2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) self.convr2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) self.convq2 = nn.Conv2d(hidden_dim+input_dim, hidden_dim, (5,1), padding=(2,0)) def forward(self, h, x): # horizontal hx = torch.cat([h, x], dim=1) z = torch.sigmoid(self.convz1(hx)) r = torch.sigmoid(self.convr1(hx)) q = torch.tanh(self.convq1(torch.cat([r*h, x], dim=1))) h = (1-z) * h + z * q # vertical hx = torch.cat([h, x], dim=1) z = torch.sigmoid(self.convz2(hx)) r = torch.sigmoid(self.convr2(hx)) q = torch.tanh(self.convq2(torch.cat([r*h, x], dim=1))) h = (1-z) * h + z * q return h class SmallMotionEncoder(nn.Module): def __init__(self, args): super(SmallMotionEncoder, self).__init__() cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2 self.convc1 = nn.Conv2d(cor_planes, 96, 1, padding=0) self.convf1 = nn.Conv2d(2, 64, 7, padding=3) self.convf2 = nn.Conv2d(64, 32, 3, padding=1) self.conv = nn.Conv2d(128, 80, 3, padding=1) def forward(self, flow, corr): cor = F.relu(self.convc1(corr)) flo = F.relu(self.convf1(flow)) flo = F.relu(self.convf2(flo)) cor_flo = torch.cat([cor, flo], dim=1) out = F.relu(self.conv(cor_flo)) return torch.cat([out, flow], dim=1) class BasicMotionEncoder(nn.Module): def __init__(self, args): super(BasicMotionEncoder, self).__init__() cor_planes = args.corr_levels * (2*args.corr_radius + 1)**2 self.convc1 = nn.Conv2d(cor_planes, 256, 1, padding=0) self.convc2 = nn.Conv2d(256, 192, 3, padding=1) self.convf1 = nn.Conv2d(2, 128, 7, padding=3) self.convf2 = nn.Conv2d(128, 64, 3, padding=1) self.conv = nn.Conv2d(64+192, 128-2, 3, padding=1) def forward(self, flow, corr): cor = F.relu(self.convc1(corr)) cor = F.relu(self.convc2(cor)) flo = F.relu(self.convf1(flow)) flo = F.relu(self.convf2(flo)) cor_flo = torch.cat([cor, flo], dim=1) out = F.relu(self.conv(cor_flo)) return torch.cat([out, flow], dim=1) class SmallUpdateBlock(nn.Module): def __init__(self, args, hidden_dim=96): super(SmallUpdateBlock, self).__init__() self.encoder = SmallMotionEncoder(args) self.gru = ConvGRU(hidden_dim=hidden_dim, input_dim=82+64) self.flow_head = FlowHead(hidden_dim, hidden_dim=128) def forward(self, net, inp, corr, flow): motion_features = self.encoder(flow, corr) inp = torch.cat([inp, motion_features], dim=1) net = self.gru(net, inp) delta_flow = self.flow_head(net) return net, None, delta_flow class BasicUpdateBlock(nn.Module): def __init__(self, args, hidden_dim=128, input_dim=128): super(BasicUpdateBlock, self).__init__() self.args = args self.encoder = BasicMotionEncoder(args) self.gru = SepConvGRU(hidden_dim=hidden_dim, input_dim=128+hidden_dim) self.flow_head = FlowHead(hidden_dim, hidden_dim=256) self.mask = nn.Sequential( nn.Conv2d(128, 256, 3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(256, 64*9, 1, padding=0)) def forward(self, net, inp, corr, flow, upsample=True): motion_features = self.encoder(flow, corr) inp = torch.cat([inp, motion_features], dim=1) net = self.gru(net, inp) delta_flow = self.flow_head(net) # scale mask to balence gradients mask = .25 * self.mask(net) return net, mask, delta_flow ================================================ FILE: propainter/RAFT/utils/__init__.py ================================================ from .flow_viz import flow_to_image from .frame_utils import writeFlow ================================================ FILE: propainter/RAFT/utils/augmentor.py ================================================ import numpy as np import random import math from PIL import Image import cv2 cv2.setNumThreads(0) cv2.ocl.setUseOpenCL(False) import torch from torchvision.transforms import ColorJitter import torch.nn.functional as F class FlowAugmentor: def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=True): # spatial augmentation params self.crop_size = crop_size self.min_scale = min_scale self.max_scale = max_scale self.spatial_aug_prob = 0.8 self.stretch_prob = 0.8 self.max_stretch = 0.2 # flip augmentation params self.do_flip = do_flip self.h_flip_prob = 0.5 self.v_flip_prob = 0.1 # photometric augmentation params self.photo_aug = ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.5/3.14) self.asymmetric_color_aug_prob = 0.2 self.eraser_aug_prob = 0.5 def color_transform(self, img1, img2): """ Photometric augmentation """ # asymmetric if np.random.rand() < self.asymmetric_color_aug_prob: img1 = np.array(self.photo_aug(Image.fromarray(img1)), dtype=np.uint8) img2 = np.array(self.photo_aug(Image.fromarray(img2)), dtype=np.uint8) # symmetric else: image_stack = np.concatenate([img1, img2], axis=0) image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8) img1, img2 = np.split(image_stack, 2, axis=0) return img1, img2 def eraser_transform(self, img1, img2, bounds=[50, 100]): """ Occlusion augmentation """ ht, wd = img1.shape[:2] if np.random.rand() < self.eraser_aug_prob: mean_color = np.mean(img2.reshape(-1, 3), axis=0) for _ in range(np.random.randint(1, 3)): x0 = np.random.randint(0, wd) y0 = np.random.randint(0, ht) dx = np.random.randint(bounds[0], bounds[1]) dy = np.random.randint(bounds[0], bounds[1]) img2[y0:y0+dy, x0:x0+dx, :] = mean_color return img1, img2 def spatial_transform(self, img1, img2, flow): # randomly sample scale ht, wd = img1.shape[:2] min_scale = np.maximum( (self.crop_size[0] + 8) / float(ht), (self.crop_size[1] + 8) / float(wd)) scale = 2 ** np.random.uniform(self.min_scale, self.max_scale) scale_x = scale scale_y = scale if np.random.rand() < self.stretch_prob: scale_x *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch) scale_y *= 2 ** np.random.uniform(-self.max_stretch, self.max_stretch) scale_x = np.clip(scale_x, min_scale, None) scale_y = np.clip(scale_y, min_scale, None) if np.random.rand() < self.spatial_aug_prob: # rescale the images img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) flow = cv2.resize(flow, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) flow = flow * [scale_x, scale_y] if self.do_flip: if np.random.rand() < self.h_flip_prob: # h-flip img1 = img1[:, ::-1] img2 = img2[:, ::-1] flow = flow[:, ::-1] * [-1.0, 1.0] if np.random.rand() < self.v_flip_prob: # v-flip img1 = img1[::-1, :] img2 = img2[::-1, :] flow = flow[::-1, :] * [1.0, -1.0] y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0]) x0 = np.random.randint(0, img1.shape[1] - self.crop_size[1]) img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] return img1, img2, flow def __call__(self, img1, img2, flow): img1, img2 = self.color_transform(img1, img2) img1, img2 = self.eraser_transform(img1, img2) img1, img2, flow = self.spatial_transform(img1, img2, flow) img1 = np.ascontiguousarray(img1) img2 = np.ascontiguousarray(img2) flow = np.ascontiguousarray(flow) return img1, img2, flow class SparseFlowAugmentor: def __init__(self, crop_size, min_scale=-0.2, max_scale=0.5, do_flip=False): # spatial augmentation params self.crop_size = crop_size self.min_scale = min_scale self.max_scale = max_scale self.spatial_aug_prob = 0.8 self.stretch_prob = 0.8 self.max_stretch = 0.2 # flip augmentation params self.do_flip = do_flip self.h_flip_prob = 0.5 self.v_flip_prob = 0.1 # photometric augmentation params self.photo_aug = ColorJitter(brightness=0.3, contrast=0.3, saturation=0.3, hue=0.3/3.14) self.asymmetric_color_aug_prob = 0.2 self.eraser_aug_prob = 0.5 def color_transform(self, img1, img2): image_stack = np.concatenate([img1, img2], axis=0) image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8) img1, img2 = np.split(image_stack, 2, axis=0) return img1, img2 def eraser_transform(self, img1, img2): ht, wd = img1.shape[:2] if np.random.rand() < self.eraser_aug_prob: mean_color = np.mean(img2.reshape(-1, 3), axis=0) for _ in range(np.random.randint(1, 3)): x0 = np.random.randint(0, wd) y0 = np.random.randint(0, ht) dx = np.random.randint(50, 100) dy = np.random.randint(50, 100) img2[y0:y0+dy, x0:x0+dx, :] = mean_color return img1, img2 def resize_sparse_flow_map(self, flow, valid, fx=1.0, fy=1.0): ht, wd = flow.shape[:2] coords = np.meshgrid(np.arange(wd), np.arange(ht)) coords = np.stack(coords, axis=-1) coords = coords.reshape(-1, 2).astype(np.float32) flow = flow.reshape(-1, 2).astype(np.float32) valid = valid.reshape(-1).astype(np.float32) coords0 = coords[valid>=1] flow0 = flow[valid>=1] ht1 = int(round(ht * fy)) wd1 = int(round(wd * fx)) coords1 = coords0 * [fx, fy] flow1 = flow0 * [fx, fy] xx = np.round(coords1[:,0]).astype(np.int32) yy = np.round(coords1[:,1]).astype(np.int32) v = (xx > 0) & (xx < wd1) & (yy > 0) & (yy < ht1) xx = xx[v] yy = yy[v] flow1 = flow1[v] flow_img = np.zeros([ht1, wd1, 2], dtype=np.float32) valid_img = np.zeros([ht1, wd1], dtype=np.int32) flow_img[yy, xx] = flow1 valid_img[yy, xx] = 1 return flow_img, valid_img def spatial_transform(self, img1, img2, flow, valid): # randomly sample scale ht, wd = img1.shape[:2] min_scale = np.maximum( (self.crop_size[0] + 1) / float(ht), (self.crop_size[1] + 1) / float(wd)) scale = 2 ** np.random.uniform(self.min_scale, self.max_scale) scale_x = np.clip(scale, min_scale, None) scale_y = np.clip(scale, min_scale, None) if np.random.rand() < self.spatial_aug_prob: # rescale the images img1 = cv2.resize(img1, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) img2 = cv2.resize(img2, None, fx=scale_x, fy=scale_y, interpolation=cv2.INTER_LINEAR) flow, valid = self.resize_sparse_flow_map(flow, valid, fx=scale_x, fy=scale_y) if self.do_flip: if np.random.rand() < 0.5: # h-flip img1 = img1[:, ::-1] img2 = img2[:, ::-1] flow = flow[:, ::-1] * [-1.0, 1.0] valid = valid[:, ::-1] margin_y = 20 margin_x = 50 y0 = np.random.randint(0, img1.shape[0] - self.crop_size[0] + margin_y) x0 = np.random.randint(-margin_x, img1.shape[1] - self.crop_size[1] + margin_x) y0 = np.clip(y0, 0, img1.shape[0] - self.crop_size[0]) x0 = np.clip(x0, 0, img1.shape[1] - self.crop_size[1]) img1 = img1[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] img2 = img2[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] flow = flow[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] valid = valid[y0:y0+self.crop_size[0], x0:x0+self.crop_size[1]] return img1, img2, flow, valid def __call__(self, img1, img2, flow, valid): img1, img2 = self.color_transform(img1, img2) img1, img2 = self.eraser_transform(img1, img2) img1, img2, flow, valid = self.spatial_transform(img1, img2, flow, valid) img1 = np.ascontiguousarray(img1) img2 = np.ascontiguousarray(img2) flow = np.ascontiguousarray(flow) valid = np.ascontiguousarray(valid) return img1, img2, flow, valid ================================================ FILE: propainter/RAFT/utils/flow_viz.py ================================================ # Flow visualization code used from https://github.com/tomrunia/OpticalFlow_Visualization # MIT License # # Copyright (c) 2018 Tom Runia # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to conditions. # # Author: Tom Runia # Date Created: 2018-08-03 import numpy as np def make_colorwheel(): """ Generates a color wheel for optical flow visualization as presented in: Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007) URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf Code follows the original C++ source code of Daniel Scharstein. Code follows the the Matlab source code of Deqing Sun. Returns: np.ndarray: Color wheel """ RY = 15 YG = 6 GC = 4 CB = 11 BM = 13 MR = 6 ncols = RY + YG + GC + CB + BM + MR colorwheel = np.zeros((ncols, 3)) col = 0 # RY colorwheel[0:RY, 0] = 255 colorwheel[0:RY, 1] = np.floor(255*np.arange(0,RY)/RY) col = col+RY # YG colorwheel[col:col+YG, 0] = 255 - np.floor(255*np.arange(0,YG)/YG) colorwheel[col:col+YG, 1] = 255 col = col+YG # GC colorwheel[col:col+GC, 1] = 255 colorwheel[col:col+GC, 2] = np.floor(255*np.arange(0,GC)/GC) col = col+GC # CB colorwheel[col:col+CB, 1] = 255 - np.floor(255*np.arange(CB)/CB) colorwheel[col:col+CB, 2] = 255 col = col+CB # BM colorwheel[col:col+BM, 2] = 255 colorwheel[col:col+BM, 0] = np.floor(255*np.arange(0,BM)/BM) col = col+BM # MR colorwheel[col:col+MR, 2] = 255 - np.floor(255*np.arange(MR)/MR) colorwheel[col:col+MR, 0] = 255 return colorwheel def flow_uv_to_colors(u, v, convert_to_bgr=False): """ Applies the flow color wheel to (possibly clipped) flow components u and v. According to the C++ source code of Daniel Scharstein According to the Matlab source code of Deqing Sun Args: u (np.ndarray): Input horizontal flow of shape [H,W] v (np.ndarray): Input vertical flow of shape [H,W] convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False. Returns: np.ndarray: Flow visualization image of shape [H,W,3] """ flow_image = np.zeros((u.shape[0], u.shape[1], 3), np.uint8) colorwheel = make_colorwheel() # shape [55x3] ncols = colorwheel.shape[0] rad = np.sqrt(np.square(u) + np.square(v)) a = np.arctan2(-v, -u)/np.pi fk = (a+1) / 2*(ncols-1) k0 = np.floor(fk).astype(np.int32) k1 = k0 + 1 k1[k1 == ncols] = 0 f = fk - k0 for i in range(colorwheel.shape[1]): tmp = colorwheel[:,i] col0 = tmp[k0] / 255.0 col1 = tmp[k1] / 255.0 col = (1-f)*col0 + f*col1 idx = (rad <= 1) col[idx] = 1 - rad[idx] * (1-col[idx]) col[~idx] = col[~idx] * 0.75 # out of range # Note the 2-i => BGR instead of RGB ch_idx = 2-i if convert_to_bgr else i flow_image[:,:,ch_idx] = np.floor(255 * col) return flow_image def flow_to_image(flow_uv, clip_flow=None, convert_to_bgr=False): """ Expects a two dimensional flow image of shape. Args: flow_uv (np.ndarray): Flow UV image of shape [H,W,2] clip_flow (float, optional): Clip maximum of flow values. Defaults to None. convert_to_bgr (bool, optional): Convert output image to BGR. Defaults to False. Returns: np.ndarray: Flow visualization image of shape [H,W,3] """ assert flow_uv.ndim == 3, 'input flow must have three dimensions' assert flow_uv.shape[2] == 2, 'input flow must have shape [H,W,2]' if clip_flow is not None: flow_uv = np.clip(flow_uv, 0, clip_flow) u = flow_uv[:,:,0] v = flow_uv[:,:,1] rad = np.sqrt(np.square(u) + np.square(v)) rad_max = np.max(rad) epsilon = 1e-5 u = u / (rad_max + epsilon) v = v / (rad_max + epsilon) return flow_uv_to_colors(u, v, convert_to_bgr) ================================================ FILE: propainter/RAFT/utils/flow_viz_pt.py ================================================ # Flow visualization code adapted from https://github.com/tomrunia/OpticalFlow_Visualization import torch torch.pi = torch.acos(torch.zeros(1)).item() * 2 # which is 3.1415927410125732 @torch.no_grad() def flow_to_image(flow: torch.Tensor) -> torch.Tensor: """ Converts a flow to an RGB image. Args: flow (Tensor): Flow of shape (N, 2, H, W) or (2, H, W) and dtype torch.float. Returns: img (Tensor): Image Tensor of dtype uint8 where each color corresponds to a given flow direction. Shape is (N, 3, H, W) or (3, H, W) depending on the input. """ if flow.dtype != torch.float: raise ValueError(f"Flow should be of dtype torch.float, got {flow.dtype}.") orig_shape = flow.shape if flow.ndim == 3: flow = flow[None] # Add batch dim if flow.ndim != 4 or flow.shape[1] != 2: raise ValueError(f"Input flow should have shape (2, H, W) or (N, 2, H, W), got {orig_shape}.") max_norm = torch.sum(flow**2, dim=1).sqrt().max() epsilon = torch.finfo((flow).dtype).eps normalized_flow = flow / (max_norm + epsilon) img = _normalized_flow_to_image(normalized_flow) if len(orig_shape) == 3: img = img[0] # Remove batch dim return img @torch.no_grad() def _normalized_flow_to_image(normalized_flow: torch.Tensor) -> torch.Tensor: """ Converts a batch of normalized flow to an RGB image. Args: normalized_flow (torch.Tensor): Normalized flow tensor of shape (N, 2, H, W) Returns: img (Tensor(N, 3, H, W)): Flow visualization image of dtype uint8. """ N, _, H, W = normalized_flow.shape device = normalized_flow.device flow_image = torch.zeros((N, 3, H, W), dtype=torch.uint8, device=device) colorwheel = _make_colorwheel().to(device) # shape [55x3] num_cols = colorwheel.shape[0] norm = torch.sum(normalized_flow**2, dim=1).sqrt() a = torch.atan2(-normalized_flow[:, 1, :, :], -normalized_flow[:, 0, :, :]) / torch.pi fk = (a + 1) / 2 * (num_cols - 1) k0 = torch.floor(fk).to(torch.long) k1 = k0 + 1 k1[k1 == num_cols] = 0 f = fk - k0 for c in range(colorwheel.shape[1]): tmp = colorwheel[:, c] col0 = tmp[k0] / 255.0 col1 = tmp[k1] / 255.0 col = (1 - f) * col0 + f * col1 col = 1 - norm * (1 - col) flow_image[:, c, :, :] = torch.floor(255. * col) return flow_image @torch.no_grad() def _make_colorwheel() -> torch.Tensor: """ Generates a color wheel for optical flow visualization as presented in: Baker et al. "A Database and Evaluation Methodology for Optical Flow" (ICCV, 2007) URL: http://vision.middlebury.edu/flow/flowEval-iccv07.pdf. Returns: colorwheel (Tensor[55, 3]): Colorwheel Tensor. """ RY = 15 YG = 6 GC = 4 CB = 11 BM = 13 MR = 6 ncols = RY + YG + GC + CB + BM + MR colorwheel = torch.zeros((ncols, 3)) col = 0 # RY colorwheel[0:RY, 0] = 255 colorwheel[0:RY, 1] = torch.floor(255. * torch.arange(0., RY) / RY) col = col + RY # YG colorwheel[col : col + YG, 0] = 255 - torch.floor(255. * torch.arange(0., YG) / YG) colorwheel[col : col + YG, 1] = 255 col = col + YG # GC colorwheel[col : col + GC, 1] = 255 colorwheel[col : col + GC, 2] = torch.floor(255. * torch.arange(0., GC) / GC) col = col + GC # CB colorwheel[col : col + CB, 1] = 255 - torch.floor(255. * torch.arange(CB) / CB) colorwheel[col : col + CB, 2] = 255 col = col + CB # BM colorwheel[col : col + BM, 2] = 255 colorwheel[col : col + BM, 0] = torch.floor(255. * torch.arange(0., BM) / BM) col = col + BM # MR colorwheel[col : col + MR, 2] = 255 - torch.floor(255. * torch.arange(MR) / MR) colorwheel[col : col + MR, 0] = 255 return colorwheel ================================================ FILE: propainter/RAFT/utils/frame_utils.py ================================================ import numpy as np from PIL import Image from os.path import * import re import cv2 cv2.setNumThreads(0) cv2.ocl.setUseOpenCL(False) TAG_CHAR = np.array([202021.25], np.float32) def readFlow(fn): """ Read .flo file in Middlebury format""" # Code adapted from: # http://stackoverflow.com/questions/28013200/reading-middlebury-flow-files-with-python-bytes-array-numpy # WARNING: this will work on little-endian architectures (eg Intel x86) only! # print 'fn = %s'%(fn) with open(fn, 'rb') as f: magic = np.fromfile(f, np.float32, count=1) if 202021.25 != magic: print('Magic number incorrect. Invalid .flo file') return None else: w = np.fromfile(f, np.int32, count=1) h = np.fromfile(f, np.int32, count=1) # print 'Reading %d x %d flo file\n' % (w, h) data = np.fromfile(f, np.float32, count=2*int(w)*int(h)) # Reshape data into 3D array (columns, rows, bands) # The reshape here is for visualization, the original code is (w,h,2) return np.resize(data, (int(h), int(w), 2)) def readPFM(file): file = open(file, 'rb') color = None width = None height = None scale = None endian = None header = file.readline().rstrip() if header == b'PF': color = True elif header == b'Pf': color = False else: raise Exception('Not a PFM file.') dim_match = re.match(rb'^(\d+)\s(\d+)\s$', file.readline()) if dim_match: width, height = map(int, dim_match.groups()) else: raise Exception('Malformed PFM header.') scale = float(file.readline().rstrip()) if scale < 0: # little-endian endian = '<' scale = -scale else: endian = '>' # big-endian data = np.fromfile(file, endian + 'f') shape = (height, width, 3) if color else (height, width) data = np.reshape(data, shape) data = np.flipud(data) return data def writeFlow(filename,uv,v=None): """ Write optical flow to file. If v is None, uv is assumed to contain both u and v channels, stacked in depth. Original code by Deqing Sun, adapted from Daniel Scharstein. """ nBands = 2 if v is None: assert(uv.ndim == 3) assert(uv.shape[2] == 2) u = uv[:,:,0] v = uv[:,:,1] else: u = uv assert(u.shape == v.shape) height,width = u.shape f = open(filename,'wb') # write the header f.write(TAG_CHAR) np.array(width).astype(np.int32).tofile(f) np.array(height).astype(np.int32).tofile(f) # arrange into matrix form tmp = np.zeros((height, width*nBands)) tmp[:,np.arange(width)*2] = u tmp[:,np.arange(width)*2 + 1] = v tmp.astype(np.float32).tofile(f) f.close() def readFlowKITTI(filename): flow = cv2.imread(filename, cv2.IMREAD_ANYDEPTH|cv2.IMREAD_COLOR) flow = flow[:,:,::-1].astype(np.float32) flow, valid = flow[:, :, :2], flow[:, :, 2] flow = (flow - 2**15) / 64.0 return flow, valid def readDispKITTI(filename): disp = cv2.imread(filename, cv2.IMREAD_ANYDEPTH) / 256.0 valid = disp > 0.0 flow = np.stack([-disp, np.zeros_like(disp)], -1) return flow, valid def writeFlowKITTI(filename, uv): uv = 64.0 * uv + 2**15 valid = np.ones([uv.shape[0], uv.shape[1], 1]) uv = np.concatenate([uv, valid], axis=-1).astype(np.uint16) cv2.imwrite(filename, uv[..., ::-1]) def read_gen(file_name, pil=False): ext = splitext(file_name)[-1] if ext == '.png' or ext == '.jpeg' or ext == '.ppm' or ext == '.jpg': return Image.open(file_name) elif ext == '.bin' or ext == '.raw': return np.load(file_name) elif ext == '.flo': return readFlow(file_name).astype(np.float32) elif ext == '.pfm': flow = readPFM(file_name).astype(np.float32) if len(flow.shape) == 2: return flow else: return flow[:, :, :-1] return [] ================================================ FILE: propainter/RAFT/utils/utils.py ================================================ import torch import torch.nn.functional as F import numpy as np from scipy import interpolate class InputPadder: """ Pads images such that dimensions are divisible by 8 """ def __init__(self, dims, mode='sintel'): self.ht, self.wd = dims[-2:] pad_ht = (((self.ht // 8) + 1) * 8 - self.ht) % 8 pad_wd = (((self.wd // 8) + 1) * 8 - self.wd) % 8 if mode == 'sintel': self._pad = [pad_wd//2, pad_wd - pad_wd//2, pad_ht//2, pad_ht - pad_ht//2] else: self._pad = [pad_wd//2, pad_wd - pad_wd//2, 0, pad_ht] def pad(self, *inputs): return [F.pad(x, self._pad, mode='replicate') for x in inputs] def unpad(self,x): ht, wd = x.shape[-2:] c = [self._pad[2], ht-self._pad[3], self._pad[0], wd-self._pad[1]] return x[..., c[0]:c[1], c[2]:c[3]] def forward_interpolate(flow): flow = flow.detach().cpu().numpy() dx, dy = flow[0], flow[1] ht, wd = dx.shape x0, y0 = np.meshgrid(np.arange(wd), np.arange(ht)) x1 = x0 + dx y1 = y0 + dy x1 = x1.reshape(-1) y1 = y1.reshape(-1) dx = dx.reshape(-1) dy = dy.reshape(-1) valid = (x1 > 0) & (x1 < wd) & (y1 > 0) & (y1 < ht) x1 = x1[valid] y1 = y1[valid] dx = dx[valid] dy = dy[valid] flow_x = interpolate.griddata( (x1, y1), dx, (x0, y0), method='nearest', fill_value=0) flow_y = interpolate.griddata( (x1, y1), dy, (x0, y0), method='nearest', fill_value=0) flow = np.stack([flow_x, flow_y], axis=0) return torch.from_numpy(flow).float() def bilinear_sampler(img, coords, mode='bilinear', mask=False): """ Wrapper for grid_sample, uses pixel coordinates """ H, W = img.shape[-2:] xgrid, ygrid = coords.split([1,1], dim=-1) xgrid = 2*xgrid/(W-1) - 1 ygrid = 2*ygrid/(H-1) - 1 grid = torch.cat([xgrid, ygrid], dim=-1) img = F.grid_sample(img, grid, align_corners=True) if mask: mask = (xgrid > -1) & (ygrid > -1) & (xgrid < 1) & (ygrid < 1) return img, mask.float() return img def coords_grid(batch, ht, wd): coords = torch.meshgrid(torch.arange(ht), torch.arange(wd)) coords = torch.stack(coords[::-1], dim=0).float() return coords[None].repeat(batch, 1, 1, 1) def upflow8(flow, mode='bilinear'): new_size = (8 * flow.shape[2], 8 * flow.shape[3]) return 8 * F.interpolate(flow, size=new_size, mode=mode, align_corners=True) ================================================ FILE: propainter/core/__init__.py ================================================ ================================================ FILE: propainter/core/dataset.py ================================================ import os import json import random import cv2 from PIL import Image import numpy as np import torch import torchvision.transforms as transforms from ..utils.file_client import FileClient from ..utils.img_util import imfrombytes from ..utils.flow_util import resize_flow, flowread from ..core.utils import (create_random_shape_with_random_motion, Stack, ToTorchFormatTensor, GroupRandomHorizontalFlip,GroupRandomHorizontalFlowFlip) class TrainDataset(torch.utils.data.Dataset): def __init__(self, args: dict): self.args = args self.video_root = args['video_root'] self.flow_root = args['flow_root'] self.num_local_frames = args['num_local_frames'] self.num_ref_frames = args['num_ref_frames'] self.size = self.w, self.h = (args['w'], args['h']) self.load_flow = args['load_flow'] if self.load_flow: assert os.path.exists(self.flow_root) json_path = os.path.join('./datasets', args['name'], 'train.json') with open(json_path, 'r') as f: self.video_train_dict = json.load(f) self.video_names = sorted(list(self.video_train_dict.keys())) # self.video_names = sorted(os.listdir(self.video_root)) self.video_dict = {} self.frame_dict = {} for v in self.video_names: frame_list = sorted(os.listdir(os.path.join(self.video_root, v))) v_len = len(frame_list) if v_len > self.num_local_frames + self.num_ref_frames: self.video_dict[v] = v_len self.frame_dict[v] = frame_list self.video_names = list(self.video_dict.keys()) # update names self._to_tensors = transforms.Compose([ Stack(), ToTorchFormatTensor(), ]) self.file_client = FileClient('disk') def __len__(self): return len(self.video_names) def _sample_index(self, length, sample_length, num_ref_frame=3): complete_idx_set = list(range(length)) pivot = random.randint(0, length - sample_length) local_idx = complete_idx_set[pivot:pivot + sample_length] remain_idx = list(set(complete_idx_set) - set(local_idx)) ref_index = sorted(random.sample(remain_idx, num_ref_frame)) return local_idx + ref_index def __getitem__(self, index): video_name = self.video_names[index] # create masks all_masks = create_random_shape_with_random_motion( self.video_dict[video_name], imageHeight=self.h, imageWidth=self.w) # create sample index selected_index = self._sample_index(self.video_dict[video_name], self.num_local_frames, self.num_ref_frames) # read video frames frames = [] masks = [] flows_f, flows_b = [], [] for idx in selected_index: frame_list = self.frame_dict[video_name] img_path = os.path.join(self.video_root, video_name, frame_list[idx]) img_bytes = self.file_client.get(img_path, 'img') img = imfrombytes(img_bytes, float32=False) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = cv2.resize(img, self.size, interpolation=cv2.INTER_LINEAR) img = Image.fromarray(img) frames.append(img) masks.append(all_masks[idx]) if len(frames) <= self.num_local_frames-1 and self.load_flow: current_n = frame_list[idx][:-4] next_n = frame_list[idx+1][:-4] flow_f_path = os.path.join(self.flow_root, video_name, f'{current_n}_{next_n}_f.flo') flow_b_path = os.path.join(self.flow_root, video_name, f'{next_n}_{current_n}_b.flo') flow_f = flowread(flow_f_path, quantize=False) flow_b = flowread(flow_b_path, quantize=False) flow_f = resize_flow(flow_f, self.h, self.w) flow_b = resize_flow(flow_b, self.h, self.w) flows_f.append(flow_f) flows_b.append(flow_b) if len(frames) == self.num_local_frames: # random reverse if random.random() < 0.5: frames.reverse() masks.reverse() if self.load_flow: flows_f.reverse() flows_b.reverse() flows_ = flows_f flows_f = flows_b flows_b = flows_ if self.load_flow: frames, flows_f, flows_b = GroupRandomHorizontalFlowFlip()(frames, flows_f, flows_b) else: frames = GroupRandomHorizontalFlip()(frames) # normalizate, to tensors frame_tensors = self._to_tensors(frames) * 2.0 - 1.0 mask_tensors = self._to_tensors(masks) if self.load_flow: flows_f = np.stack(flows_f, axis=-1) # H W 2 T-1 flows_b = np.stack(flows_b, axis=-1) flows_f = torch.from_numpy(flows_f).permute(3, 2, 0, 1).contiguous().float() flows_b = torch.from_numpy(flows_b).permute(3, 2, 0, 1).contiguous().float() # img [-1,1] mask [0,1] if self.load_flow: return frame_tensors, mask_tensors, flows_f, flows_b, video_name else: return frame_tensors, mask_tensors, 'None', 'None', video_name class TestDataset(torch.utils.data.Dataset): def __init__(self, args): self.args = args self.size = self.w, self.h = args['size'] self.video_root = args['video_root'] self.mask_root = args['mask_root'] self.flow_root = args['flow_root'] self.load_flow = args['load_flow'] if self.load_flow: assert os.path.exists(self.flow_root) self.video_names = sorted(os.listdir(self.mask_root)) self.video_dict = {} self.frame_dict = {} for v in self.video_names: frame_list = sorted(os.listdir(os.path.join(self.video_root, v))) v_len = len(frame_list) self.video_dict[v] = v_len self.frame_dict[v] = frame_list self._to_tensors = transforms.Compose([ Stack(), ToTorchFormatTensor(), ]) self.file_client = FileClient('disk') def __len__(self): return len(self.video_names) def __getitem__(self, index): video_name = self.video_names[index] selected_index = list(range(self.video_dict[video_name])) # read video frames frames = [] masks = [] flows_f, flows_b = [], [] for idx in selected_index: frame_list = self.frame_dict[video_name] frame_path = os.path.join(self.video_root, video_name, frame_list[idx]) img_bytes = self.file_client.get(frame_path, 'input') img = imfrombytes(img_bytes, float32=False) img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = cv2.resize(img, self.size, interpolation=cv2.INTER_LINEAR) img = Image.fromarray(img) frames.append(img) mask_path = os.path.join(self.mask_root, video_name, str(idx).zfill(5) + '.png') mask = Image.open(mask_path).resize(self.size, Image.NEAREST).convert('L') # origin: 0 indicates missing. now: 1 indicates missing mask = np.asarray(mask) m = np.array(mask > 0).astype(np.uint8) m = cv2.dilate(m, cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3)), iterations=4) mask = Image.fromarray(m * 255) masks.append(mask) if len(frames) <= len(selected_index)-1 and self.load_flow: current_n = frame_list[idx][:-4] next_n = frame_list[idx+1][:-4] flow_f_path = os.path.join(self.flow_root, video_name, f'{current_n}_{next_n}_f.flo') flow_b_path = os.path.join(self.flow_root, video_name, f'{next_n}_{current_n}_b.flo') flow_f = flowread(flow_f_path, quantize=False) flow_b = flowread(flow_b_path, quantize=False) flow_f = resize_flow(flow_f, self.h, self.w) flow_b = resize_flow(flow_b, self.h, self.w) flows_f.append(flow_f) flows_b.append(flow_b) # normalizate, to tensors frames_PIL = [np.array(f).astype(np.uint8) for f in frames] frame_tensors = self._to_tensors(frames) * 2.0 - 1.0 mask_tensors = self._to_tensors(masks) if self.load_flow: flows_f = np.stack(flows_f, axis=-1) # H W 2 T-1 flows_b = np.stack(flows_b, axis=-1) flows_f = torch.from_numpy(flows_f).permute(3, 2, 0, 1).contiguous().float() flows_b = torch.from_numpy(flows_b).permute(3, 2, 0, 1).contiguous().float() if self.load_flow: return frame_tensors, mask_tensors, flows_f, flows_b, video_name, frames_PIL else: return frame_tensors, mask_tensors, 'None', 'None', video_name ================================================ FILE: propainter/core/dist.py ================================================ import os import torch def get_world_size(): """Find OMPI world size without calling mpi functions :rtype: int """ if os.environ.get('PMI_SIZE') is not None: return int(os.environ.get('PMI_SIZE') or 1) elif os.environ.get('OMPI_COMM_WORLD_SIZE') is not None: return int(os.environ.get('OMPI_COMM_WORLD_SIZE') or 1) else: return torch.cuda.device_count() def get_global_rank(): """Find OMPI world rank without calling mpi functions :rtype: int """ if os.environ.get('PMI_RANK') is not None: return int(os.environ.get('PMI_RANK') or 0) elif os.environ.get('OMPI_COMM_WORLD_RANK') is not None: return int(os.environ.get('OMPI_COMM_WORLD_RANK') or 0) else: return 0 def get_local_rank(): """Find OMPI local rank without calling mpi functions :rtype: int """ if os.environ.get('MPI_LOCALRANKID') is not None: return int(os.environ.get('MPI_LOCALRANKID') or 0) elif os.environ.get('OMPI_COMM_WORLD_LOCAL_RANK') is not None: return int(os.environ.get('OMPI_COMM_WORLD_LOCAL_RANK') or 0) else: return 0 def get_master_ip(): if os.environ.get('AZ_BATCH_MASTER_NODE') is not None: return os.environ.get('AZ_BATCH_MASTER_NODE').split(':')[0] elif os.environ.get('AZ_BATCHAI_MPI_MASTER_NODE') is not None: return os.environ.get('AZ_BATCHAI_MPI_MASTER_NODE') else: return "127.0.0.1" ================================================ FILE: propainter/core/loss.py ================================================ import torch import torch.nn as nn import lpips from ..model.vgg_arch import VGGFeatureExtractor class PerceptualLoss(nn.Module): """Perceptual loss with commonly used style loss. Args: layer_weights (dict): The weight for each layer of vgg feature. Here is an example: {'conv5_4': 1.}, which means the conv5_4 feature layer (before relu5_4) will be extracted with weight 1.0 in calculting losses. vgg_type (str): The type of vgg network used as feature extractor. Default: 'vgg19'. use_input_norm (bool): If True, normalize the input image in vgg. Default: True. range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. Default: False. perceptual_weight (float): If `perceptual_weight > 0`, the perceptual loss will be calculated and the loss will multiplied by the weight. Default: 1.0. style_weight (float): If `style_weight > 0`, the style loss will be calculated and the loss will multiplied by the weight. Default: 0. criterion (str): Criterion used for perceptual loss. Default: 'l1'. """ def __init__(self, layer_weights, vgg_type='vgg19', use_input_norm=True, range_norm=False, perceptual_weight=1.0, style_weight=0., criterion='l1'): super(PerceptualLoss, self).__init__() self.perceptual_weight = perceptual_weight self.style_weight = style_weight self.layer_weights = layer_weights self.vgg = VGGFeatureExtractor( layer_name_list=list(layer_weights.keys()), vgg_type=vgg_type, use_input_norm=use_input_norm, range_norm=range_norm) self.criterion_type = criterion if self.criterion_type == 'l1': self.criterion = torch.nn.L1Loss() elif self.criterion_type == 'l2': self.criterion = torch.nn.L2loss() elif self.criterion_type == 'mse': self.criterion = torch.nn.MSELoss(reduction='mean') elif self.criterion_type == 'fro': self.criterion = None else: raise NotImplementedError(f'{criterion} criterion has not been supported.') def forward(self, x, gt): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). gt (Tensor): Ground-truth tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ # extract vgg features x_features = self.vgg(x) gt_features = self.vgg(gt.detach()) # calculate perceptual loss if self.perceptual_weight > 0: percep_loss = 0 for k in x_features.keys(): if self.criterion_type == 'fro': percep_loss += torch.norm(x_features[k] - gt_features[k], p='fro') * self.layer_weights[k] else: percep_loss += self.criterion(x_features[k], gt_features[k]) * self.layer_weights[k] percep_loss *= self.perceptual_weight else: percep_loss = None # calculate style loss if self.style_weight > 0: style_loss = 0 for k in x_features.keys(): if self.criterion_type == 'fro': style_loss += torch.norm( self._gram_mat(x_features[k]) - self._gram_mat(gt_features[k]), p='fro') * self.layer_weights[k] else: style_loss += self.criterion(self._gram_mat(x_features[k]), self._gram_mat( gt_features[k])) * self.layer_weights[k] style_loss *= self.style_weight else: style_loss = None return percep_loss, style_loss def _gram_mat(self, x): """Calculate Gram matrix. Args: x (torch.Tensor): Tensor with shape of (n, c, h, w). Returns: torch.Tensor: Gram matrix. """ n, c, h, w = x.size() features = x.view(n, c, w * h) features_t = features.transpose(1, 2) gram = features.bmm(features_t) / (c * h * w) return gram class LPIPSLoss(nn.Module): def __init__(self, loss_weight=1.0, use_input_norm=True, range_norm=False,): super(LPIPSLoss, self).__init__() self.perceptual = lpips.LPIPS(net="vgg", spatial=False).eval() self.loss_weight = loss_weight self.use_input_norm = use_input_norm self.range_norm = range_norm if self.use_input_norm: # the mean is for image with range [0, 1] self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) # the std is for image with range [0, 1] self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) def forward(self, pred, target): if self.range_norm: pred = (pred + 1) / 2 target = (target + 1) / 2 if self.use_input_norm: pred = (pred - self.mean) / self.std target = (target - self.mean) / self.std lpips_loss = self.perceptual(target.contiguous(), pred.contiguous()) return self.loss_weight * lpips_loss.mean(), None class AdversarialLoss(nn.Module): r""" Adversarial loss https://arxiv.org/abs/1711.10337 """ def __init__(self, type='nsgan', target_real_label=1.0, target_fake_label=0.0): r""" type = nsgan | lsgan | hinge """ super(AdversarialLoss, self).__init__() self.type = type self.register_buffer('real_label', torch.tensor(target_real_label)) self.register_buffer('fake_label', torch.tensor(target_fake_label)) if type == 'nsgan': self.criterion = nn.BCELoss() elif type == 'lsgan': self.criterion = nn.MSELoss() elif type == 'hinge': self.criterion = nn.ReLU() def __call__(self, outputs, is_real, is_disc=None): if self.type == 'hinge': if is_disc: if is_real: outputs = -outputs return self.criterion(1 + outputs).mean() else: return (-outputs).mean() else: labels = (self.real_label if is_real else self.fake_label).expand_as(outputs) loss = self.criterion(outputs, labels) return loss ================================================ FILE: propainter/core/lr_scheduler.py ================================================ """ LR scheduler from BasicSR https://github.com/xinntao/BasicSR """ import math from collections import Counter from torch.optim.lr_scheduler import _LRScheduler class MultiStepRestartLR(_LRScheduler): """ MultiStep with restarts learning rate scheme. Args: optimizer (torch.nn.optimizer): Torch optimizer. milestones (list): Iterations that will decrease learning rate. gamma (float): Decrease ratio. Default: 0.1. restarts (list): Restart iterations. Default: [0]. restart_weights (list): Restart weights at each restart iteration. Default: [1]. last_epoch (int): Used in _LRScheduler. Default: -1. """ def __init__(self, optimizer, milestones, gamma=0.1, restarts=(0, ), restart_weights=(1, ), last_epoch=-1): self.milestones = Counter(milestones) self.gamma = gamma self.restarts = restarts self.restart_weights = restart_weights assert len(self.restarts) == len( self.restart_weights), 'restarts and their weights do not match.' super(MultiStepRestartLR, self).__init__(optimizer, last_epoch) def get_lr(self): if self.last_epoch in self.restarts: weight = self.restart_weights[self.restarts.index(self.last_epoch)] return [ group['initial_lr'] * weight for group in self.optimizer.param_groups ] if self.last_epoch not in self.milestones: return [group['lr'] for group in self.optimizer.param_groups] return [ group['lr'] * self.gamma**self.milestones[self.last_epoch] for group in self.optimizer.param_groups ] def get_position_from_periods(iteration, cumulative_period): """Get the position from a period list. It will return the index of the right-closest number in the period list. For example, the cumulative_period = [100, 200, 300, 400], if iteration == 50, return 0; if iteration == 210, return 2; if iteration == 300, return 2. Args: iteration (int): Current iteration. cumulative_period (list[int]): Cumulative period list. Returns: int: The position of the right-closest number in the period list. """ for i, period in enumerate(cumulative_period): if iteration <= period: return i class CosineAnnealingRestartLR(_LRScheduler): """ Cosine annealing with restarts learning rate scheme. An example of config: periods = [10, 10, 10, 10] restart_weights = [1, 0.5, 0.5, 0.5] eta_min=1e-7 It has four cycles, each has 10 iterations. At 10th, 20th, 30th, the scheduler will restart with the weights in restart_weights. Args: optimizer (torch.nn.optimizer): Torch optimizer. periods (list): Period for each cosine anneling cycle. restart_weights (list): Restart weights at each restart iteration. Default: [1]. eta_min (float): The mimimum lr. Default: 0. last_epoch (int): Used in _LRScheduler. Default: -1. """ def __init__(self, optimizer, periods, restart_weights=(1, ), eta_min=1e-7, last_epoch=-1): self.periods = periods self.restart_weights = restart_weights self.eta_min = eta_min assert (len(self.periods) == len(self.restart_weights) ), 'periods and restart_weights should have the same length.' self.cumulative_period = [ sum(self.periods[0:i + 1]) for i in range(0, len(self.periods)) ] super(CosineAnnealingRestartLR, self).__init__(optimizer, last_epoch) def get_lr(self): idx = get_position_from_periods(self.last_epoch, self.cumulative_period) current_weight = self.restart_weights[idx] nearest_restart = 0 if idx == 0 else self.cumulative_period[idx - 1] current_period = self.periods[idx] return [ self.eta_min + current_weight * 0.5 * (base_lr - self.eta_min) * (1 + math.cos(math.pi * ( (self.last_epoch - nearest_restart) / current_period))) for base_lr in self.base_lrs ] ================================================ FILE: propainter/core/metrics.py ================================================ import numpy as np # from skimage import measure from skimage.metrics import structural_similarity as compare_ssim from scipy import linalg import torch import torch.nn as nn import torch.nn.functional as F from .utils import to_tensors def calculate_epe(flow1, flow2): """Calculate End point errors.""" epe = torch.sum((flow1 - flow2)**2, dim=1).sqrt() epe = epe.view(-1) return epe.mean().item() def calculate_psnr(img1, img2): """Calculate PSNR (Peak Signal-to-Noise Ratio). Ref: https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio Args: img1 (ndarray): Images with range [0, 255]. img2 (ndarray): Images with range [0, 255]. Returns: float: psnr result. """ assert img1.shape == img2.shape, \ (f'Image shapes are differnet: {img1.shape}, {img2.shape}.') mse = np.mean((img1 - img2)**2) if mse == 0: return float('inf') return 20. * np.log10(255. / np.sqrt(mse)) def calc_psnr_and_ssim(img1, img2): """Calculate PSNR and SSIM for images. img1: ndarray, range [0, 255] img2: ndarray, range [0, 255] """ img1 = img1.astype(np.float64) img2 = img2.astype(np.float64) psnr = calculate_psnr(img1, img2) ssim = compare_ssim(img1, img2, data_range=255, multichannel=True, win_size=65, channel_axis=2) return psnr, ssim ########################### # I3D models ########################### def init_i3d_model(i3d_model_path): print(f"[Loading I3D model from {i3d_model_path} for FID score ..]") i3d_model = InceptionI3d(400, in_channels=3, final_endpoint='Logits') i3d_model.load_state_dict(torch.load(i3d_model_path)) i3d_model.to(torch.device('cuda:0')) return i3d_model def calculate_i3d_activations(video1, video2, i3d_model, device): """Calculate VFID metric. video1: list[PIL.Image] video2: list[PIL.Image] """ video1 = to_tensors()(video1).unsqueeze(0).to(device) video2 = to_tensors()(video2).unsqueeze(0).to(device) video1_activations = get_i3d_activations( video1, i3d_model).cpu().numpy().flatten() video2_activations = get_i3d_activations( video2, i3d_model).cpu().numpy().flatten() return video1_activations, video2_activations def calculate_vfid(real_activations, fake_activations): """ Given two distribution of features, compute the FID score between them Params: real_activations: list[ndarray] fake_activations: list[ndarray] """ m1 = np.mean(real_activations, axis=0) m2 = np.mean(fake_activations, axis=0) s1 = np.cov(real_activations, rowvar=False) s2 = np.cov(fake_activations, rowvar=False) return calculate_frechet_distance(m1, s1, m2, s2) def calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6): """Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(C_1 + C_2 - 2*sqrt(C_1*C_2)). Stable version by Dougal J. Sutherland. Params: -- mu1 : Numpy array containing the activations of a layer of the inception net (like returned by the function 'get_predictions') for generated samples. -- mu2 : The sample mean over activations, precalculated on an representive data set. -- sigma1: The covariance matrix over activations for generated samples. -- sigma2: The covariance matrix over activations, precalculated on an representive data set. Returns: -- : The Frechet Distance. """ mu1 = np.atleast_1d(mu1) mu2 = np.atleast_1d(mu2) sigma1 = np.atleast_2d(sigma1) sigma2 = np.atleast_2d(sigma2) assert mu1.shape == mu2.shape, \ 'Training and test mean vectors have different lengths' assert sigma1.shape == sigma2.shape, \ 'Training and test covariances have different dimensions' diff = mu1 - mu2 # Product might be almost singular covmean, _ = linalg.sqrtm(sigma1.dot(sigma2), disp=False) if not np.isfinite(covmean).all(): msg = ('fid calculation produces singular product; ' 'adding %s to diagonal of cov estimates') % eps print(msg) offset = np.eye(sigma1.shape[0]) * eps covmean = linalg.sqrtm((sigma1 + offset).dot(sigma2 + offset)) # Numerical error might give slight imaginary component if np.iscomplexobj(covmean): if not np.allclose(np.diagonal(covmean).imag, 0, atol=1e-3): m = np.max(np.abs(covmean.imag)) raise ValueError('Imaginary component {}'.format(m)) covmean = covmean.real tr_covmean = np.trace(covmean) return (diff.dot(diff) + np.trace(sigma1) + # NOQA np.trace(sigma2) - 2 * tr_covmean) def get_i3d_activations(batched_video, i3d_model, target_endpoint='Logits', flatten=True, grad_enabled=False): """ Get features from i3d model and flatten them to 1d feature, valid target endpoints are defined in InceptionI3d.VALID_ENDPOINTS VALID_ENDPOINTS = ( 'Conv3d_1a_7x7', 'MaxPool3d_2a_3x3', 'Conv3d_2b_1x1', 'Conv3d_2c_3x3', 'MaxPool3d_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'MaxPool3d_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool3d_5a_2x2', 'Mixed_5b', 'Mixed_5c', 'Logits', 'Predictions', ) """ with torch.set_grad_enabled(grad_enabled): feat = i3d_model.extract_features(batched_video.transpose(1, 2), target_endpoint) if flatten: feat = feat.view(feat.size(0), -1) return feat # This code is from https://github.com/piergiaj/pytorch-i3d/blob/master/pytorch_i3d.py # I only fix flake8 errors and do some cleaning here class MaxPool3dSamePadding(nn.MaxPool3d): def compute_pad(self, dim, s): if s % self.stride[dim] == 0: return max(self.kernel_size[dim] - self.stride[dim], 0) else: return max(self.kernel_size[dim] - (s % self.stride[dim]), 0) def forward(self, x): # compute 'same' padding (batch, channel, t, h, w) = x.size() pad_t = self.compute_pad(0, t) pad_h = self.compute_pad(1, h) pad_w = self.compute_pad(2, w) pad_t_f = pad_t // 2 pad_t_b = pad_t - pad_t_f pad_h_f = pad_h // 2 pad_h_b = pad_h - pad_h_f pad_w_f = pad_w // 2 pad_w_b = pad_w - pad_w_f pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b) x = F.pad(x, pad) return super(MaxPool3dSamePadding, self).forward(x) class Unit3D(nn.Module): def __init__(self, in_channels, output_channels, kernel_shape=(1, 1, 1), stride=(1, 1, 1), padding=0, activation_fn=F.relu, use_batch_norm=True, use_bias=False, name='unit_3d'): """Initializes Unit3D module.""" super(Unit3D, self).__init__() self._output_channels = output_channels self._kernel_shape = kernel_shape self._stride = stride self._use_batch_norm = use_batch_norm self._activation_fn = activation_fn self._use_bias = use_bias self.name = name self.padding = padding self.conv3d = nn.Conv3d( in_channels=in_channels, out_channels=self._output_channels, kernel_size=self._kernel_shape, stride=self._stride, padding=0, # we always want padding to be 0 here. We will # dynamically pad based on input size in forward function bias=self._use_bias) if self._use_batch_norm: self.bn = nn.BatchNorm3d(self._output_channels, eps=0.001, momentum=0.01) def compute_pad(self, dim, s): if s % self._stride[dim] == 0: return max(self._kernel_shape[dim] - self._stride[dim], 0) else: return max(self._kernel_shape[dim] - (s % self._stride[dim]), 0) def forward(self, x): # compute 'same' padding (batch, channel, t, h, w) = x.size() pad_t = self.compute_pad(0, t) pad_h = self.compute_pad(1, h) pad_w = self.compute_pad(2, w) pad_t_f = pad_t // 2 pad_t_b = pad_t - pad_t_f pad_h_f = pad_h // 2 pad_h_b = pad_h - pad_h_f pad_w_f = pad_w // 2 pad_w_b = pad_w - pad_w_f pad = (pad_w_f, pad_w_b, pad_h_f, pad_h_b, pad_t_f, pad_t_b) x = F.pad(x, pad) x = self.conv3d(x) if self._use_batch_norm: x = self.bn(x) if self._activation_fn is not None: x = self._activation_fn(x) return x class InceptionModule(nn.Module): def __init__(self, in_channels, out_channels, name): super(InceptionModule, self).__init__() self.b0 = Unit3D(in_channels=in_channels, output_channels=out_channels[0], kernel_shape=[1, 1, 1], padding=0, name=name + '/Branch_0/Conv3d_0a_1x1') self.b1a = Unit3D(in_channels=in_channels, output_channels=out_channels[1], kernel_shape=[1, 1, 1], padding=0, name=name + '/Branch_1/Conv3d_0a_1x1') self.b1b = Unit3D(in_channels=out_channels[1], output_channels=out_channels[2], kernel_shape=[3, 3, 3], name=name + '/Branch_1/Conv3d_0b_3x3') self.b2a = Unit3D(in_channels=in_channels, output_channels=out_channels[3], kernel_shape=[1, 1, 1], padding=0, name=name + '/Branch_2/Conv3d_0a_1x1') self.b2b = Unit3D(in_channels=out_channels[3], output_channels=out_channels[4], kernel_shape=[3, 3, 3], name=name + '/Branch_2/Conv3d_0b_3x3') self.b3a = MaxPool3dSamePadding(kernel_size=[3, 3, 3], stride=(1, 1, 1), padding=0) self.b3b = Unit3D(in_channels=in_channels, output_channels=out_channels[5], kernel_shape=[1, 1, 1], padding=0, name=name + '/Branch_3/Conv3d_0b_1x1') self.name = name def forward(self, x): b0 = self.b0(x) b1 = self.b1b(self.b1a(x)) b2 = self.b2b(self.b2a(x)) b3 = self.b3b(self.b3a(x)) return torch.cat([b0, b1, b2, b3], dim=1) class InceptionI3d(nn.Module): """Inception-v1 I3D architecture. The model is introduced in: Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset Joao Carreira, Andrew Zisserman https://arxiv.org/pdf/1705.07750v1.pdf. See also the Inception architecture, introduced in: Going deeper with convolutions Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich. http://arxiv.org/pdf/1409.4842v1.pdf. """ # Endpoints of the model in order. During construction, all the endpoints up # to a designated `final_endpoint` are returned in a dictionary as the # second return value. VALID_ENDPOINTS = ( 'Conv3d_1a_7x7', 'MaxPool3d_2a_3x3', 'Conv3d_2b_1x1', 'Conv3d_2c_3x3', 'MaxPool3d_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'MaxPool3d_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool3d_5a_2x2', 'Mixed_5b', 'Mixed_5c', 'Logits', 'Predictions', ) def __init__(self, num_classes=400, spatial_squeeze=True, final_endpoint='Logits', name='inception_i3d', in_channels=3, dropout_keep_prob=0.5): """Initializes I3D model instance. Args: num_classes: The number of outputs in the logit layer (default 400, which matches the Kinetics dataset). spatial_squeeze: Whether to squeeze the spatial dimensions for the logits before returning (default True). final_endpoint: The model contains many possible endpoints. `final_endpoint` specifies the last endpoint for the model to be built up to. In addition to the output at `final_endpoint`, all the outputs at endpoints up to `final_endpoint` will also be returned, in a dictionary. `final_endpoint` must be one of InceptionI3d.VALID_ENDPOINTS (default 'Logits'). name: A string (optional). The name of this module. Raises: ValueError: if `final_endpoint` is not recognized. """ if final_endpoint not in self.VALID_ENDPOINTS: raise ValueError('Unknown final endpoint %s' % final_endpoint) super(InceptionI3d, self).__init__() self._num_classes = num_classes self._spatial_squeeze = spatial_squeeze self._final_endpoint = final_endpoint self.logits = None if self._final_endpoint not in self.VALID_ENDPOINTS: raise ValueError('Unknown final endpoint %s' % self._final_endpoint) self.end_points = {} end_point = 'Conv3d_1a_7x7' self.end_points[end_point] = Unit3D(in_channels=in_channels, output_channels=64, kernel_shape=[7, 7, 7], stride=(2, 2, 2), padding=(3, 3, 3), name=name + end_point) if self._final_endpoint == end_point: return end_point = 'MaxPool3d_2a_3x3' self.end_points[end_point] = MaxPool3dSamePadding( kernel_size=[1, 3, 3], stride=(1, 2, 2), padding=0) if self._final_endpoint == end_point: return end_point = 'Conv3d_2b_1x1' self.end_points[end_point] = Unit3D(in_channels=64, output_channels=64, kernel_shape=[1, 1, 1], padding=0, name=name + end_point) if self._final_endpoint == end_point: return end_point = 'Conv3d_2c_3x3' self.end_points[end_point] = Unit3D(in_channels=64, output_channels=192, kernel_shape=[3, 3, 3], padding=1, name=name + end_point) if self._final_endpoint == end_point: return end_point = 'MaxPool3d_3a_3x3' self.end_points[end_point] = MaxPool3dSamePadding( kernel_size=[1, 3, 3], stride=(1, 2, 2), padding=0) if self._final_endpoint == end_point: return end_point = 'Mixed_3b' self.end_points[end_point] = InceptionModule(192, [64, 96, 128, 16, 32, 32], name + end_point) if self._final_endpoint == end_point: return end_point = 'Mixed_3c' self.end_points[end_point] = InceptionModule( 256, [128, 128, 192, 32, 96, 64], name + end_point) if self._final_endpoint == end_point: return end_point = 'MaxPool3d_4a_3x3' self.end_points[end_point] = MaxPool3dSamePadding( kernel_size=[3, 3, 3], stride=(2, 2, 2), padding=0) if self._final_endpoint == end_point: return end_point = 'Mixed_4b' self.end_points[end_point] = InceptionModule( 128 + 192 + 96 + 64, [192, 96, 208, 16, 48, 64], name + end_point) if self._final_endpoint == end_point: return end_point = 'Mixed_4c' self.end_points[end_point] = InceptionModule( 192 + 208 + 48 + 64, [160, 112, 224, 24, 64, 64], name + end_point) if self._final_endpoint == end_point: return end_point = 'Mixed_4d' self.end_points[end_point] = InceptionModule( 160 + 224 + 64 + 64, [128, 128, 256, 24, 64, 64], name + end_point) if self._final_endpoint == end_point: return end_point = 'Mixed_4e' self.end_points[end_point] = InceptionModule( 128 + 256 + 64 + 64, [112, 144, 288, 32, 64, 64], name + end_point) if self._final_endpoint == end_point: return end_point = 'Mixed_4f' self.end_points[end_point] = InceptionModule( 112 + 288 + 64 + 64, [256, 160, 320, 32, 128, 128], name + end_point) if self._final_endpoint == end_point: return end_point = 'MaxPool3d_5a_2x2' self.end_points[end_point] = MaxPool3dSamePadding( kernel_size=[2, 2, 2], stride=(2, 2, 2), padding=0) if self._final_endpoint == end_point: return end_point = 'Mixed_5b' self.end_points[end_point] = InceptionModule( 256 + 320 + 128 + 128, [256, 160, 320, 32, 128, 128], name + end_point) if self._final_endpoint == end_point: return end_point = 'Mixed_5c' self.end_points[end_point] = InceptionModule( 256 + 320 + 128 + 128, [384, 192, 384, 48, 128, 128], name + end_point) if self._final_endpoint == end_point: return end_point = 'Logits' self.avg_pool = nn.AvgPool3d(kernel_size=[2, 7, 7], stride=(1, 1, 1)) self.dropout = nn.Dropout(dropout_keep_prob) self.logits = Unit3D(in_channels=384 + 384 + 128 + 128, output_channels=self._num_classes, kernel_shape=[1, 1, 1], padding=0, activation_fn=None, use_batch_norm=False, use_bias=True, name='logits') self.build() def replace_logits(self, num_classes): self._num_classes = num_classes self.logits = Unit3D(in_channels=384 + 384 + 128 + 128, output_channels=self._num_classes, kernel_shape=[1, 1, 1], padding=0, activation_fn=None, use_batch_norm=False, use_bias=True, name='logits') def build(self): for k in self.end_points.keys(): self.add_module(k, self.end_points[k]) def forward(self, x): for end_point in self.VALID_ENDPOINTS: if end_point in self.end_points: x = self._modules[end_point]( x) # use _modules to work with dataparallel x = self.logits(self.dropout(self.avg_pool(x))) if self._spatial_squeeze: logits = x.squeeze(3).squeeze(3) # logits is batch X time X classes, which is what we want to work with return logits def extract_features(self, x, target_endpoint='Logits'): for end_point in self.VALID_ENDPOINTS: if end_point in self.end_points: x = self._modules[end_point](x) if end_point == target_endpoint: break if target_endpoint == 'Logits': return x.mean(4).mean(3).mean(2) else: return x ================================================ FILE: propainter/core/prefetch_dataloader.py ================================================ import queue as Queue import threading import torch from torch.utils.data import DataLoader class PrefetchGenerator(threading.Thread): """A general prefetch generator. Ref: https://stackoverflow.com/questions/7323664/python-generator-pre-fetch Args: generator: Python generator. num_prefetch_queue (int): Number of prefetch queue. """ def __init__(self, generator, num_prefetch_queue): threading.Thread.__init__(self) self.queue = Queue.Queue(num_prefetch_queue) self.generator = generator self.daemon = True self.start() def run(self): for item in self.generator: self.queue.put(item) self.queue.put(None) def __next__(self): next_item = self.queue.get() if next_item is None: raise StopIteration return next_item def __iter__(self): return self class PrefetchDataLoader(DataLoader): """Prefetch version of dataloader. Ref: https://github.com/IgorSusmelj/pytorch-styleguide/issues/5# TODO: Need to test on single gpu and ddp (multi-gpu). There is a known issue in ddp. Args: num_prefetch_queue (int): Number of prefetch queue. kwargs (dict): Other arguments for dataloader. """ def __init__(self, num_prefetch_queue, **kwargs): self.num_prefetch_queue = num_prefetch_queue super(PrefetchDataLoader, self).__init__(**kwargs) def __iter__(self): return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue) class CPUPrefetcher(): """CPU prefetcher. Args: loader: Dataloader. """ def __init__(self, loader): self.ori_loader = loader self.loader = iter(loader) def next(self): try: return next(self.loader) except StopIteration: return None def reset(self): self.loader = iter(self.ori_loader) class CUDAPrefetcher(): """CUDA prefetcher. Ref: https://github.com/NVIDIA/apex/issues/304# It may consums more GPU memory. Args: loader: Dataloader. opt (dict): Options. """ def __init__(self, loader, opt): self.ori_loader = loader self.loader = iter(loader) self.opt = opt self.stream = torch.cuda.Stream() self.device = torch.device('cuda' if opt['num_gpu'] != 0 else 'cpu') self.preload() def preload(self): try: self.batch = next(self.loader) # self.batch is a dict except StopIteration: self.batch = None return None # put tensors to gpu with torch.cuda.stream(self.stream): for k, v in self.batch.items(): if torch.is_tensor(v): self.batch[k] = self.batch[k].to(device=self.device, non_blocking=True) def next(self): torch.cuda.current_stream().wait_stream(self.stream) batch = self.batch self.preload() return batch def reset(self): self.loader = iter(self.ori_loader) self.preload() ================================================ FILE: propainter/core/trainer.py ================================================ import os import glob import logging import importlib from tqdm import tqdm import torch import torch.nn as nn import torch.nn.functional as F from .prefetch_dataloader import PrefetchDataLoader, CPUPrefetcher from torch.utils.data.distributed import DistributedSampler from torch.nn.parallel import DistributedDataParallel as DDP import torchvision from torch.utils.tensorboard import SummaryWriter from .lr_scheduler import MultiStepRestartLR, CosineAnnealingRestartLR from .loss import AdversarialLoss, PerceptualLoss, LPIPSLoss from .dataset import TrainDataset from ..model.modules.flow_comp_raft import RAFT_bi, FlowLoss, EdgeLoss from ..model.recurrent_flow_completion import RecurrentFlowCompleteNet from ..RAFT.utils.flow_viz_pt import flow_to_image class Trainer: def __init__(self, config): self.config = config self.epoch = 0 self.iteration = 0 self.num_local_frames = config['train_data_loader']['num_local_frames'] self.num_ref_frames = config['train_data_loader']['num_ref_frames'] # setup data set and data loader self.train_dataset = TrainDataset(config['train_data_loader']) self.train_sampler = None self.train_args = config['trainer'] if config['distributed']: self.train_sampler = DistributedSampler( self.train_dataset, num_replicas=config['world_size'], rank=config['global_rank']) dataloader_args = dict( dataset=self.train_dataset, batch_size=self.train_args['batch_size'] // config['world_size'], shuffle=(self.train_sampler is None), num_workers=self.train_args['num_workers'], sampler=self.train_sampler, drop_last=True) self.train_loader = PrefetchDataLoader(self.train_args['num_prefetch_queue'], **dataloader_args) self.prefetcher = CPUPrefetcher(self.train_loader) # set loss functions self.adversarial_loss = AdversarialLoss(type=self.config['losses']['GAN_LOSS']) self.adversarial_loss = self.adversarial_loss.to(self.config['device']) self.l1_loss = nn.L1Loss() # self.perc_loss = PerceptualLoss( # layer_weights={'conv3_4': 0.25, 'conv4_4': 0.25, 'conv5_4': 0.5}, # use_input_norm=True, # range_norm=True, # criterion='l1' # ).to(self.config['device']) if self.config['losses']['perceptual_weight'] > 0: self.perc_loss = LPIPSLoss(use_input_norm=True, range_norm=True).to(self.config['device']) # self.flow_comp_loss = FlowCompletionLoss().to(self.config['device']) # self.flow_comp_loss = FlowCompletionLoss(self.config['device']) # set raft self.fix_raft = RAFT_bi(device = self.config['device']) self.fix_flow_complete = RecurrentFlowCompleteNet('weights/recurrent_flow_completion.pth') for p in self.fix_flow_complete.parameters(): p.requires_grad = False self.fix_flow_complete.to(self.config['device']) self.fix_flow_complete.eval() # self.flow_loss = FlowLoss() # setup models including generator and discriminator net = importlib.import_module('model.' + config['model']['net']) self.netG = net.InpaintGenerator() # print(self.netG) self.netG = self.netG.to(self.config['device']) if not self.config['model'].get('no_dis', False): if self.config['model'].get('dis_2d', False): self.netD = net.Discriminator_2D( in_channels=3, use_sigmoid=config['losses']['GAN_LOSS'] != 'hinge') else: self.netD = net.Discriminator( in_channels=3, use_sigmoid=config['losses']['GAN_LOSS'] != 'hinge') self.netD = self.netD.to(self.config['device']) self.interp_mode = self.config['model']['interp_mode'] # setup optimizers and schedulers self.setup_optimizers() self.setup_schedulers() self.load() if config['distributed']: self.netG = DDP(self.netG, device_ids=[self.config['local_rank']], output_device=self.config['local_rank'], broadcast_buffers=True, find_unused_parameters=True) if not self.config['model']['no_dis']: self.netD = DDP(self.netD, device_ids=[self.config['local_rank']], output_device=self.config['local_rank'], broadcast_buffers=True, find_unused_parameters=False) # set summary writer self.dis_writer = None self.gen_writer = None self.summary = {} if self.config['global_rank'] == 0 or (not config['distributed']): if not self.config['model']['no_dis']: self.dis_writer = SummaryWriter( os.path.join(config['save_dir'], 'dis')) self.gen_writer = SummaryWriter( os.path.join(config['save_dir'], 'gen')) def setup_optimizers(self): """Set up optimizers.""" backbone_params = [] for name, param in self.netG.named_parameters(): if param.requires_grad: backbone_params.append(param) else: print(f'Params {name} will not be optimized.') optim_params = [ { 'params': backbone_params, 'lr': self.config['trainer']['lr'] }, ] self.optimG = torch.optim.Adam(optim_params, betas=(self.config['trainer']['beta1'], self.config['trainer']['beta2'])) if not self.config['model']['no_dis']: self.optimD = torch.optim.Adam( self.netD.parameters(), lr=self.config['trainer']['lr'], betas=(self.config['trainer']['beta1'], self.config['trainer']['beta2'])) def setup_schedulers(self): """Set up schedulers.""" scheduler_opt = self.config['trainer']['scheduler'] scheduler_type = scheduler_opt.pop('type') if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']: self.scheG = MultiStepRestartLR( self.optimG, milestones=scheduler_opt['milestones'], gamma=scheduler_opt['gamma']) if not self.config['model']['no_dis']: self.scheD = MultiStepRestartLR( self.optimD, milestones=scheduler_opt['milestones'], gamma=scheduler_opt['gamma']) elif scheduler_type == 'CosineAnnealingRestartLR': self.scheG = CosineAnnealingRestartLR( self.optimG, periods=scheduler_opt['periods'], restart_weights=scheduler_opt['restart_weights'], eta_min=scheduler_opt['eta_min']) if not self.config['model']['no_dis']: self.scheD = CosineAnnealingRestartLR( self.optimD, periods=scheduler_opt['periods'], restart_weights=scheduler_opt['restart_weights'], eta_min=scheduler_opt['eta_min']) else: raise NotImplementedError( f'Scheduler {scheduler_type} is not implemented yet.') def update_learning_rate(self): """Update learning rate.""" self.scheG.step() if not self.config['model']['no_dis']: self.scheD.step() def get_lr(self): """Get current learning rate.""" return self.optimG.param_groups[0]['lr'] def add_summary(self, writer, name, val): """Add tensorboard summary.""" if name not in self.summary: self.summary[name] = 0 self.summary[name] += val n = self.train_args['log_freq'] if writer is not None and self.iteration % n == 0: writer.add_scalar(name, self.summary[name] / n, self.iteration) self.summary[name] = 0 def load(self): """Load netG (and netD).""" # get the latest checkpoint model_path = self.config['save_dir'] # TODO: add resume name if os.path.isfile(os.path.join(model_path, 'latest.ckpt')): latest_epoch = open(os.path.join(model_path, 'latest.ckpt'), 'r').read().splitlines()[-1] else: ckpts = [ os.path.basename(i).split('.pth')[0] for i in glob.glob(os.path.join(model_path, '*.pth')) ] ckpts.sort() latest_epoch = ckpts[-1][4:] if len(ckpts) > 0 else None if latest_epoch is not None: gen_path = os.path.join(model_path, f'gen_{int(latest_epoch):06d}.pth') dis_path = os.path.join(model_path, f'dis_{int(latest_epoch):06d}.pth') opt_path = os.path.join(model_path, f'opt_{int(latest_epoch):06d}.pth') if self.config['global_rank'] == 0: print(f'Loading model from {gen_path}...') dataG = torch.load(gen_path, map_location=self.config['device']) self.netG.load_state_dict(dataG) if not self.config['model']['no_dis'] and self.config['model']['load_d']: dataD = torch.load(dis_path, map_location=self.config['device']) self.netD.load_state_dict(dataD) data_opt = torch.load(opt_path, map_location=self.config['device']) self.optimG.load_state_dict(data_opt['optimG']) # self.scheG.load_state_dict(data_opt['scheG']) if not self.config['model']['no_dis'] and self.config['model']['load_d']: self.optimD.load_state_dict(data_opt['optimD']) # self.scheD.load_state_dict(data_opt['scheD']) self.epoch = data_opt['epoch'] self.iteration = data_opt['iteration'] else: gen_path = self.config['trainer'].get('gen_path', None) dis_path = self.config['trainer'].get('dis_path', None) opt_path = self.config['trainer'].get('opt_path', None) if gen_path is not None: if self.config['global_rank'] == 0: print(f'Loading Gen-Net from {gen_path}...') dataG = torch.load(gen_path, map_location=self.config['device']) self.netG.load_state_dict(dataG) if dis_path is not None and not self.config['model']['no_dis'] and self.config['model']['load_d']: if self.config['global_rank'] == 0: print(f'Loading Dis-Net from {dis_path}...') dataD = torch.load(dis_path, map_location=self.config['device']) self.netD.load_state_dict(dataD) if opt_path is not None: data_opt = torch.load(opt_path, map_location=self.config['device']) self.optimG.load_state_dict(data_opt['optimG']) self.scheG.load_state_dict(data_opt['scheG']) if not self.config['model']['no_dis'] and self.config['model']['load_d']: self.optimD.load_state_dict(data_opt['optimD']) self.scheD.load_state_dict(data_opt['scheD']) else: if self.config['global_rank'] == 0: print('Warnning: There is no trained model found.' 'An initialized model will be used.') def save(self, it): """Save parameters every eval_epoch""" if self.config['global_rank'] == 0: # configure path gen_path = os.path.join(self.config['save_dir'], f'gen_{it:06d}.pth') dis_path = os.path.join(self.config['save_dir'], f'dis_{it:06d}.pth') opt_path = os.path.join(self.config['save_dir'], f'opt_{it:06d}.pth') print(f'\nsaving model to {gen_path} ...') # remove .module for saving if isinstance(self.netG, torch.nn.DataParallel) or isinstance(self.netG, DDP): netG = self.netG.module if not self.config['model']['no_dis']: netD = self.netD.module else: netG = self.netG if not self.config['model']['no_dis']: netD = self.netD # save checkpoints torch.save(netG.state_dict(), gen_path) if not self.config['model']['no_dis']: torch.save(netD.state_dict(), dis_path) torch.save( { 'epoch': self.epoch, 'iteration': self.iteration, 'optimG': self.optimG.state_dict(), 'optimD': self.optimD.state_dict(), 'scheG': self.scheG.state_dict(), 'scheD': self.scheD.state_dict() }, opt_path) else: torch.save( { 'epoch': self.epoch, 'iteration': self.iteration, 'optimG': self.optimG.state_dict(), 'scheG': self.scheG.state_dict() }, opt_path) latest_path = os.path.join(self.config['save_dir'], 'latest.ckpt') os.system(f"echo {it:06d} > {latest_path}") def train(self): """training entry""" pbar = range(int(self.train_args['iterations'])) if self.config['global_rank'] == 0: pbar = tqdm(pbar, initial=self.iteration, dynamic_ncols=True, smoothing=0.01) os.makedirs('logs', exist_ok=True) logging.basicConfig( level=logging.INFO, format="%(asctime)s %(filename)s[line:%(lineno)d]" "%(levelname)s %(message)s", datefmt="%a, %d %b %Y %H:%M:%S", filename=f"logs/{self.config['save_dir'].split('/')[-1]}.log", filemode='w') while True: self.epoch += 1 self.prefetcher.reset() if self.config['distributed']: self.train_sampler.set_epoch(self.epoch) self._train_epoch(pbar) if self.iteration > self.train_args['iterations']: break print('\nEnd training....') def _train_epoch(self, pbar): """Process input and calculate loss every training epoch""" device = self.config['device'] train_data = self.prefetcher.next() while train_data is not None: self.iteration += 1 frames, masks, flows_f, flows_b, _ = train_data frames, masks = frames.to(device), masks.to(device).float() l_t = self.num_local_frames b, t, c, h, w = frames.size() gt_local_frames = frames[:, :l_t, ...] local_masks = masks[:, :l_t, ...].contiguous() masked_frames = frames * (1 - masks) masked_local_frames = masked_frames[:, :l_t, ...] # get gt optical flow if flows_f[0] == 'None' or flows_b[0] == 'None': gt_flows_bi = self.fix_raft(gt_local_frames) else: gt_flows_bi = (flows_f.to(device), flows_b.to(device)) # ---- complete flow ---- pred_flows_bi, _ = self.fix_flow_complete.forward_bidirect_flow(gt_flows_bi, local_masks) pred_flows_bi = self.fix_flow_complete.combine_flow(gt_flows_bi, pred_flows_bi, local_masks) # pred_flows_bi = gt_flows_bi # ---- image propagation ---- prop_imgs, updated_local_masks = self.netG.module.img_propagation(masked_local_frames, pred_flows_bi, local_masks, interpolation=self.interp_mode) updated_masks = masks.clone() updated_masks[:, :l_t, ...] = updated_local_masks.view(b, l_t, 1, h, w) updated_frames = masked_frames.clone() prop_local_frames = gt_local_frames * (1-local_masks) + prop_imgs.view(b, l_t, 3, h, w) * local_masks # merge updated_frames[:, :l_t, ...] = prop_local_frames # ---- feature propagation + Transformer ---- pred_imgs = self.netG(updated_frames, pred_flows_bi, masks, updated_masks, l_t) pred_imgs = pred_imgs.view(b, -1, c, h, w) # get the local frames pred_local_frames = pred_imgs[:, :l_t, ...] comp_local_frames = gt_local_frames * (1. - local_masks) + pred_local_frames * local_masks comp_imgs = frames * (1. - masks) + pred_imgs * masks gen_loss = 0 dis_loss = 0 # optimize net_g if not self.config['model']['no_dis']: for p in self.netD.parameters(): p.requires_grad = False self.optimG.zero_grad() # generator l1 loss hole_loss = self.l1_loss(pred_imgs * masks, frames * masks) hole_loss = hole_loss / torch.mean(masks) * self.config['losses']['hole_weight'] gen_loss += hole_loss self.add_summary(self.gen_writer, 'loss/hole_loss', hole_loss.item()) valid_loss = self.l1_loss(pred_imgs * (1 - masks), frames * (1 - masks)) valid_loss = valid_loss / torch.mean(1-masks) * self.config['losses']['valid_weight'] gen_loss += valid_loss self.add_summary(self.gen_writer, 'loss/valid_loss', valid_loss.item()) # perceptual loss if self.config['losses']['perceptual_weight'] > 0: perc_loss = self.perc_loss(pred_imgs.view(-1,3,h,w), frames.view(-1,3,h,w))[0] * self.config['losses']['perceptual_weight'] gen_loss += perc_loss self.add_summary(self.gen_writer, 'loss/perc_loss', perc_loss.item()) # gan loss if not self.config['model']['no_dis']: # generator adversarial loss gen_clip = self.netD(comp_imgs) gan_loss = self.adversarial_loss(gen_clip, True, False) gan_loss = gan_loss * self.config['losses']['adversarial_weight'] gen_loss += gan_loss self.add_summary(self.gen_writer, 'loss/gan_loss', gan_loss.item()) gen_loss.backward() self.optimG.step() if not self.config['model']['no_dis']: # optimize net_d for p in self.netD.parameters(): p.requires_grad = True self.optimD.zero_grad() # discriminator adversarial loss real_clip = self.netD(frames) fake_clip = self.netD(comp_imgs.detach()) dis_real_loss = self.adversarial_loss(real_clip, True, True) dis_fake_loss = self.adversarial_loss(fake_clip, False, True) dis_loss += (dis_real_loss + dis_fake_loss) / 2 self.add_summary(self.dis_writer, 'loss/dis_vid_real', dis_real_loss.item()) self.add_summary(self.dis_writer, 'loss/dis_vid_fake', dis_fake_loss.item()) dis_loss.backward() self.optimD.step() self.update_learning_rate() # write image to tensorboard if self.iteration % 200 == 0: # img to cpu t = 0 gt_local_frames_cpu = ((gt_local_frames.view(b,-1,3,h,w) + 1)/2.0).cpu() masked_local_frames = ((masked_local_frames.view(b,-1,3,h,w) + 1)/2.0).cpu() prop_local_frames_cpu = ((prop_local_frames.view(b,-1,3,h,w) + 1)/2.0).cpu() pred_local_frames_cpu = ((pred_local_frames.view(b,-1,3,h,w) + 1)/2.0).cpu() img_results = torch.cat([masked_local_frames[0][t], gt_local_frames_cpu[0][t], prop_local_frames_cpu[0][t], pred_local_frames_cpu[0][t]], 1) img_results = torchvision.utils.make_grid(img_results, nrow=1, normalize=True) if self.gen_writer is not None: self.gen_writer.add_image(f'img/img:inp-gt-res-{t}', img_results, self.iteration) t = 5 if masked_local_frames.shape[1] > 5: img_results = torch.cat([masked_local_frames[0][t], gt_local_frames_cpu[0][t], prop_local_frames_cpu[0][t], pred_local_frames_cpu[0][t]], 1) img_results = torchvision.utils.make_grid(img_results, nrow=1, normalize=True) if self.gen_writer is not None: self.gen_writer.add_image(f'img/img:inp-gt-res-{t}', img_results, self.iteration) # flow to cpu gt_flows_forward_cpu = flow_to_image(gt_flows_bi[0][0]).cpu() masked_flows_forward_cpu = (gt_flows_forward_cpu[0] * (1-local_masks[0][0].cpu())).to(gt_flows_forward_cpu) pred_flows_forward_cpu = flow_to_image(pred_flows_bi[0][0]).cpu() flow_results = torch.cat([gt_flows_forward_cpu[0], masked_flows_forward_cpu, pred_flows_forward_cpu[0]], 1) if self.gen_writer is not None: self.gen_writer.add_image('img/flow:gt-pred', flow_results, self.iteration) # console logs if self.config['global_rank'] == 0: pbar.update(1) if not self.config['model']['no_dis']: pbar.set_description((f"d: {dis_loss.item():.3f}; " f"hole: {hole_loss.item():.3f}; " f"valid: {valid_loss.item():.3f}")) else: pbar.set_description((f"hole: {hole_loss.item():.3f}; " f"valid: {valid_loss.item():.3f}")) if self.iteration % self.train_args['log_freq'] == 0: if not self.config['model']['no_dis']: logging.info(f"[Iter {self.iteration}] " f"d: {dis_loss.item():.4f}; " f"hole: {hole_loss.item():.4f}; " f"valid: {valid_loss.item():.4f}") else: logging.info(f"[Iter {self.iteration}] " f"hole: {hole_loss.item():.4f}; " f"valid: {valid_loss.item():.4f}") # saving models if self.iteration % self.train_args['save_freq'] == 0: self.save(int(self.iteration)) if self.iteration > self.train_args['iterations']: break train_data = self.prefetcher.next() ================================================ FILE: propainter/core/trainer_flow_w_edge.py ================================================ import os import glob import logging import importlib from tqdm import tqdm import torch import torch.nn as nn import torch.nn.functional as F from .prefetch_dataloader import PrefetchDataLoader, CPUPrefetcher from torch.utils.data.distributed import DistributedSampler from torch.nn.parallel import DistributedDataParallel as DDP from torch.utils.tensorboard import SummaryWriter from .lr_scheduler import MultiStepRestartLR, CosineAnnealingRestartLR from .dataset import TrainDataset from ..model.modules.flow_comp_raft import RAFT_bi, FlowLoss, EdgeLoss # from skimage.feature import canny from ..model.canny.canny_filter import Canny from ..RAFT.utils.flow_viz_pt import flow_to_image class Trainer: def __init__(self, config): self.config = config self.epoch = 0 self.iteration = 0 self.num_local_frames = config['train_data_loader']['num_local_frames'] self.num_ref_frames = config['train_data_loader']['num_ref_frames'] # setup data set and data loader self.train_dataset = TrainDataset(config['train_data_loader']) self.train_sampler = None self.train_args = config['trainer'] if config['distributed']: self.train_sampler = DistributedSampler( self.train_dataset, num_replicas=config['world_size'], rank=config['global_rank']) dataloader_args = dict( dataset=self.train_dataset, batch_size=self.train_args['batch_size'] // config['world_size'], shuffle=(self.train_sampler is None), num_workers=self.train_args['num_workers'], sampler=self.train_sampler, drop_last=True) self.train_loader = PrefetchDataLoader(self.train_args['num_prefetch_queue'], **dataloader_args) self.prefetcher = CPUPrefetcher(self.train_loader) # set raft self.fix_raft = RAFT_bi(device = self.config['device']) self.flow_loss = FlowLoss() self.edge_loss = EdgeLoss() self.canny = Canny(sigma=(2,2), low_threshold=0.1, high_threshold=0.2) # setup models including generator and discriminator net = importlib.import_module('model.' + config['model']['net']) self.netG = net.RecurrentFlowCompleteNet() # print(self.netG) self.netG = self.netG.to(self.config['device']) # setup optimizers and schedulers self.setup_optimizers() self.setup_schedulers() self.load() if config['distributed']: self.netG = DDP(self.netG, device_ids=[self.config['local_rank']], output_device=self.config['local_rank'], broadcast_buffers=True, find_unused_parameters=True) # set summary writer self.dis_writer = None self.gen_writer = None self.summary = {} if self.config['global_rank'] == 0 or (not config['distributed']): self.gen_writer = SummaryWriter( os.path.join(config['save_dir'], 'gen')) def setup_optimizers(self): """Set up optimizers.""" backbone_params = [] for name, param in self.netG.named_parameters(): if param.requires_grad: backbone_params.append(param) else: print(f'Params {name} will not be optimized.') optim_params = [ { 'params': backbone_params, 'lr': self.config['trainer']['lr'] }, ] self.optimG = torch.optim.Adam(optim_params, betas=(self.config['trainer']['beta1'], self.config['trainer']['beta2'])) def setup_schedulers(self): """Set up schedulers.""" scheduler_opt = self.config['trainer']['scheduler'] scheduler_type = scheduler_opt.pop('type') if scheduler_type in ['MultiStepLR', 'MultiStepRestartLR']: self.scheG = MultiStepRestartLR( self.optimG, milestones=scheduler_opt['milestones'], gamma=scheduler_opt['gamma']) elif scheduler_type == 'CosineAnnealingRestartLR': self.scheG = CosineAnnealingRestartLR( self.optimG, periods=scheduler_opt['periods'], restart_weights=scheduler_opt['restart_weights']) else: raise NotImplementedError( f'Scheduler {scheduler_type} is not implemented yet.') def update_learning_rate(self): """Update learning rate.""" self.scheG.step() def get_lr(self): """Get current learning rate.""" return self.optimG.param_groups[0]['lr'] def add_summary(self, writer, name, val): """Add tensorboard summary.""" if name not in self.summary: self.summary[name] = 0 self.summary[name] += val n = self.train_args['log_freq'] if writer is not None and self.iteration % n == 0: writer.add_scalar(name, self.summary[name] / n, self.iteration) self.summary[name] = 0 def load(self): """Load netG.""" # get the latest checkpoint model_path = self.config['save_dir'] if os.path.isfile(os.path.join(model_path, 'latest.ckpt')): latest_epoch = open(os.path.join(model_path, 'latest.ckpt'), 'r').read().splitlines()[-1] else: ckpts = [ os.path.basename(i).split('.pth')[0] for i in glob.glob(os.path.join(model_path, '*.pth')) ] ckpts.sort() latest_epoch = ckpts[-1][4:] if len(ckpts) > 0 else None if latest_epoch is not None: gen_path = os.path.join(model_path, f'gen_{int(latest_epoch):06d}.pth') opt_path = os.path.join(model_path,f'opt_{int(latest_epoch):06d}.pth') if self.config['global_rank'] == 0: print(f'Loading model from {gen_path}...') dataG = torch.load(gen_path, map_location=self.config['device']) self.netG.load_state_dict(dataG) data_opt = torch.load(opt_path, map_location=self.config['device']) self.optimG.load_state_dict(data_opt['optimG']) self.scheG.load_state_dict(data_opt['scheG']) self.epoch = data_opt['epoch'] self.iteration = data_opt['iteration'] else: if self.config['global_rank'] == 0: print('Warnning: There is no trained model found.' 'An initialized model will be used.') def save(self, it): """Save parameters every eval_epoch""" if self.config['global_rank'] == 0: # configure path gen_path = os.path.join(self.config['save_dir'], f'gen_{it:06d}.pth') opt_path = os.path.join(self.config['save_dir'], f'opt_{it:06d}.pth') print(f'\nsaving model to {gen_path} ...') # remove .module for saving if isinstance(self.netG, torch.nn.DataParallel) or isinstance(self.netG, DDP): netG = self.netG.module else: netG = self.netG # save checkpoints torch.save(netG.state_dict(), gen_path) torch.save( { 'epoch': self.epoch, 'iteration': self.iteration, 'optimG': self.optimG.state_dict(), 'scheG': self.scheG.state_dict() }, opt_path) latest_path = os.path.join(self.config['save_dir'], 'latest.ckpt') os.system(f"echo {it:06d} > {latest_path}") def train(self): """training entry""" pbar = range(int(self.train_args['iterations'])) if self.config['global_rank'] == 0: pbar = tqdm(pbar, initial=self.iteration, dynamic_ncols=True, smoothing=0.01) os.makedirs('logs', exist_ok=True) logging.basicConfig( level=logging.INFO, format="%(asctime)s %(filename)s[line:%(lineno)d]" "%(levelname)s %(message)s", datefmt="%a, %d %b %Y %H:%M:%S", filename=f"logs/{self.config['save_dir'].split('/')[-1]}.log", filemode='w') while True: self.epoch += 1 self.prefetcher.reset() if self.config['distributed']: self.train_sampler.set_epoch(self.epoch) self._train_epoch(pbar) if self.iteration > self.train_args['iterations']: break print('\nEnd training....') # def get_edges(self, flows): # fgvc # # (b, t, 2, H, W) # b, t, _, h, w = flows.shape # flows = flows.view(-1, 2, h, w) # flows_list = flows.permute(0, 2, 3, 1).cpu().numpy() # edges = [] # for f in list(flows_list): # flows_gray = (f[:, :, 0] ** 2 + f[:, :, 1] ** 2) ** 0.5 # if flows_gray.max() < 1: # flows_gray = flows_gray*0 # else: # flows_gray = flows_gray / flows_gray.max() # edge = canny(flows_gray, sigma=2, low_threshold=0.1, high_threshold=0.2) # fgvc # edge = torch.from_numpy(edge).view(1, 1, h, w).float() # edges.append(edge) # edges = torch.stack(edges, dim=0).to(self.config['device']) # edges = edges.view(b, t, 1, h, w) # return edges def get_edges(self, flows): # (b, t, 2, H, W) b, t, _, h, w = flows.shape flows = flows.view(-1, 2, h, w) flows_gray = (flows[:, 0, None] ** 2 + flows[:, 1, None] ** 2) ** 0.5 if flows_gray.max() < 1: flows_gray = flows_gray*0 else: flows_gray = flows_gray / flows_gray.max() magnitude, edges = self.canny(flows_gray.float()) edges = edges.view(b, t, 1, h, w) return edges def _train_epoch(self, pbar): """Process input and calculate loss every training epoch""" device = self.config['device'] train_data = self.prefetcher.next() while train_data is not None: self.iteration += 1 frames, masks, flows_f, flows_b, _ = train_data frames, masks = frames.to(device), masks.to(device) masks = masks.float() l_t = self.num_local_frames b, t, c, h, w = frames.size() gt_local_frames = frames[:, :l_t, ...] local_masks = masks[:, :l_t, ...].contiguous() # get gt optical flow if flows_f[0] == 'None' or flows_b[0] == 'None': gt_flows_bi = self.fix_raft(gt_local_frames) else: gt_flows_bi = (flows_f.to(device), flows_b.to(device)) # get gt edge gt_edges_forward = self.get_edges(gt_flows_bi[0]) gt_edges_backward = self.get_edges(gt_flows_bi[1]) gt_edges_bi = [gt_edges_forward, gt_edges_backward] # complete flow pred_flows_bi, pred_edges_bi = self.netG.module.forward_bidirect_flow(gt_flows_bi, local_masks) # optimize net_g self.optimG.zero_grad() # compulte flow_loss flow_loss, warp_loss = self.flow_loss(pred_flows_bi, gt_flows_bi, local_masks, gt_local_frames) flow_loss = flow_loss * self.config['losses']['flow_weight'] warp_loss = warp_loss * 0.01 self.add_summary(self.gen_writer, 'loss/flow_loss', flow_loss.item()) self.add_summary(self.gen_writer, 'loss/warp_loss', warp_loss.item()) # compute edge loss edge_loss = self.edge_loss(pred_edges_bi, gt_edges_bi, local_masks) edge_loss = edge_loss*1.0 self.add_summary(self.gen_writer, 'loss/edge_loss', edge_loss.item()) loss = flow_loss + warp_loss + edge_loss loss.backward() self.optimG.step() self.update_learning_rate() # write image to tensorboard # if self.iteration % 200 == 0: if self.iteration % 200 == 0 and self.gen_writer is not None: t = 5 # forward to cpu gt_flows_forward_cpu = flow_to_image(gt_flows_bi[0][0]).cpu() masked_flows_forward_cpu = (gt_flows_forward_cpu[t] * (1-local_masks[0][t].cpu())).to(gt_flows_forward_cpu) pred_flows_forward_cpu = flow_to_image(pred_flows_bi[0][0]).cpu() flow_results = torch.cat([gt_flows_forward_cpu[t], masked_flows_forward_cpu, pred_flows_forward_cpu[t]], 1) self.gen_writer.add_image('img/flow-f:gt-pred', flow_results, self.iteration) # backward to cpu gt_flows_backward_cpu = flow_to_image(gt_flows_bi[1][0]).cpu() masked_flows_backward_cpu = (gt_flows_backward_cpu[t] * (1-local_masks[0][t+1].cpu())).to(gt_flows_backward_cpu) pred_flows_backward_cpu = flow_to_image(pred_flows_bi[1][0]).cpu() flow_results = torch.cat([gt_flows_backward_cpu[t], masked_flows_backward_cpu, pred_flows_backward_cpu[t]], 1) self.gen_writer.add_image('img/flow-b:gt-pred', flow_results, self.iteration) # TODO: show edge # forward gt_edges_forward_cpu = gt_edges_bi[0][0].cpu() masked_edges_forward_cpu = (gt_edges_forward_cpu[t] * (1-local_masks[0][t].cpu())).to(gt_edges_forward_cpu) pred_edges_forward_cpu = pred_edges_bi[0][0].cpu() edge_results = torch.cat([gt_edges_forward_cpu[t], masked_edges_forward_cpu, pred_edges_forward_cpu[t]], 1) self.gen_writer.add_image('img/edge-f:gt-pred', edge_results, self.iteration) # backward gt_edges_backward_cpu = gt_edges_bi[1][0].cpu() masked_edges_backward_cpu = (gt_edges_backward_cpu[t] * (1-local_masks[0][t+1].cpu())).to(gt_edges_backward_cpu) pred_edges_backward_cpu = pred_edges_bi[1][0].cpu() edge_results = torch.cat([gt_edges_backward_cpu[t], masked_edges_backward_cpu, pred_edges_backward_cpu[t]], 1) self.gen_writer.add_image('img/edge-b:gt-pred', edge_results, self.iteration) # console logs if self.config['global_rank'] == 0: pbar.update(1) pbar.set_description((f"flow: {flow_loss.item():.3f}; " f"warp: {warp_loss.item():.3f}; " f"edge: {edge_loss.item():.3f}; " f"lr: {self.get_lr()}")) if self.iteration % self.train_args['log_freq'] == 0: logging.info(f"[Iter {self.iteration}] " f"flow: {flow_loss.item():.4f}; " f"warp: {warp_loss.item():.4f}") # saving models if self.iteration % self.train_args['save_freq'] == 0: self.save(int(self.iteration)) if self.iteration > self.train_args['iterations']: break train_data = self.prefetcher.next() ================================================ FILE: propainter/core/utils.py ================================================ import os import io import cv2 import random import numpy as np from PIL import Image, ImageOps import zipfile import math import torch import matplotlib import matplotlib.patches as patches from matplotlib.path import Path from matplotlib import pyplot as plt from torchvision import transforms # matplotlib.use('agg') # ########################################################################### # Directory IO # ########################################################################### def read_dirnames_under_root(root_dir): dirnames = [ name for i, name in enumerate(sorted(os.listdir(root_dir))) if os.path.isdir(os.path.join(root_dir, name)) ] print(f'Reading directories under {root_dir}, num: {len(dirnames)}') return dirnames class TrainZipReader(object): file_dict = dict() def __init__(self): super(TrainZipReader, self).__init__() @staticmethod def build_file_dict(path): file_dict = TrainZipReader.file_dict if path in file_dict: return file_dict[path] else: file_handle = zipfile.ZipFile(path, 'r') file_dict[path] = file_handle return file_dict[path] @staticmethod def imread(path, idx): zfile = TrainZipReader.build_file_dict(path) filelist = zfile.namelist() filelist.sort() data = zfile.read(filelist[idx]) # im = Image.open(io.BytesIO(data)) return im class TestZipReader(object): file_dict = dict() def __init__(self): super(TestZipReader, self).__init__() @staticmethod def build_file_dict(path): file_dict = TestZipReader.file_dict if path in file_dict: return file_dict[path] else: file_handle = zipfile.ZipFile(path, 'r') file_dict[path] = file_handle return file_dict[path] @staticmethod def imread(path, idx): zfile = TestZipReader.build_file_dict(path) filelist = zfile.namelist() filelist.sort() data = zfile.read(filelist[idx]) file_bytes = np.asarray(bytearray(data), dtype=np.uint8) im = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR) im = Image.fromarray(cv2.cvtColor(im, cv2.COLOR_BGR2RGB)) # im = Image.open(io.BytesIO(data)) return im # ########################################################################### # Data augmentation # ########################################################################### def to_tensors(): return transforms.Compose([Stack(), ToTorchFormatTensor()]) class GroupRandomHorizontalFlowFlip(object): """Randomly horizontally flips the given PIL.Image with a probability of 0.5 """ def __call__(self, img_group, flowF_group, flowB_group): v = random.random() if v < 0.5: ret_img = [ img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group ] ret_flowF = [ff[:, ::-1] * [-1.0, 1.0] for ff in flowF_group] ret_flowB = [fb[:, ::-1] * [-1.0, 1.0] for fb in flowB_group] return ret_img, ret_flowF, ret_flowB else: return img_group, flowF_group, flowB_group class GroupRandomHorizontalFlip(object): """Randomly horizontally flips the given PIL.Image with a probability of 0.5 """ def __call__(self, img_group, is_flow=False): v = random.random() if v < 0.5: ret = [img.transpose(Image.FLIP_LEFT_RIGHT) for img in img_group] if is_flow: for i in range(0, len(ret), 2): # invert flow pixel values when flipping ret[i] = ImageOps.invert(ret[i]) return ret else: return img_group class Stack(object): def __init__(self, roll=False): self.roll = roll def __call__(self, img_group): mode = img_group[0].mode if mode == '1': img_group = [img.convert('L') for img in img_group] mode = 'L' if mode == 'L': return np.stack([np.expand_dims(x, 2) for x in img_group], axis=2) elif mode == 'RGB': if self.roll: return np.stack([np.array(x)[:, :, ::-1] for x in img_group], axis=2) else: return np.stack(img_group, axis=2) else: raise NotImplementedError(f"Image mode {mode}") class ToTorchFormatTensor(object): """ Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0] """ def __init__(self, div=True): self.div = div def __call__(self, pic): if isinstance(pic, np.ndarray): # numpy img: [L, C, H, W] img = torch.from_numpy(pic).permute(2, 3, 0, 1).contiguous() else: # handle PIL Image img = torch.ByteTensor(torch.ByteStorage.from_buffer( pic.tobytes())) img = img.view(pic.size[1], pic.size[0], len(pic.mode)) # put it from HWC to CHW format # yikes, this transpose takes 80% of the loading time/CPU img = img.transpose(0, 1).transpose(0, 2).contiguous() img = img.float().div(255) if self.div else img.float() return img # ########################################################################### # Create masks with random shape # ########################################################################### def create_random_shape_with_random_motion(video_length, imageHeight=240, imageWidth=432): # get a random shape height = random.randint(imageHeight // 3, imageHeight - 1) width = random.randint(imageWidth // 3, imageWidth - 1) edge_num = random.randint(6, 8) ratio = random.randint(6, 8) / 10 region = get_random_shape(edge_num=edge_num, ratio=ratio, height=height, width=width) region_width, region_height = region.size # get random position x, y = random.randint(0, imageHeight - region_height), random.randint( 0, imageWidth - region_width) velocity = get_random_velocity(max_speed=3) m = Image.fromarray(np.zeros((imageHeight, imageWidth)).astype(np.uint8)) m.paste(region, (y, x, y + region.size[0], x + region.size[1])) masks = [m.convert('L')] # return fixed masks if random.uniform(0, 1) > 0.5: return masks * video_length # return moving masks for _ in range(video_length - 1): x, y, velocity = random_move_control_points(x, y, imageHeight, imageWidth, velocity, region.size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3) m = Image.fromarray( np.zeros((imageHeight, imageWidth)).astype(np.uint8)) m.paste(region, (y, x, y + region.size[0], x + region.size[1])) masks.append(m.convert('L')) return masks def create_random_shape_with_random_motion_zoom_rotation(video_length, zoomin=0.9, zoomout=1.1, rotmin=1, rotmax=10, imageHeight=240, imageWidth=432): # get a random shape assert zoomin < 1, "Zoom-in parameter must be smaller than 1" assert zoomout > 1, "Zoom-out parameter must be larger than 1" assert rotmin < rotmax, "Minimum value of rotation must be smaller than maximun value !" height = random.randint(imageHeight//3, imageHeight-1) width = random.randint(imageWidth//3, imageWidth-1) edge_num = random.randint(6, 8) ratio = random.randint(6, 8)/10 region = get_random_shape( edge_num=edge_num, ratio=ratio, height=height, width=width) region_width, region_height = region.size # get random position x, y = random.randint( 0, imageHeight-region_height), random.randint(0, imageWidth-region_width) velocity = get_random_velocity(max_speed=3) m = Image.fromarray(np.zeros((imageHeight, imageWidth)).astype(np.uint8)) m.paste(region, (y, x, y+region.size[0], x+region.size[1])) masks = [m.convert('L')] # return fixed masks if random.uniform(0, 1) > 0.5: return masks*video_length # -> directly copy all the base masks # return moving masks for _ in range(video_length-1): x, y, velocity = random_move_control_points( x, y, imageHeight, imageWidth, velocity, region.size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3) m = Image.fromarray( np.zeros((imageHeight, imageWidth)).astype(np.uint8)) ### add by kaidong, to simulate zoon-in, zoom-out and rotation extra_transform = random.uniform(0, 1) # zoom in and zoom out if extra_transform > 0.75: resize_coefficient = random.uniform(zoomin, zoomout) region = region.resize((math.ceil(region_width * resize_coefficient), math.ceil(region_height * resize_coefficient)), Image.NEAREST) m.paste(region, (y, x, y + region.size[0], x + region.size[1])) region_width, region_height = region.size # rotation elif extra_transform > 0.5: m.paste(region, (y, x, y + region.size[0], x + region.size[1])) m = m.rotate(random.randint(rotmin, rotmax)) # region_width, region_height = region.size ### end else: m.paste(region, (y, x, y+region.size[0], x+region.size[1])) masks.append(m.convert('L')) return masks def get_random_shape(edge_num=9, ratio=0.7, width=432, height=240): ''' There is the initial point and 3 points per cubic bezier curve. Thus, the curve will only pass though n points, which will be the sharp edges. The other 2 modify the shape of the bezier curve. edge_num, Number of possibly sharp edges points_num, number of points in the Path ratio, (0, 1) magnitude of the perturbation from the unit circle, ''' points_num = edge_num*3 + 1 angles = np.linspace(0, 2*np.pi, points_num) codes = np.full(points_num, Path.CURVE4) codes[0] = Path.MOVETO # Using this instead of Path.CLOSEPOLY avoids an innecessary straight line verts = np.stack((np.cos(angles), np.sin(angles))).T * \ (2*ratio*np.random.random(points_num)+1-ratio)[:, None] verts[-1, :] = verts[0, :] path = Path(verts, codes) # draw paths into images fig = plt.figure() ax = fig.add_subplot(111) patch = patches.PathPatch(path, facecolor='black', lw=2) ax.add_patch(patch) ax.set_xlim(np.min(verts)*1.1, np.max(verts)*1.1) ax.set_ylim(np.min(verts)*1.1, np.max(verts)*1.1) ax.axis('off') # removes the axis to leave only the shape fig.canvas.draw() # convert plt images into numpy images data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8) data = data.reshape((fig.canvas.get_width_height()[::-1] + (3,))) plt.close(fig) # postprocess data = cv2.resize(data, (width, height))[:, :, 0] data = (1 - np.array(data > 0).astype(np.uint8))*255 corrdinates = np.where(data > 0) xmin, xmax, ymin, ymax = np.min(corrdinates[0]), np.max( corrdinates[0]), np.min(corrdinates[1]), np.max(corrdinates[1]) region = Image.fromarray(data).crop((ymin, xmin, ymax, xmax)) return region def random_accelerate(velocity, maxAcceleration, dist='uniform'): speed, angle = velocity d_speed, d_angle = maxAcceleration if dist == 'uniform': speed += np.random.uniform(-d_speed, d_speed) angle += np.random.uniform(-d_angle, d_angle) elif dist == 'guassian': speed += np.random.normal(0, d_speed / 2) angle += np.random.normal(0, d_angle / 2) else: raise NotImplementedError( f'Distribution type {dist} is not supported.') return (speed, angle) def get_random_velocity(max_speed=3, dist='uniform'): if dist == 'uniform': speed = np.random.uniform(max_speed) elif dist == 'guassian': speed = np.abs(np.random.normal(0, max_speed / 2)) else: raise NotImplementedError( f'Distribution type {dist} is not supported.') angle = np.random.uniform(0, 2 * np.pi) return (speed, angle) def random_move_control_points(X, Y, imageHeight, imageWidth, lineVelocity, region_size, maxLineAcceleration=(3, 0.5), maxInitSpeed=3): region_width, region_height = region_size speed, angle = lineVelocity X += int(speed * np.cos(angle)) Y += int(speed * np.sin(angle)) lineVelocity = random_accelerate(lineVelocity, maxLineAcceleration, dist='guassian') if ((X > imageHeight - region_height) or (X < 0) or (Y > imageWidth - region_width) or (Y < 0)): lineVelocity = get_random_velocity(maxInitSpeed, dist='guassian') new_X = np.clip(X, 0, imageHeight - region_height) new_Y = np.clip(Y, 0, imageWidth - region_width) return new_X, new_Y, lineVelocity if __name__ == '__main__': trials = 10 for _ in range(trials): video_length = 10 # The returned masks are either stationary (50%) or moving (50%) masks = create_random_shape_with_random_motion(video_length, imageHeight=240, imageWidth=432) for m in masks: cv2.imshow('mask', np.array(m)) cv2.waitKey(500) ================================================ FILE: propainter/inference.py ================================================ # -*- coding: utf-8 -*- import os import cv2 import numpy as np import scipy.ndimage from PIL import Image from tqdm import tqdm import torch import torchvision import gc # try: # from model.modules.flow_comp_raft import RAFT_bi # from model.recurrent_flow_completion import RecurrentFlowCompleteNet # from model.propainter import InpaintGenerator # from utils.download_util import load_file_from_url # from core.utils import to_tensors # from model.misc import get_device # except: from .model.modules.flow_comp_raft import RAFT_bi from .model.recurrent_flow_completion import RecurrentFlowCompleteNet from .model.propainter import InpaintGenerator from .utils.download_util import load_file_from_url from .core.utils import to_tensors from .model.misc import get_device import warnings warnings.filterwarnings("ignore") pretrain_model_url = 'https://github.com/sczhou/ProPainter/releases/download/v0.1.0/' MaxSideThresh = 960 # resize frames def resize_frames(frames, size=None): if size is not None: out_size = size process_size = (out_size[0]-out_size[0]%8, out_size[1]-out_size[1]%8) if not out_size == process_size: frames = [f.resize(process_size) for f in frames] else: out_size = frames[0].size process_size = (out_size[0]-out_size[0]%8, out_size[1]-out_size[1]%8) if not out_size == process_size: frames = [f.resize(process_size) for f in frames] return frames, process_size, out_size # read frames from video def read_frame_from_videos(frame_root, video_length): if frame_root.endswith(('mp4', 'mov', 'avi', 'MP4', 'MOV', 'AVI')): # input video path video_name = os.path.basename(frame_root)[:-4] vframes, aframes, info = torchvision.io.read_video(filename=frame_root, pts_unit='sec', end_pts=video_length) # RGB frames = list(vframes.numpy()) frames = [Image.fromarray(f) for f in frames] fps = info['video_fps'] nframes = len(frames) else: video_name = os.path.basename(frame_root) frames = [] fr_lst = sorted(os.listdir(frame_root)) for fr in fr_lst: frame = cv2.imread(os.path.join(frame_root, fr)) frame = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) frames.append(frame) fps = None nframes = len(frames) size = frames[0].size return frames, fps, size, video_name, nframes def binary_mask(mask, th=0.1): mask[mask>th] = 1 mask[mask<=th] = 0 return mask # read frame-wise masks def read_mask(mpath, frames_len, size, flow_mask_dilates=8, mask_dilates=5): masks_img = [] masks_dilated = [] flow_masks = [] if mpath.endswith(('jpg', 'jpeg', 'png', 'JPG', 'JPEG', 'PNG')): # input single img path masks_img = [Image.open(mpath)] elif mpath.endswith(('mp4', 'mov', 'avi', 'MP4', 'MOV', 'AVI')): # input video path cap = cv2.VideoCapture(mpath) if not cap.isOpened(): print("Error: Could not open video.") exit() idx = 0 while True: ret, frame = cap.read() if not ret: break if(idx >= frames_len): break masks_img.append(Image.fromarray(frame)) idx += 1 cap.release() else: mnames = sorted(os.listdir(mpath)) for mp in mnames: masks_img.append(Image.open(os.path.join(mpath, mp))) # print(mp) for mask_img in masks_img: if size is not None: mask_img = mask_img.resize(size, Image.NEAREST) mask_img = np.array(mask_img.convert('L')) # Dilate 8 pixel so that all known pixel is trustworthy if flow_mask_dilates > 0: flow_mask_img = scipy.ndimage.binary_dilation(mask_img, iterations=flow_mask_dilates).astype(np.uint8) else: flow_mask_img = binary_mask(mask_img).astype(np.uint8) # Close the small holes inside the foreground objects # flow_mask_img = cv2.morphologyEx(flow_mask_img, cv2.MORPH_CLOSE, np.ones((21, 21),np.uint8)).astype(bool) # flow_mask_img = scipy.ndimage.binary_fill_holes(flow_mask_img).astype(np.uint8) flow_masks.append(Image.fromarray(flow_mask_img * 255)) if mask_dilates > 0: mask_img = scipy.ndimage.binary_dilation(mask_img, iterations=mask_dilates).astype(np.uint8) else: mask_img = binary_mask(mask_img).astype(np.uint8) masks_dilated.append(Image.fromarray(mask_img * 255)) if len(masks_img) == 1: flow_masks = flow_masks * frames_len masks_dilated = masks_dilated * frames_len return flow_masks, masks_dilated def get_ref_index(mid_neighbor_id, neighbor_ids, length, ref_stride=10, ref_num=-1): ref_index = [] if ref_num == -1: for i in range(0, length, ref_stride): if i not in neighbor_ids: ref_index.append(i) else: start_idx = max(0, mid_neighbor_id - ref_stride * (ref_num // 2)) end_idx = min(length, mid_neighbor_id + ref_stride * (ref_num // 2)) for i in range(start_idx, end_idx, ref_stride): if i not in neighbor_ids: if len(ref_index) > ref_num: break ref_index.append(i) return ref_index def file_exists(directory, filename): # 构建文件的完整路径 file_path = os.path.join(directory, filename) # 检查文件是否存在 return os.path.isfile(file_path) class Propainter: def __init__(self, device): self.device = device def load_propainter(self,fix_raft_path,flow_path,ProPainter_path): ############################################## # set up RAFT and flow competition model ############################################## # if file_exists(propainter_model_dir,'raft-things.pth'): # ckpt_path=os.path.join(propainter_model_dir,'raft-things.pth') # else: # ckpt_path = load_file_from_url(url=os.path.join(pretrain_model_url, 'raft-things.pth'), # model_dir=propainter_model_dir, progress=True, file_name=None) self.fix_raft = RAFT_bi(fix_raft_path, self.device) # if file_exists(propainter_model_dir,'recurrent_flow_completion.pth'): # ckpt_path_=os.path.join(propainter_model_dir,'recurrent_flow_completion.pth') # else: # ckpt_path_ = load_file_from_url(url=os.path.join(pretrain_model_url, 'recurrent_flow_completion.pth'), # model_dir=propainter_model_dir, progress=True, file_name=None) self.fix_flow_complete = RecurrentFlowCompleteNet(flow_path) for p in self.fix_flow_complete.parameters(): p.requires_grad = False self.fix_flow_complete.to(self.device) self.fix_flow_complete.eval() ############################################## # set up ProPainter model ############################################## # if file_exists(propainter_model_dir,'ProPainter.pth'): # ckpt_path_p=os.path.join(propainter_model_dir,'ProPainter.pth') # else: # ckpt_path_p = load_file_from_url(url=os.path.join(pretrain_model_url, 'ProPainter.pth'), # model_dir=propainter_model_dir, progress=True, file_name=None) self.model = InpaintGenerator(model_path=ProPainter_path).to(self.device) self.model.eval() def to(self, device): self.device = device self.fix_raft.to(device) self.fix_flow_complete.to(device) self.model.to(device) def forward(self, video, mask,load_videobypath=False, resize_ratio=1.0, video_length=2, height=-1, width=-1, mask_dilation=4, ref_stride=10, neighbor_length=10, subvideo_length=80, raft_iter=20, save_fps=24.0, fp16=True): # Use fp16 precision during inference to reduce running memory cost use_half = True if fp16 else False if self.device == torch.device('cpu'): use_half = False ################ read input video ################ if load_videobypath: frames, fps, size, video_name, nframes = read_frame_from_videos(video, video_length) else: frames=video size = (width, height) video_name='input' nframes=len(frames) fps=None #frames, fps, size, video_name, nframes = read_frame_from_videos(video, video_length) frames = frames[:nframes] if not width == -1 and not height == -1: size = (width, height) longer_edge = max(size[0], size[1]) if(longer_edge > MaxSideThresh): print('input video size is too large, resize to', MaxSideThresh) scale = MaxSideThresh / longer_edge resize_ratio = resize_ratio * scale if resize_ratio < 1.0: # if longer_edge>960 resize to 960 origin_size = (int(resize_ratio * size[0]), int(resize_ratio * size[1])) frames, size, out_size = resize_frames(frames, origin_size) mask,size_,out_size_=resize_frames(mask, origin_size) else: origin_size = size frames, size, out_size = resize_frames(frames, origin_size) mask,size_,out_size_ = resize_frames(mask, origin_size) origin_frames=frames.copy() origin_masks=mask.copy() fps = save_fps if fps is None else fps ################ read mask ################ frames_len = len(frames) if load_videobypath: flow_masks, masks_dilated = read_mask(mask, frames_len, size, flow_mask_dilates=mask_dilation, mask_dilates=mask_dilation) else: #flow_masks_list=[i.convert('L') for i in mask.copy()] masks_dilated = [] flow_masks = [] flow_mask_dilates=mask_dilation mask_dilates=mask_dilation for i in mask.copy(): # if size is not None: # don't resize mask # i = i.resize(size, Image.NEAREST) mask_img = np.array(i.convert('L')) # Dilate 8 pixel so that all known pixel is trustworthy if flow_mask_dilates > 0: flow_mask_img = scipy.ndimage.binary_dilation(mask_img, iterations=flow_mask_dilates).astype(np.uint8) else: flow_mask_img = binary_mask(mask_img).astype(np.uint8) # Close the small holes inside the foreground objects # flow_mask_img = cv2.morphologyEx(flow_mask_img, cv2.MORPH_CLOSE, np.ones((21, 21),np.uint8)).astype(bool) # flow_mask_img = scipy.ndimage.binary_fill_holes(flow_mask_img).astype(np.uint8) flow_masks.append(Image.fromarray(flow_mask_img * 255)) if mask_dilates > 0: mask_img = scipy.ndimage.binary_dilation(mask_img, iterations=mask_dilates).astype(np.uint8) else: mask_img = binary_mask(mask_img).astype(np.uint8) masks_dilated.append(Image.fromarray(mask_img * 255)) if len(mask) == 1: flow_masks = flow_masks * frames_len masks_dilated = masks_dilated * frames_len # masks_dilated = mask.copy() flow_masks = flow_masks[:nframes] masks_dilated = masks_dilated[:nframes] w, h = size ################ adjust input ################ frames_len = min(len(frames), len(masks_dilated)) frames = frames[:frames_len] flow_masks = flow_masks[:frames_len] masks_dilated = masks_dilated[:frames_len] ori_frames_inp = [np.array(f).astype(np.uint8) for f in frames] frames = to_tensors()(frames).unsqueeze(0) * 2 - 1 flow_masks = to_tensors()(flow_masks).unsqueeze(0) masks_dilated = to_tensors()(masks_dilated).unsqueeze(0) frames, flow_masks, masks_dilated = frames.to(self.device), flow_masks.to(self.device), masks_dilated.to(self.device) ############################################## # ProPainter inference ############################################## video_length = frames.size(1) print(f'Priori generating: [{video_length} frames]...') with torch.no_grad(): # ---- compute flow ---- new_longer_edge = max(frames.size(-1), frames.size(-2)) if new_longer_edge <= 640: short_clip_len = 12 elif new_longer_edge <= 720: short_clip_len = 8 elif new_longer_edge <= 1280: short_clip_len = 4 else: short_clip_len = 2 # use fp32 for RAFT if frames.size(1) > short_clip_len: gt_flows_f_list, gt_flows_b_list = [], [] for f in range(0, video_length, short_clip_len): end_f = min(video_length, f + short_clip_len) if f == 0: flows_f, flows_b = self.fix_raft(frames[:,f:end_f], iters=raft_iter) else: flows_f, flows_b = self.fix_raft(frames[:,f-1:end_f], iters=raft_iter) gt_flows_f_list.append(flows_f) gt_flows_b_list.append(flows_b) torch.cuda.empty_cache() gt_flows_f = torch.cat(gt_flows_f_list, dim=1) gt_flows_b = torch.cat(gt_flows_b_list, dim=1) gt_flows_bi = (gt_flows_f, gt_flows_b) else: gt_flows_bi = self.fix_raft(frames, iters=raft_iter) torch.cuda.empty_cache() torch.cuda.empty_cache() gc.collect() if use_half: frames, flow_masks, masks_dilated = frames.half(), flow_masks.half(), masks_dilated.half() gt_flows_bi = (gt_flows_bi[0].half(), gt_flows_bi[1].half()) self.fix_flow_complete = self.fix_flow_complete.half() self.model = self.model.half() # ---- complete flow ---- flow_length = gt_flows_bi[0].size(1) if flow_length > subvideo_length: pred_flows_f, pred_flows_b = [], [] pad_len = 5 for f in range(0, flow_length, subvideo_length): s_f = max(0, f - pad_len) e_f = min(flow_length, f + subvideo_length + pad_len) pad_len_s = max(0, f) - s_f pad_len_e = e_f - min(flow_length, f + subvideo_length) pred_flows_bi_sub, _ = self.fix_flow_complete.forward_bidirect_flow( (gt_flows_bi[0][:, s_f:e_f], gt_flows_bi[1][:, s_f:e_f]), flow_masks[:, s_f:e_f+1]) pred_flows_bi_sub = self.fix_flow_complete.combine_flow( (gt_flows_bi[0][:, s_f:e_f], gt_flows_bi[1][:, s_f:e_f]), pred_flows_bi_sub, flow_masks[:, s_f:e_f+1]) pred_flows_f.append(pred_flows_bi_sub[0][:, pad_len_s:e_f-s_f-pad_len_e]) pred_flows_b.append(pred_flows_bi_sub[1][:, pad_len_s:e_f-s_f-pad_len_e]) torch.cuda.empty_cache() pred_flows_f = torch.cat(pred_flows_f, dim=1) pred_flows_b = torch.cat(pred_flows_b, dim=1) pred_flows_bi = (pred_flows_f, pred_flows_b) else: pred_flows_bi, _ = self.fix_flow_complete.forward_bidirect_flow(gt_flows_bi, flow_masks) pred_flows_bi = self.fix_flow_complete.combine_flow(gt_flows_bi, pred_flows_bi, flow_masks) torch.cuda.empty_cache() torch.cuda.empty_cache() gc.collect() masks_dilated_ori = masks_dilated.clone() # ---- Pre-propagation ---- subvideo_length_img_prop = min(100, subvideo_length) # ensure a minimum of 100 frames for image propagation if(len(frames[0]))>subvideo_length_img_prop: # perform propagation only when length of frames is larger than subvideo_length_img_prop sample_rate = len(frames[0])//(subvideo_length_img_prop//2) index_sample = list(range(0, len(frames[0]), sample_rate)) sample_frames = torch.stack([frames[0][i].to(torch.float32) for i in index_sample]).unsqueeze(0) # use fp32 for RAFT sample_masks_dilated = torch.stack([masks_dilated[0][i] for i in index_sample]).unsqueeze(0) sample_flow_masks = torch.stack([flow_masks[0][i] for i in index_sample]).unsqueeze(0) ## recompute flow for sampled frames # use fp32 for RAFT sample_video_length = sample_frames.size(1) if sample_frames.size(1) > short_clip_len: gt_flows_f_list, gt_flows_b_list = [], [] for f in range(0, sample_video_length, short_clip_len): end_f = min(sample_video_length, f + short_clip_len) if f == 0: flows_f, flows_b = self.fix_raft(sample_frames[:,f:end_f], iters=raft_iter) else: flows_f, flows_b = self.fix_raft(sample_frames[:,f-1:end_f], iters=raft_iter) gt_flows_f_list.append(flows_f) gt_flows_b_list.append(flows_b) torch.cuda.empty_cache() gt_flows_f = torch.cat(gt_flows_f_list, dim=1) gt_flows_b = torch.cat(gt_flows_b_list, dim=1) sample_gt_flows_bi = (gt_flows_f, gt_flows_b) else: sample_gt_flows_bi = self.fix_raft(sample_frames, iters=raft_iter) torch.cuda.empty_cache() torch.cuda.empty_cache() gc.collect() if use_half: sample_frames, sample_flow_masks, sample_masks_dilated = sample_frames.half(), sample_flow_masks.half(), sample_masks_dilated.half() sample_gt_flows_bi = (sample_gt_flows_bi[0].half(), sample_gt_flows_bi[1].half()) # ---- complete flow ---- flow_length = sample_gt_flows_bi[0].size(1) if flow_length > subvideo_length: pred_flows_f, pred_flows_b = [], [] pad_len = 5 for f in range(0, flow_length, subvideo_length): s_f = max(0, f - pad_len) e_f = min(flow_length, f + subvideo_length + pad_len) pad_len_s = max(0, f) - s_f pad_len_e = e_f - min(flow_length, f + subvideo_length) pred_flows_bi_sub, _ = self.fix_flow_complete.forward_bidirect_flow( (sample_gt_flows_bi[0][:, s_f:e_f], sample_gt_flows_bi[1][:, s_f:e_f]), sample_flow_masks[:, s_f:e_f+1]) pred_flows_bi_sub = self.fix_flow_complete.combine_flow( (sample_gt_flows_bi[0][:, s_f:e_f], sample_gt_flows_bi[1][:, s_f:e_f]), pred_flows_bi_sub, sample_flow_masks[:, s_f:e_f+1]) pred_flows_f.append(pred_flows_bi_sub[0][:, pad_len_s:e_f-s_f-pad_len_e]) pred_flows_b.append(pred_flows_bi_sub[1][:, pad_len_s:e_f-s_f-pad_len_e]) torch.cuda.empty_cache() pred_flows_f = torch.cat(pred_flows_f, dim=1) pred_flows_b = torch.cat(pred_flows_b, dim=1) sample_pred_flows_bi = (pred_flows_f, pred_flows_b) else: sample_pred_flows_bi, _ = self.fix_flow_complete.forward_bidirect_flow(sample_gt_flows_bi, sample_flow_masks) sample_pred_flows_bi = self.fix_flow_complete.combine_flow(sample_gt_flows_bi, sample_pred_flows_bi, sample_flow_masks) torch.cuda.empty_cache() torch.cuda.empty_cache() gc.collect() masked_frames = sample_frames * (1 - sample_masks_dilated) if sample_video_length > subvideo_length_img_prop: updated_frames, updated_masks = [], [] pad_len = 10 for f in range(0, sample_video_length, subvideo_length_img_prop): s_f = max(0, f - pad_len) e_f = min(sample_video_length, f + subvideo_length_img_prop + pad_len) pad_len_s = max(0, f) - s_f pad_len_e = e_f - min(sample_video_length, f + subvideo_length_img_prop) b, t, _, _, _ = sample_masks_dilated[:, s_f:e_f].size() pred_flows_bi_sub = (sample_pred_flows_bi[0][:, s_f:e_f-1], sample_pred_flows_bi[1][:, s_f:e_f-1]) prop_imgs_sub, updated_local_masks_sub = self.model.img_propagation(masked_frames[:, s_f:e_f], pred_flows_bi_sub, sample_masks_dilated[:, s_f:e_f], 'nearest') updated_frames_sub = sample_frames[:, s_f:e_f] * (1 - sample_masks_dilated[:, s_f:e_f]) + \ prop_imgs_sub.view(b, t, 3, h, w) * sample_masks_dilated[:, s_f:e_f] updated_masks_sub = updated_local_masks_sub.view(b, t, 1, h, w) updated_frames.append(updated_frames_sub[:, pad_len_s:e_f-s_f-pad_len_e]) updated_masks.append(updated_masks_sub[:, pad_len_s:e_f-s_f-pad_len_e]) torch.cuda.empty_cache() updated_frames = torch.cat(updated_frames, dim=1) updated_masks = torch.cat(updated_masks, dim=1) else: b, t, _, _, _ = sample_masks_dilated.size() prop_imgs, updated_local_masks = self.model.img_propagation(masked_frames, sample_pred_flows_bi, sample_masks_dilated, 'nearest') updated_frames = sample_frames * (1 - sample_masks_dilated) + prop_imgs.view(b, t, 3, h, w) * sample_masks_dilated updated_masks = updated_local_masks.view(b, t, 1, h, w) torch.cuda.empty_cache() ## replace input frames/masks with updated frames/masks for i,index in enumerate(index_sample): frames[0][index] = updated_frames[0][i] masks_dilated[0][index] = updated_masks[0][i] # ---- frame-by-frame image propagation ---- masked_frames = frames * (1 - masks_dilated) subvideo_length_img_prop = min(100, subvideo_length) # ensure a minimum of 100 frames for image propagation if video_length > subvideo_length_img_prop: updated_frames, updated_masks = [], [] pad_len = 10 for f in range(0, video_length, subvideo_length_img_prop): s_f = max(0, f - pad_len) e_f = min(video_length, f + subvideo_length_img_prop + pad_len) pad_len_s = max(0, f) - s_f pad_len_e = e_f - min(video_length, f + subvideo_length_img_prop) b, t, _, _, _ = masks_dilated[:, s_f:e_f].size() pred_flows_bi_sub = (pred_flows_bi[0][:, s_f:e_f-1], pred_flows_bi[1][:, s_f:e_f-1]) prop_imgs_sub, updated_local_masks_sub = self.model.img_propagation(masked_frames[:, s_f:e_f], pred_flows_bi_sub, masks_dilated[:, s_f:e_f], 'nearest') updated_frames_sub = frames[:, s_f:e_f] * (1 - masks_dilated[:, s_f:e_f]) + \ prop_imgs_sub.view(b, t, 3, h, w) * masks_dilated[:, s_f:e_f] updated_masks_sub = updated_local_masks_sub.view(b, t, 1, h, w) updated_frames.append(updated_frames_sub[:, pad_len_s:e_f-s_f-pad_len_e]) updated_masks.append(updated_masks_sub[:, pad_len_s:e_f-s_f-pad_len_e]) torch.cuda.empty_cache() updated_frames = torch.cat(updated_frames, dim=1) updated_masks = torch.cat(updated_masks, dim=1) else: b, t, _, _, _ = masks_dilated.size() prop_imgs, updated_local_masks = self.model.img_propagation(masked_frames, pred_flows_bi, masks_dilated, 'nearest') updated_frames = frames * (1 - masks_dilated) + prop_imgs.view(b, t, 3, h, w) * masks_dilated updated_masks = updated_local_masks.view(b, t, 1, h, w) torch.cuda.empty_cache() comp_frames = [None] * video_length neighbor_stride = neighbor_length // 2 if video_length > subvideo_length: ref_num = subvideo_length // ref_stride else: ref_num = -1 torch.cuda.empty_cache() # ---- feature propagation + transformer ---- for f in tqdm(range(0, video_length, neighbor_stride)): neighbor_ids = [ i for i in range(max(0, f - neighbor_stride), min(video_length, f + neighbor_stride + 1)) ] ref_ids = get_ref_index(f, neighbor_ids, video_length, ref_stride, ref_num) selected_imgs = updated_frames[:, neighbor_ids + ref_ids, :, :, :] selected_masks = masks_dilated[:, neighbor_ids + ref_ids, :, :, :] selected_update_masks = updated_masks[:, neighbor_ids + ref_ids, :, :, :] selected_pred_flows_bi = (pred_flows_bi[0][:, neighbor_ids[:-1], :, :, :], pred_flows_bi[1][:, neighbor_ids[:-1], :, :, :]) with torch.no_grad(): # 1.0 indicates mask l_t = len(neighbor_ids) # pred_img = selected_imgs # results of image propagation pred_img = self.model(selected_imgs, selected_pred_flows_bi, selected_masks, selected_update_masks, l_t) pred_img = pred_img.view(-1, 3, h, w) ## compose with input frames pred_img = (pred_img + 1) / 2 pred_img = pred_img.cpu().permute(0, 2, 3, 1).numpy() * 255 binary_masks = masks_dilated_ori[0, neighbor_ids, :, :, :].cpu().permute( 0, 2, 3, 1).numpy().astype(np.uint8) # use original mask for i in range(len(neighbor_ids)): idx = neighbor_ids[i] img = np.array(pred_img[i]).astype(np.uint8) * binary_masks[i] \ + ori_frames_inp[idx] * (1 - binary_masks[i]) if comp_frames[idx] is None: comp_frames[idx] = img else: comp_frames[idx] = comp_frames[idx].astype(np.float32) * 0.5 + img.astype(np.float32) * 0.5 comp_frames[idx] = comp_frames[idx].astype(np.uint8) torch.cuda.empty_cache() ##save composed video## #comp_frames = [cv2.resize(f, out_size) for f in comp_frames] pil_frames = [Image.fromarray(f) for f in comp_frames] # writer = cv2.VideoWriter(output_path, cv2.VideoWriter_fourcc(*"mp4v"), # fps, (comp_frames[0].shape[1],comp_frames[0].shape[0])) # for f in range(video_length): # frame = comp_frames[f].astype(np.uint8) # writer.write(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) # writer.release() # torch.cuda.empty_cache() return pil_frames ================================================ FILE: propainter/model/__init__.py ================================================ ================================================ FILE: propainter/model/canny/__init__.py ================================================ ================================================ FILE: propainter/model/canny/canny_filter.py ================================================ import math from typing import Tuple import torch import torch.nn as nn import torch.nn.functional as F from .gaussian import gaussian_blur2d from .kernels import get_canny_nms_kernel, get_hysteresis_kernel from .sobel import spatial_gradient def rgb_to_grayscale(image, rgb_weights = None): if len(image.shape) < 3 or image.shape[-3] != 3: raise ValueError(f"Input size must have a shape of (*, 3, H, W). Got {image.shape}") if rgb_weights is None: # 8 bit images if image.dtype == torch.uint8: rgb_weights = torch.tensor([76, 150, 29], device=image.device, dtype=torch.uint8) # floating point images elif image.dtype in (torch.float16, torch.float32, torch.float64): rgb_weights = torch.tensor([0.299, 0.587, 0.114], device=image.device, dtype=image.dtype) else: raise TypeError(f"Unknown data type: {image.dtype}") else: # is tensor that we make sure is in the same device/dtype rgb_weights = rgb_weights.to(image) # unpack the color image channels with RGB order r = image[..., 0:1, :, :] g = image[..., 1:2, :, :] b = image[..., 2:3, :, :] w_r, w_g, w_b = rgb_weights.unbind() return w_r * r + w_g * g + w_b * b def canny( input: torch.Tensor, low_threshold: float = 0.1, high_threshold: float = 0.2, kernel_size: Tuple[int, int] = (5, 5), sigma: Tuple[float, float] = (1, 1), hysteresis: bool = True, eps: float = 1e-6, ) -> Tuple[torch.Tensor, torch.Tensor]: r"""Find edges of the input image and filters them using the Canny algorithm. .. image:: _static/img/canny.png Args: input: input image tensor with shape :math:`(B,C,H,W)`. low_threshold: lower threshold for the hysteresis procedure. high_threshold: upper threshold for the hysteresis procedure. kernel_size: the size of the kernel for the gaussian blur. sigma: the standard deviation of the kernel for the gaussian blur. hysteresis: if True, applies the hysteresis edge tracking. Otherwise, the edges are divided between weak (0.5) and strong (1) edges. eps: regularization number to avoid NaN during backprop. Returns: - the canny edge magnitudes map, shape of :math:`(B,1,H,W)`. - the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`. .. note:: See a working example `here `__. Example: >>> input = torch.rand(5, 3, 4, 4) >>> magnitude, edges = canny(input) # 5x3x4x4 >>> magnitude.shape torch.Size([5, 1, 4, 4]) >>> edges.shape torch.Size([5, 1, 4, 4]) """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(input)}") if not len(input.shape) == 4: raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {input.shape}") if low_threshold > high_threshold: raise ValueError( "Invalid input thresholds. low_threshold should be smaller than the high_threshold. Got: {}>{}".format( low_threshold, high_threshold ) ) if low_threshold < 0 and low_threshold > 1: raise ValueError(f"Invalid input threshold. low_threshold should be in range (0,1). Got: {low_threshold}") if high_threshold < 0 and high_threshold > 1: raise ValueError(f"Invalid input threshold. high_threshold should be in range (0,1). Got: {high_threshold}") device: torch.device = input.device dtype: torch.dtype = input.dtype # To Grayscale if input.shape[1] == 3: input = rgb_to_grayscale(input) # Gaussian filter blurred: torch.Tensor = gaussian_blur2d(input, kernel_size, sigma) # Compute the gradients gradients: torch.Tensor = spatial_gradient(blurred, normalized=False) # Unpack the edges gx: torch.Tensor = gradients[:, :, 0] gy: torch.Tensor = gradients[:, :, 1] # Compute gradient magnitude and angle magnitude: torch.Tensor = torch.sqrt(gx * gx + gy * gy + eps) angle: torch.Tensor = torch.atan2(gy, gx) # Radians to Degrees angle = 180.0 * angle / math.pi # Round angle to the nearest 45 degree angle = torch.round(angle / 45) * 45 # Non-maximal suppression nms_kernels: torch.Tensor = get_canny_nms_kernel(device, dtype) nms_magnitude: torch.Tensor = F.conv2d(magnitude, nms_kernels, padding=nms_kernels.shape[-1] // 2) # Get the indices for both directions positive_idx: torch.Tensor = (angle / 45) % 8 positive_idx = positive_idx.long() negative_idx: torch.Tensor = ((angle / 45) + 4) % 8 negative_idx = negative_idx.long() # Apply the non-maximum suppression to the different directions channel_select_filtered_positive: torch.Tensor = torch.gather(nms_magnitude, 1, positive_idx) channel_select_filtered_negative: torch.Tensor = torch.gather(nms_magnitude, 1, negative_idx) channel_select_filtered: torch.Tensor = torch.stack( [channel_select_filtered_positive, channel_select_filtered_negative], 1 ) is_max: torch.Tensor = channel_select_filtered.min(dim=1)[0] > 0.0 magnitude = magnitude * is_max # Threshold edges: torch.Tensor = F.threshold(magnitude, low_threshold, 0.0) low: torch.Tensor = magnitude > low_threshold high: torch.Tensor = magnitude > high_threshold edges = low * 0.5 + high * 0.5 edges = edges.to(dtype) # Hysteresis if hysteresis: edges_old: torch.Tensor = -torch.ones(edges.shape, device=edges.device, dtype=dtype) hysteresis_kernels: torch.Tensor = get_hysteresis_kernel(device, dtype) while ((edges_old - edges).abs() != 0).any(): weak: torch.Tensor = (edges == 0.5).float() strong: torch.Tensor = (edges == 1).float() hysteresis_magnitude: torch.Tensor = F.conv2d( edges, hysteresis_kernels, padding=hysteresis_kernels.shape[-1] // 2 ) hysteresis_magnitude = (hysteresis_magnitude == 1).any(1, keepdim=True).to(dtype) hysteresis_magnitude = hysteresis_magnitude * weak + strong edges_old = edges.clone() edges = hysteresis_magnitude + (hysteresis_magnitude == 0) * weak * 0.5 edges = hysteresis_magnitude return magnitude, edges class Canny(nn.Module): r"""Module that finds edges of the input image and filters them using the Canny algorithm. Args: input: input image tensor with shape :math:`(B,C,H,W)`. low_threshold: lower threshold for the hysteresis procedure. high_threshold: upper threshold for the hysteresis procedure. kernel_size: the size of the kernel for the gaussian blur. sigma: the standard deviation of the kernel for the gaussian blur. hysteresis: if True, applies the hysteresis edge tracking. Otherwise, the edges are divided between weak (0.5) and strong (1) edges. eps: regularization number to avoid NaN during backprop. Returns: - the canny edge magnitudes map, shape of :math:`(B,1,H,W)`. - the canny edge detection filtered by thresholds and hysteresis, shape of :math:`(B,1,H,W)`. Example: >>> input = torch.rand(5, 3, 4, 4) >>> magnitude, edges = Canny()(input) # 5x3x4x4 >>> magnitude.shape torch.Size([5, 1, 4, 4]) >>> edges.shape torch.Size([5, 1, 4, 4]) """ def __init__( self, low_threshold: float = 0.1, high_threshold: float = 0.2, kernel_size: Tuple[int, int] = (5, 5), sigma: Tuple[float, float] = (1, 1), hysteresis: bool = True, eps: float = 1e-6, ) -> None: super().__init__() if low_threshold > high_threshold: raise ValueError( "Invalid input thresholds. low_threshold should be\ smaller than the high_threshold. Got: {}>{}".format( low_threshold, high_threshold ) ) if low_threshold < 0 or low_threshold > 1: raise ValueError(f"Invalid input threshold. low_threshold should be in range (0,1). Got: {low_threshold}") if high_threshold < 0 or high_threshold > 1: raise ValueError(f"Invalid input threshold. high_threshold should be in range (0,1). Got: {high_threshold}") # Gaussian blur parameters self.kernel_size = kernel_size self.sigma = sigma # Double threshold self.low_threshold = low_threshold self.high_threshold = high_threshold # Hysteresis self.hysteresis = hysteresis self.eps: float = eps def __repr__(self) -> str: return ''.join( ( f'{type(self).__name__}(', ', '.join( f'{name}={getattr(self, name)}' for name in sorted(self.__dict__) if not name.startswith('_') ), ')', ) ) def forward(self, input: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]: return canny( input, self.low_threshold, self.high_threshold, self.kernel_size, self.sigma, self.hysteresis, self.eps ) ================================================ FILE: propainter/model/canny/filter.py ================================================ from typing import List import torch import torch.nn.functional as F from .kernels import normalize_kernel2d def _compute_padding(kernel_size: List[int]) -> List[int]: """Compute padding tuple.""" # 4 or 6 ints: (padding_left, padding_right,padding_top,padding_bottom) # https://pytorch.org/docs/stable/nn.html#torch.nn.functional.pad if len(kernel_size) < 2: raise AssertionError(kernel_size) computed = [k - 1 for k in kernel_size] # for even kernels we need to do asymmetric padding :( out_padding = 2 * len(kernel_size) * [0] for i in range(len(kernel_size)): computed_tmp = computed[-(i + 1)] pad_front = computed_tmp // 2 pad_rear = computed_tmp - pad_front out_padding[2 * i + 0] = pad_front out_padding[2 * i + 1] = pad_rear return out_padding def filter2d( input: torch.Tensor, kernel: torch.Tensor, border_type: str = 'reflect', normalized: bool = False, padding: str = 'same', ) -> torch.Tensor: r"""Convolve a tensor with a 2d kernel. The function applies a given kernel to a tensor. The kernel is applied independently at each depth channel of the tensor. Before applying the kernel, the function applies padding according to the specified mode so that the output remains in the same shape. Args: input: the input tensor with shape of :math:`(B, C, H, W)`. kernel: the kernel to be convolved with the input tensor. The kernel shape must be :math:`(1, kH, kW)` or :math:`(B, kH, kW)`. border_type: the padding mode to be applied before convolving. The expected modes are: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. normalized: If True, kernel will be L1 normalized. padding: This defines the type of padding. 2 modes available ``'same'`` or ``'valid'``. Return: torch.Tensor: the convolved tensor of same size and numbers of channels as the input with shape :math:`(B, C, H, W)`. Example: >>> input = torch.tensor([[[ ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 5., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.],]]]) >>> kernel = torch.ones(1, 3, 3) >>> filter2d(input, kernel, padding='same') tensor([[[[0., 0., 0., 0., 0.], [0., 5., 5., 5., 0.], [0., 5., 5., 5., 0.], [0., 5., 5., 5., 0.], [0., 0., 0., 0., 0.]]]]) """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input input is not torch.Tensor. Got {type(input)}") if not isinstance(kernel, torch.Tensor): raise TypeError(f"Input kernel is not torch.Tensor. Got {type(kernel)}") if not isinstance(border_type, str): raise TypeError(f"Input border_type is not string. Got {type(border_type)}") if border_type not in ['constant', 'reflect', 'replicate', 'circular']: raise ValueError( f"Invalid border type, we expect 'constant', \ 'reflect', 'replicate', 'circular'. Got:{border_type}" ) if not isinstance(padding, str): raise TypeError(f"Input padding is not string. Got {type(padding)}") if padding not in ['valid', 'same']: raise ValueError(f"Invalid padding mode, we expect 'valid' or 'same'. Got: {padding}") if not len(input.shape) == 4: raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {input.shape}") if (not len(kernel.shape) == 3) and not ((kernel.shape[0] == 0) or (kernel.shape[0] == input.shape[0])): raise ValueError(f"Invalid kernel shape, we expect 1xHxW or BxHxW. Got: {kernel.shape}") # prepare kernel b, c, h, w = input.shape tmp_kernel: torch.Tensor = kernel.unsqueeze(1).to(input) if normalized: tmp_kernel = normalize_kernel2d(tmp_kernel) tmp_kernel = tmp_kernel.expand(-1, c, -1, -1) height, width = tmp_kernel.shape[-2:] # pad the input tensor if padding == 'same': padding_shape: List[int] = _compute_padding([height, width]) input = F.pad(input, padding_shape, mode=border_type) # kernel and input tensor reshape to align element-wise or batch-wise params tmp_kernel = tmp_kernel.reshape(-1, 1, height, width) input = input.view(-1, tmp_kernel.size(0), input.size(-2), input.size(-1)) # convolve the tensor with the kernel. output = F.conv2d(input, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1) if padding == 'same': out = output.view(b, c, h, w) else: out = output.view(b, c, h - height + 1, w - width + 1) return out def filter2d_separable( input: torch.Tensor, kernel_x: torch.Tensor, kernel_y: torch.Tensor, border_type: str = 'reflect', normalized: bool = False, padding: str = 'same', ) -> torch.Tensor: r"""Convolve a tensor with two 1d kernels, in x and y directions. The function applies a given kernel to a tensor. The kernel is applied independently at each depth channel of the tensor. Before applying the kernel, the function applies padding according to the specified mode so that the output remains in the same shape. Args: input: the input tensor with shape of :math:`(B, C, H, W)`. kernel_x: the kernel to be convolved with the input tensor. The kernel shape must be :math:`(1, kW)` or :math:`(B, kW)`. kernel_y: the kernel to be convolved with the input tensor. The kernel shape must be :math:`(1, kH)` or :math:`(B, kH)`. border_type: the padding mode to be applied before convolving. The expected modes are: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. normalized: If True, kernel will be L1 normalized. padding: This defines the type of padding. 2 modes available ``'same'`` or ``'valid'``. Return: torch.Tensor: the convolved tensor of same size and numbers of channels as the input with shape :math:`(B, C, H, W)`. Example: >>> input = torch.tensor([[[ ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 5., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.],]]]) >>> kernel = torch.ones(1, 3) >>> filter2d_separable(input, kernel, kernel, padding='same') tensor([[[[0., 0., 0., 0., 0.], [0., 5., 5., 5., 0.], [0., 5., 5., 5., 0.], [0., 5., 5., 5., 0.], [0., 0., 0., 0., 0.]]]]) """ out_x = filter2d(input, kernel_x.unsqueeze(0), border_type, normalized, padding) out = filter2d(out_x, kernel_y.unsqueeze(-1), border_type, normalized, padding) return out def filter3d( input: torch.Tensor, kernel: torch.Tensor, border_type: str = 'replicate', normalized: bool = False ) -> torch.Tensor: r"""Convolve a tensor with a 3d kernel. The function applies a given kernel to a tensor. The kernel is applied independently at each depth channel of the tensor. Before applying the kernel, the function applies padding according to the specified mode so that the output remains in the same shape. Args: input: the input tensor with shape of :math:`(B, C, D, H, W)`. kernel: the kernel to be convolved with the input tensor. The kernel shape must be :math:`(1, kD, kH, kW)` or :math:`(B, kD, kH, kW)`. border_type: the padding mode to be applied before convolving. The expected modes are: ``'constant'``, ``'replicate'`` or ``'circular'``. normalized: If True, kernel will be L1 normalized. Return: the convolved tensor of same size and numbers of channels as the input with shape :math:`(B, C, D, H, W)`. Example: >>> input = torch.tensor([[[ ... [[0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.]], ... [[0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 5., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.]], ... [[0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.], ... [0., 0., 0., 0., 0.]] ... ]]]) >>> kernel = torch.ones(1, 3, 3, 3) >>> filter3d(input, kernel) tensor([[[[[0., 0., 0., 0., 0.], [0., 5., 5., 5., 0.], [0., 5., 5., 5., 0.], [0., 5., 5., 5., 0.], [0., 0., 0., 0., 0.]], [[0., 0., 0., 0., 0.], [0., 5., 5., 5., 0.], [0., 5., 5., 5., 0.], [0., 5., 5., 5., 0.], [0., 0., 0., 0., 0.]], [[0., 0., 0., 0., 0.], [0., 5., 5., 5., 0.], [0., 5., 5., 5., 0.], [0., 5., 5., 5., 0.], [0., 0., 0., 0., 0.]]]]]) """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input border_type is not torch.Tensor. Got {type(input)}") if not isinstance(kernel, torch.Tensor): raise TypeError(f"Input border_type is not torch.Tensor. Got {type(kernel)}") if not isinstance(border_type, str): raise TypeError(f"Input border_type is not string. Got {type(kernel)}") if not len(input.shape) == 5: raise ValueError(f"Invalid input shape, we expect BxCxDxHxW. Got: {input.shape}") if not len(kernel.shape) == 4 and kernel.shape[0] != 1: raise ValueError(f"Invalid kernel shape, we expect 1xDxHxW. Got: {kernel.shape}") # prepare kernel b, c, d, h, w = input.shape tmp_kernel: torch.Tensor = kernel.unsqueeze(1).to(input) if normalized: bk, dk, hk, wk = kernel.shape tmp_kernel = normalize_kernel2d(tmp_kernel.view(bk, dk, hk * wk)).view_as(tmp_kernel) tmp_kernel = tmp_kernel.expand(-1, c, -1, -1, -1) # pad the input tensor depth, height, width = tmp_kernel.shape[-3:] padding_shape: List[int] = _compute_padding([depth, height, width]) input_pad: torch.Tensor = F.pad(input, padding_shape, mode=border_type) # kernel and input tensor reshape to align element-wise or batch-wise params tmp_kernel = tmp_kernel.reshape(-1, 1, depth, height, width) input_pad = input_pad.view(-1, tmp_kernel.size(0), input_pad.size(-3), input_pad.size(-2), input_pad.size(-1)) # convolve the tensor with the kernel. output = F.conv3d(input_pad, tmp_kernel, groups=tmp_kernel.size(0), padding=0, stride=1) return output.view(b, c, d, h, w) ================================================ FILE: propainter/model/canny/gaussian.py ================================================ from typing import Tuple import torch import torch.nn as nn from .filter import filter2d, filter2d_separable from .kernels import get_gaussian_kernel1d, get_gaussian_kernel2d def gaussian_blur2d( input: torch.Tensor, kernel_size: Tuple[int, int], sigma: Tuple[float, float], border_type: str = 'reflect', separable: bool = True, ) -> torch.Tensor: r"""Create an operator that blurs a tensor using a Gaussian filter. .. image:: _static/img/gaussian_blur2d.png The operator smooths the given tensor with a gaussian kernel by convolving it to each channel. It supports batched operation. Arguments: input: the input tensor with shape :math:`(B,C,H,W)`. kernel_size: the size of the kernel. sigma: the standard deviation of the kernel. border_type: the padding mode to be applied before convolving. The expected modes are: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'reflect'``. separable: run as composition of two 1d-convolutions. Returns: the blurred tensor with shape :math:`(B, C, H, W)`. .. note:: See a working example `here `__. Examples: >>> input = torch.rand(2, 4, 5, 5) >>> output = gaussian_blur2d(input, (3, 3), (1.5, 1.5)) >>> output.shape torch.Size([2, 4, 5, 5]) """ if separable: kernel_x: torch.Tensor = get_gaussian_kernel1d(kernel_size[1], sigma[1]) kernel_y: torch.Tensor = get_gaussian_kernel1d(kernel_size[0], sigma[0]) out = filter2d_separable(input, kernel_x[None], kernel_y[None], border_type) else: kernel: torch.Tensor = get_gaussian_kernel2d(kernel_size, sigma) out = filter2d(input, kernel[None], border_type) return out class GaussianBlur2d(nn.Module): r"""Create an operator that blurs a tensor using a Gaussian filter. The operator smooths the given tensor with a gaussian kernel by convolving it to each channel. It supports batched operation. Arguments: kernel_size: the size of the kernel. sigma: the standard deviation of the kernel. border_type: the padding mode to be applied before convolving. The expected modes are: ``'constant'``, ``'reflect'``, ``'replicate'`` or ``'circular'``. Default: ``'reflect'``. separable: run as composition of two 1d-convolutions. Returns: the blurred tensor. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, H, W)` Examples:: >>> input = torch.rand(2, 4, 5, 5) >>> gauss = GaussianBlur2d((3, 3), (1.5, 1.5)) >>> output = gauss(input) # 2x4x5x5 >>> output.shape torch.Size([2, 4, 5, 5]) """ def __init__( self, kernel_size: Tuple[int, int], sigma: Tuple[float, float], border_type: str = 'reflect', separable: bool = True, ) -> None: super().__init__() self.kernel_size: Tuple[int, int] = kernel_size self.sigma: Tuple[float, float] = sigma self.border_type = border_type self.separable = separable def __repr__(self) -> str: return ( self.__class__.__name__ + '(kernel_size=' + str(self.kernel_size) + ', ' + 'sigma=' + str(self.sigma) + ', ' + 'border_type=' + self.border_type + 'separable=' + str(self.separable) + ')' ) def forward(self, input: torch.Tensor) -> torch.Tensor: return gaussian_blur2d(input, self.kernel_size, self.sigma, self.border_type, self.separable) ================================================ FILE: propainter/model/canny/kernels.py ================================================ import math from math import sqrt from typing import List, Optional, Tuple import torch def normalize_kernel2d(input: torch.Tensor) -> torch.Tensor: r"""Normalize both derivative and smoothing kernel.""" if len(input.size()) < 2: raise TypeError(f"input should be at least 2D tensor. Got {input.size()}") norm: torch.Tensor = input.abs().sum(dim=-1).sum(dim=-1) return input / (norm.unsqueeze(-1).unsqueeze(-1)) def gaussian(window_size: int, sigma: float) -> torch.Tensor: device, dtype = None, None if isinstance(sigma, torch.Tensor): device, dtype = sigma.device, sigma.dtype x = torch.arange(window_size, device=device, dtype=dtype) - window_size // 2 if window_size % 2 == 0: x = x + 0.5 gauss = torch.exp((-x.pow(2.0) / (2 * sigma**2)).float()) return gauss / gauss.sum() def gaussian_discrete_erf(window_size: int, sigma) -> torch.Tensor: r"""Discrete Gaussian by interpolating the error function. Adapted from: https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py """ device = sigma.device if isinstance(sigma, torch.Tensor) else None sigma = torch.as_tensor(sigma, dtype=torch.float, device=device) x = torch.arange(window_size).float() - window_size // 2 t = 0.70710678 / torch.abs(sigma) gauss = 0.5 * ((t * (x + 0.5)).erf() - (t * (x - 0.5)).erf()) gauss = gauss.clamp(min=0) return gauss / gauss.sum() def _modified_bessel_0(x: torch.Tensor) -> torch.Tensor: r"""Adapted from: https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py """ if torch.abs(x) < 3.75: y = (x / 3.75) * (x / 3.75) return 1.0 + y * ( 3.5156229 + y * (3.0899424 + y * (1.2067492 + y * (0.2659732 + y * (0.360768e-1 + y * 0.45813e-2)))) ) ax = torch.abs(x) y = 3.75 / ax ans = 0.916281e-2 + y * (-0.2057706e-1 + y * (0.2635537e-1 + y * (-0.1647633e-1 + y * 0.392377e-2))) coef = 0.39894228 + y * (0.1328592e-1 + y * (0.225319e-2 + y * (-0.157565e-2 + y * ans))) return (torch.exp(ax) / torch.sqrt(ax)) * coef def _modified_bessel_1(x: torch.Tensor) -> torch.Tensor: r"""adapted from: https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py """ if torch.abs(x) < 3.75: y = (x / 3.75) * (x / 3.75) ans = 0.51498869 + y * (0.15084934 + y * (0.2658733e-1 + y * (0.301532e-2 + y * 0.32411e-3))) return torch.abs(x) * (0.5 + y * (0.87890594 + y * ans)) ax = torch.abs(x) y = 3.75 / ax ans = 0.2282967e-1 + y * (-0.2895312e-1 + y * (0.1787654e-1 - y * 0.420059e-2)) ans = 0.39894228 + y * (-0.3988024e-1 + y * (-0.362018e-2 + y * (0.163801e-2 + y * (-0.1031555e-1 + y * ans)))) ans = ans * torch.exp(ax) / torch.sqrt(ax) return -ans if x < 0.0 else ans def _modified_bessel_i(n: int, x: torch.Tensor) -> torch.Tensor: r"""adapted from: https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py """ if n < 2: raise ValueError("n must be greater than 1.") if x == 0.0: return x device = x.device tox = 2.0 / torch.abs(x) ans = torch.tensor(0.0, device=device) bip = torch.tensor(0.0, device=device) bi = torch.tensor(1.0, device=device) m = int(2 * (n + int(sqrt(40.0 * n)))) for j in range(m, 0, -1): bim = bip + float(j) * tox * bi bip = bi bi = bim if abs(bi) > 1.0e10: ans = ans * 1.0e-10 bi = bi * 1.0e-10 bip = bip * 1.0e-10 if j == n: ans = bip ans = ans * _modified_bessel_0(x) / bi return -ans if x < 0.0 and (n % 2) == 1 else ans def gaussian_discrete(window_size, sigma) -> torch.Tensor: r"""Discrete Gaussian kernel based on the modified Bessel functions. Adapted from: https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py """ device = sigma.device if isinstance(sigma, torch.Tensor) else None sigma = torch.as_tensor(sigma, dtype=torch.float, device=device) sigma2 = sigma * sigma tail = int(window_size // 2) out_pos: List[Optional[torch.Tensor]] = [None] * (tail + 1) out_pos[0] = _modified_bessel_0(sigma2) out_pos[1] = _modified_bessel_1(sigma2) for k in range(2, len(out_pos)): out_pos[k] = _modified_bessel_i(k, sigma2) out = out_pos[:0:-1] out.extend(out_pos) out = torch.stack(out) * torch.exp(sigma2) # type: ignore return out / out.sum() # type: ignore def laplacian_1d(window_size) -> torch.Tensor: r"""One could also use the Laplacian of Gaussian formula to design the filter.""" filter_1d = torch.ones(window_size) filter_1d[window_size // 2] = 1 - window_size laplacian_1d: torch.Tensor = filter_1d return laplacian_1d def get_box_kernel2d(kernel_size: Tuple[int, int]) -> torch.Tensor: r"""Utility function that returns a box filter.""" kx: float = float(kernel_size[0]) ky: float = float(kernel_size[1]) scale: torch.Tensor = torch.tensor(1.0) / torch.tensor([kx * ky]) tmp_kernel: torch.Tensor = torch.ones(1, kernel_size[0], kernel_size[1]) return scale.to(tmp_kernel.dtype) * tmp_kernel def get_binary_kernel2d(window_size: Tuple[int, int]) -> torch.Tensor: r"""Create a binary kernel to extract the patches. If the window size is HxW will create a (H*W)xHxW kernel. """ window_range: int = window_size[0] * window_size[1] kernel: torch.Tensor = torch.zeros(window_range, window_range) for i in range(window_range): kernel[i, i] += 1.0 return kernel.view(window_range, 1, window_size[0], window_size[1]) def get_sobel_kernel_3x3() -> torch.Tensor: """Utility function that returns a sobel kernel of 3x3.""" return torch.tensor([[-1.0, 0.0, 1.0], [-2.0, 0.0, 2.0], [-1.0, 0.0, 1.0]]) def get_sobel_kernel_5x5_2nd_order() -> torch.Tensor: """Utility function that returns a 2nd order sobel kernel of 5x5.""" return torch.tensor( [ [-1.0, 0.0, 2.0, 0.0, -1.0], [-4.0, 0.0, 8.0, 0.0, -4.0], [-6.0, 0.0, 12.0, 0.0, -6.0], [-4.0, 0.0, 8.0, 0.0, -4.0], [-1.0, 0.0, 2.0, 0.0, -1.0], ] ) def _get_sobel_kernel_5x5_2nd_order_xy() -> torch.Tensor: """Utility function that returns a 2nd order sobel kernel of 5x5.""" return torch.tensor( [ [-1.0, -2.0, 0.0, 2.0, 1.0], [-2.0, -4.0, 0.0, 4.0, 2.0], [0.0, 0.0, 0.0, 0.0, 0.0], [2.0, 4.0, 0.0, -4.0, -2.0], [1.0, 2.0, 0.0, -2.0, -1.0], ] ) def get_diff_kernel_3x3() -> torch.Tensor: """Utility function that returns a first order derivative kernel of 3x3.""" return torch.tensor([[-0.0, 0.0, 0.0], [-1.0, 0.0, 1.0], [-0.0, 0.0, 0.0]]) def get_diff_kernel3d(device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor: """Utility function that returns a first order derivative kernel of 3x3x3.""" kernel: torch.Tensor = torch.tensor( [ [ [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [-0.5, 0.0, 0.5], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], ], [ [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, -0.5, 0.0], [0.0, 0.0, 0.0], [0.0, 0.5, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], ], [ [[0.0, 0.0, 0.0], [0.0, -0.5, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.5, 0.0], [0.0, 0.0, 0.0]], ], ], device=device, dtype=dtype, ) return kernel.unsqueeze(1) def get_diff_kernel3d_2nd_order(device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor: """Utility function that returns a first order derivative kernel of 3x3x3.""" kernel: torch.Tensor = torch.tensor( [ [ [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, -2.0, 1.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], ], [ [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, -2.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], ], [ [[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, -2.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]], ], [ [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[1.0, 0.0, -1.0], [0.0, 0.0, 0.0], [-1.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], ], [ [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, -1.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, -1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], ], [ [[0.0, 0.0, 0.0], [1.0, 0.0, -1.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [-1.0, 0.0, 1.0], [0.0, 0.0, 0.0]], ], ], device=device, dtype=dtype, ) return kernel.unsqueeze(1) def get_sobel_kernel2d() -> torch.Tensor: kernel_x: torch.Tensor = get_sobel_kernel_3x3() kernel_y: torch.Tensor = kernel_x.transpose(0, 1) return torch.stack([kernel_x, kernel_y]) def get_diff_kernel2d() -> torch.Tensor: kernel_x: torch.Tensor = get_diff_kernel_3x3() kernel_y: torch.Tensor = kernel_x.transpose(0, 1) return torch.stack([kernel_x, kernel_y]) def get_sobel_kernel2d_2nd_order() -> torch.Tensor: gxx: torch.Tensor = get_sobel_kernel_5x5_2nd_order() gyy: torch.Tensor = gxx.transpose(0, 1) gxy: torch.Tensor = _get_sobel_kernel_5x5_2nd_order_xy() return torch.stack([gxx, gxy, gyy]) def get_diff_kernel2d_2nd_order() -> torch.Tensor: gxx: torch.Tensor = torch.tensor([[0.0, 0.0, 0.0], [1.0, -2.0, 1.0], [0.0, 0.0, 0.0]]) gyy: torch.Tensor = gxx.transpose(0, 1) gxy: torch.Tensor = torch.tensor([[-1.0, 0.0, 1.0], [0.0, 0.0, 0.0], [1.0, 0.0, -1.0]]) return torch.stack([gxx, gxy, gyy]) def get_spatial_gradient_kernel2d(mode: str, order: int) -> torch.Tensor: r"""Function that returns kernel for 1st or 2nd order image gradients, using one of the following operators: sobel, diff. """ if mode not in ['sobel', 'diff']: raise TypeError( "mode should be either sobel\ or diff. Got {}".format( mode ) ) if order not in [1, 2]: raise TypeError( "order should be either 1 or 2\ Got {}".format( order ) ) if mode == 'sobel' and order == 1: kernel: torch.Tensor = get_sobel_kernel2d() elif mode == 'sobel' and order == 2: kernel = get_sobel_kernel2d_2nd_order() elif mode == 'diff' and order == 1: kernel = get_diff_kernel2d() elif mode == 'diff' and order == 2: kernel = get_diff_kernel2d_2nd_order() else: raise NotImplementedError("") return kernel def get_spatial_gradient_kernel3d(mode: str, order: int, device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor: r"""Function that returns kernel for 1st or 2nd order scale pyramid gradients, using one of the following operators: sobel, diff.""" if mode not in ['sobel', 'diff']: raise TypeError( "mode should be either sobel\ or diff. Got {}".format( mode ) ) if order not in [1, 2]: raise TypeError( "order should be either 1 or 2\ Got {}".format( order ) ) if mode == 'sobel': raise NotImplementedError("Sobel kernel for 3d gradient is not implemented yet") if mode == 'diff' and order == 1: kernel = get_diff_kernel3d(device, dtype) elif mode == 'diff' and order == 2: kernel = get_diff_kernel3d_2nd_order(device, dtype) else: raise NotImplementedError("") return kernel def get_gaussian_kernel1d(kernel_size: int, sigma: float, force_even: bool = False) -> torch.Tensor: r"""Function that returns Gaussian filter coefficients. Args: kernel_size: filter size. It should be odd and positive. sigma: gaussian standard deviation. force_even: overrides requirement for odd kernel size. Returns: 1D tensor with gaussian filter coefficients. Shape: - Output: :math:`(\text{kernel_size})` Examples: >>> get_gaussian_kernel1d(3, 2.5) tensor([0.3243, 0.3513, 0.3243]) >>> get_gaussian_kernel1d(5, 1.5) tensor([0.1201, 0.2339, 0.2921, 0.2339, 0.1201]) """ if not isinstance(kernel_size, int) or ((kernel_size % 2 == 0) and not force_even) or (kernel_size <= 0): raise TypeError("kernel_size must be an odd positive integer. " "Got {}".format(kernel_size)) window_1d: torch.Tensor = gaussian(kernel_size, sigma) return window_1d def get_gaussian_discrete_kernel1d(kernel_size: int, sigma: float, force_even: bool = False) -> torch.Tensor: r"""Function that returns Gaussian filter coefficients based on the modified Bessel functions. Adapted from: https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py. Args: kernel_size: filter size. It should be odd and positive. sigma: gaussian standard deviation. force_even: overrides requirement for odd kernel size. Returns: 1D tensor with gaussian filter coefficients. Shape: - Output: :math:`(\text{kernel_size})` Examples: >>> get_gaussian_discrete_kernel1d(3, 2.5) tensor([0.3235, 0.3531, 0.3235]) >>> get_gaussian_discrete_kernel1d(5, 1.5) tensor([0.1096, 0.2323, 0.3161, 0.2323, 0.1096]) """ if not isinstance(kernel_size, int) or ((kernel_size % 2 == 0) and not force_even) or (kernel_size <= 0): raise TypeError("kernel_size must be an odd positive integer. " "Got {}".format(kernel_size)) window_1d = gaussian_discrete(kernel_size, sigma) return window_1d def get_gaussian_erf_kernel1d(kernel_size: int, sigma: float, force_even: bool = False) -> torch.Tensor: r"""Function that returns Gaussian filter coefficients by interpolating the error function, adapted from: https://github.com/Project-MONAI/MONAI/blob/master/monai/networks/layers/convutils.py. Args: kernel_size: filter size. It should be odd and positive. sigma: gaussian standard deviation. force_even: overrides requirement for odd kernel size. Returns: 1D tensor with gaussian filter coefficients. Shape: - Output: :math:`(\text{kernel_size})` Examples: >>> get_gaussian_erf_kernel1d(3, 2.5) tensor([0.3245, 0.3511, 0.3245]) >>> get_gaussian_erf_kernel1d(5, 1.5) tensor([0.1226, 0.2331, 0.2887, 0.2331, 0.1226]) """ if not isinstance(kernel_size, int) or ((kernel_size % 2 == 0) and not force_even) or (kernel_size <= 0): raise TypeError("kernel_size must be an odd positive integer. " "Got {}".format(kernel_size)) window_1d = gaussian_discrete_erf(kernel_size, sigma) return window_1d def get_gaussian_kernel2d( kernel_size: Tuple[int, int], sigma: Tuple[float, float], force_even: bool = False ) -> torch.Tensor: r"""Function that returns Gaussian filter matrix coefficients. Args: kernel_size: filter sizes in the x and y direction. Sizes should be odd and positive. sigma: gaussian standard deviation in the x and y direction. force_even: overrides requirement for odd kernel size. Returns: 2D tensor with gaussian filter matrix coefficients. Shape: - Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)` Examples: >>> get_gaussian_kernel2d((3, 3), (1.5, 1.5)) tensor([[0.0947, 0.1183, 0.0947], [0.1183, 0.1478, 0.1183], [0.0947, 0.1183, 0.0947]]) >>> get_gaussian_kernel2d((3, 5), (1.5, 1.5)) tensor([[0.0370, 0.0720, 0.0899, 0.0720, 0.0370], [0.0462, 0.0899, 0.1123, 0.0899, 0.0462], [0.0370, 0.0720, 0.0899, 0.0720, 0.0370]]) """ if not isinstance(kernel_size, tuple) or len(kernel_size) != 2: raise TypeError(f"kernel_size must be a tuple of length two. Got {kernel_size}") if not isinstance(sigma, tuple) or len(sigma) != 2: raise TypeError(f"sigma must be a tuple of length two. Got {sigma}") ksize_x, ksize_y = kernel_size sigma_x, sigma_y = sigma kernel_x: torch.Tensor = get_gaussian_kernel1d(ksize_x, sigma_x, force_even) kernel_y: torch.Tensor = get_gaussian_kernel1d(ksize_y, sigma_y, force_even) kernel_2d: torch.Tensor = torch.matmul(kernel_x.unsqueeze(-1), kernel_y.unsqueeze(-1).t()) return kernel_2d def get_laplacian_kernel1d(kernel_size: int) -> torch.Tensor: r"""Function that returns the coefficients of a 1D Laplacian filter. Args: kernel_size: filter size. It should be odd and positive. Returns: 1D tensor with laplacian filter coefficients. Shape: - Output: math:`(\text{kernel_size})` Examples: >>> get_laplacian_kernel1d(3) tensor([ 1., -2., 1.]) >>> get_laplacian_kernel1d(5) tensor([ 1., 1., -4., 1., 1.]) """ if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or kernel_size <= 0: raise TypeError(f"ksize must be an odd positive integer. Got {kernel_size}") window_1d: torch.Tensor = laplacian_1d(kernel_size) return window_1d def get_laplacian_kernel2d(kernel_size: int) -> torch.Tensor: r"""Function that returns Gaussian filter matrix coefficients. Args: kernel_size: filter size should be odd. Returns: 2D tensor with laplacian filter matrix coefficients. Shape: - Output: :math:`(\text{kernel_size}_x, \text{kernel_size}_y)` Examples: >>> get_laplacian_kernel2d(3) tensor([[ 1., 1., 1.], [ 1., -8., 1.], [ 1., 1., 1.]]) >>> get_laplacian_kernel2d(5) tensor([[ 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1.], [ 1., 1., -24., 1., 1.], [ 1., 1., 1., 1., 1.], [ 1., 1., 1., 1., 1.]]) """ if not isinstance(kernel_size, int) or kernel_size % 2 == 0 or kernel_size <= 0: raise TypeError(f"ksize must be an odd positive integer. Got {kernel_size}") kernel = torch.ones((kernel_size, kernel_size)) mid = kernel_size // 2 kernel[mid, mid] = 1 - kernel_size**2 kernel_2d: torch.Tensor = kernel return kernel_2d def get_pascal_kernel_2d(kernel_size: int, norm: bool = True) -> torch.Tensor: """Generate pascal filter kernel by kernel size. Args: kernel_size: height and width of the kernel. norm: if to normalize the kernel or not. Default: True. Returns: kernel shaped as :math:`(kernel_size, kernel_size)` Examples: >>> get_pascal_kernel_2d(1) tensor([[1.]]) >>> get_pascal_kernel_2d(4) tensor([[0.0156, 0.0469, 0.0469, 0.0156], [0.0469, 0.1406, 0.1406, 0.0469], [0.0469, 0.1406, 0.1406, 0.0469], [0.0156, 0.0469, 0.0469, 0.0156]]) >>> get_pascal_kernel_2d(4, norm=False) tensor([[1., 3., 3., 1.], [3., 9., 9., 3.], [3., 9., 9., 3.], [1., 3., 3., 1.]]) """ a = get_pascal_kernel_1d(kernel_size) filt = a[:, None] * a[None, :] if norm: filt = filt / torch.sum(filt) return filt def get_pascal_kernel_1d(kernel_size: int, norm: bool = False) -> torch.Tensor: """Generate Yang Hui triangle (Pascal's triangle) by a given number. Args: kernel_size: height and width of the kernel. norm: if to normalize the kernel or not. Default: False. Returns: kernel shaped as :math:`(kernel_size,)` Examples: >>> get_pascal_kernel_1d(1) tensor([1.]) >>> get_pascal_kernel_1d(2) tensor([1., 1.]) >>> get_pascal_kernel_1d(3) tensor([1., 2., 1.]) >>> get_pascal_kernel_1d(4) tensor([1., 3., 3., 1.]) >>> get_pascal_kernel_1d(5) tensor([1., 4., 6., 4., 1.]) >>> get_pascal_kernel_1d(6) tensor([ 1., 5., 10., 10., 5., 1.]) """ pre: List[float] = [] cur: List[float] = [] for i in range(kernel_size): cur = [1.0] * (i + 1) for j in range(1, i // 2 + 1): value = pre[j - 1] + pre[j] cur[j] = value if i != 2 * j: cur[-j - 1] = value pre = cur out = torch.as_tensor(cur) if norm: out = out / torch.sum(out) return out def get_canny_nms_kernel(device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor: """Utility function that returns 3x3 kernels for the Canny Non-maximal suppression.""" kernel: torch.Tensor = torch.tensor( [ [[0.0, 0.0, 0.0], [0.0, 1.0, -1.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, -1.0]], [[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, -1.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 1.0, 0.0], [-1.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [-1.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[-1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, -1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, -1.0], [0.0, 1.0, 0.0], [0.0, 0.0, 0.0]], ], device=device, dtype=dtype, ) return kernel.unsqueeze(1) def get_hysteresis_kernel(device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor: """Utility function that returns the 3x3 kernels for the Canny hysteresis.""" kernel: torch.Tensor = torch.tensor( [ [[0.0, 0.0, 0.0], [0.0, 0.0, 1.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 1.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 1.0, 0.0]], [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [1.0, 0.0, 0.0]], [[0.0, 0.0, 0.0], [1.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 1.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], [[0.0, 0.0, 1.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]], ], device=device, dtype=dtype, ) return kernel.unsqueeze(1) def get_hanning_kernel1d(kernel_size: int, device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor: r"""Returns Hanning (also known as Hann) kernel, used in signal processing and KCF tracker. .. math:: w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right) \\qquad 0 \\leq n \\leq M-1 See further in numpy docs https://numpy.org/doc/stable/reference/generated/numpy.hanning.html Args: kernel_size: The size the of the kernel. It should be positive. Returns: 1D tensor with Hanning filter coefficients. .. math:: w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right) Shape: - Output: math:`(\text{kernel_size})` Examples: >>> get_hanning_kernel1d(4) tensor([0.0000, 0.7500, 0.7500, 0.0000]) """ if not isinstance(kernel_size, int) or kernel_size <= 2: raise TypeError(f"ksize must be an positive integer > 2. Got {kernel_size}") x: torch.Tensor = torch.arange(kernel_size, device=device, dtype=dtype) x = 0.5 - 0.5 * torch.cos(2.0 * math.pi * x / float(kernel_size - 1)) return x def get_hanning_kernel2d(kernel_size: Tuple[int, int], device=torch.device('cpu'), dtype=torch.float) -> torch.Tensor: r"""Returns 2d Hanning kernel, used in signal processing and KCF tracker. Args: kernel_size: The size of the kernel for the filter. It should be positive. Returns: 2D tensor with Hanning filter coefficients. .. math:: w(n) = 0.5 - 0.5cos\\left(\\frac{2\\pi{n}}{M-1}\\right) Shape: - Output: math:`(\text{kernel_size[0], kernel_size[1]})` """ if kernel_size[0] <= 2 or kernel_size[1] <= 2: raise TypeError(f"ksize must be an tuple of positive integers > 2. Got {kernel_size}") ky: torch.Tensor = get_hanning_kernel1d(kernel_size[0], device, dtype)[None].T kx: torch.Tensor = get_hanning_kernel1d(kernel_size[1], device, dtype)[None] kernel2d = ky @ kx return kernel2d ================================================ FILE: propainter/model/canny/sobel.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from .kernels import get_spatial_gradient_kernel2d, get_spatial_gradient_kernel3d, normalize_kernel2d def spatial_gradient(input: torch.Tensor, mode: str = 'sobel', order: int = 1, normalized: bool = True) -> torch.Tensor: r"""Compute the first order image derivative in both x and y using a Sobel operator. .. image:: _static/img/spatial_gradient.png Args: input: input image tensor with shape :math:`(B, C, H, W)`. mode: derivatives modality, can be: `sobel` or `diff`. order: the order of the derivatives. normalized: whether the output is normalized. Return: the derivatives of the input feature map. with shape :math:`(B, C, 2, H, W)`. .. note:: See a working example `here `__. Examples: >>> input = torch.rand(1, 3, 4, 4) >>> output = spatial_gradient(input) # 1x3x2x4x4 >>> output.shape torch.Size([1, 3, 2, 4, 4]) """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(input)}") if not len(input.shape) == 4: raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {input.shape}") # allocate kernel kernel: torch.Tensor = get_spatial_gradient_kernel2d(mode, order) if normalized: kernel = normalize_kernel2d(kernel) # prepare kernel b, c, h, w = input.shape tmp_kernel: torch.Tensor = kernel.to(input).detach() tmp_kernel = tmp_kernel.unsqueeze(1).unsqueeze(1) # convolve input tensor with sobel kernel kernel_flip: torch.Tensor = tmp_kernel.flip(-3) # Pad with "replicate for spatial dims, but with zeros for channel spatial_pad = [kernel.size(1) // 2, kernel.size(1) // 2, kernel.size(2) // 2, kernel.size(2) // 2] out_channels: int = 3 if order == 2 else 2 padded_inp: torch.Tensor = F.pad(input.reshape(b * c, 1, h, w), spatial_pad, 'replicate')[:, :, None] return F.conv3d(padded_inp, kernel_flip, padding=0).view(b, c, out_channels, h, w) def spatial_gradient3d(input: torch.Tensor, mode: str = 'diff', order: int = 1) -> torch.Tensor: r"""Compute the first and second order volume derivative in x, y and d using a diff operator. Args: input: input features tensor with shape :math:`(B, C, D, H, W)`. mode: derivatives modality, can be: `sobel` or `diff`. order: the order of the derivatives. Return: the spatial gradients of the input feature map with shape math:`(B, C, 3, D, H, W)` or :math:`(B, C, 6, D, H, W)`. Examples: >>> input = torch.rand(1, 4, 2, 4, 4) >>> output = spatial_gradient3d(input) >>> output.shape torch.Size([1, 4, 3, 2, 4, 4]) """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(input)}") if not len(input.shape) == 5: raise ValueError(f"Invalid input shape, we expect BxCxDxHxW. Got: {input.shape}") b, c, d, h, w = input.shape dev = input.device dtype = input.dtype if (mode == 'diff') and (order == 1): # we go for the special case implementation due to conv3d bad speed x: torch.Tensor = F.pad(input, 6 * [1], 'replicate') center = slice(1, -1) left = slice(0, -2) right = slice(2, None) out = torch.empty(b, c, 3, d, h, w, device=dev, dtype=dtype) out[..., 0, :, :, :] = x[..., center, center, right] - x[..., center, center, left] out[..., 1, :, :, :] = x[..., center, right, center] - x[..., center, left, center] out[..., 2, :, :, :] = x[..., right, center, center] - x[..., left, center, center] out = 0.5 * out else: # prepare kernel # allocate kernel kernel: torch.Tensor = get_spatial_gradient_kernel3d(mode, order) tmp_kernel: torch.Tensor = kernel.to(input).detach() tmp_kernel = tmp_kernel.repeat(c, 1, 1, 1, 1) # convolve input tensor with grad kernel kernel_flip: torch.Tensor = tmp_kernel.flip(-3) # Pad with "replicate for spatial dims, but with zeros for channel spatial_pad = [ kernel.size(2) // 2, kernel.size(2) // 2, kernel.size(3) // 2, kernel.size(3) // 2, kernel.size(4) // 2, kernel.size(4) // 2, ] out_ch: int = 6 if order == 2 else 3 out = F.conv3d(F.pad(input, spatial_pad, 'replicate'), kernel_flip, padding=0, groups=c).view( b, c, out_ch, d, h, w ) return out def sobel(input: torch.Tensor, normalized: bool = True, eps: float = 1e-6) -> torch.Tensor: r"""Compute the Sobel operator and returns the magnitude per channel. .. image:: _static/img/sobel.png Args: input: the input image with shape :math:`(B,C,H,W)`. normalized: if True, L1 norm of the kernel is set to 1. eps: regularization number to avoid NaN during backprop. Return: the sobel edge gradient magnitudes map with shape :math:`(B,C,H,W)`. .. note:: See a working example `here `__. Example: >>> input = torch.rand(1, 3, 4, 4) >>> output = sobel(input) # 1x3x4x4 >>> output.shape torch.Size([1, 3, 4, 4]) """ if not isinstance(input, torch.Tensor): raise TypeError(f"Input type is not a torch.Tensor. Got {type(input)}") if not len(input.shape) == 4: raise ValueError(f"Invalid input shape, we expect BxCxHxW. Got: {input.shape}") # comput the x/y gradients edges: torch.Tensor = spatial_gradient(input, normalized=normalized) # unpack the edges gx: torch.Tensor = edges[:, :, 0] gy: torch.Tensor = edges[:, :, 1] # compute gradient maginitude magnitude: torch.Tensor = torch.sqrt(gx * gx + gy * gy + eps) return magnitude class SpatialGradient(nn.Module): r"""Compute the first order image derivative in both x and y using a Sobel operator. Args: mode: derivatives modality, can be: `sobel` or `diff`. order: the order of the derivatives. normalized: whether the output is normalized. Return: the sobel edges of the input feature map. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, 2, H, W)` Examples: >>> input = torch.rand(1, 3, 4, 4) >>> output = SpatialGradient()(input) # 1x3x2x4x4 """ def __init__(self, mode: str = 'sobel', order: int = 1, normalized: bool = True) -> None: super().__init__() self.normalized: bool = normalized self.order: int = order self.mode: str = mode def __repr__(self) -> str: return ( self.__class__.__name__ + '(' 'order=' + str(self.order) + ', ' + 'normalized=' + str(self.normalized) + ', ' + 'mode=' + self.mode + ')' ) def forward(self, input: torch.Tensor) -> torch.Tensor: return spatial_gradient(input, self.mode, self.order, self.normalized) class SpatialGradient3d(nn.Module): r"""Compute the first and second order volume derivative in x, y and d using a diff operator. Args: mode: derivatives modality, can be: `sobel` or `diff`. order: the order of the derivatives. Return: the spatial gradients of the input feature map. Shape: - Input: :math:`(B, C, D, H, W)`. D, H, W are spatial dimensions, gradient is calculated w.r.t to them. - Output: :math:`(B, C, 3, D, H, W)` or :math:`(B, C, 6, D, H, W)` Examples: >>> input = torch.rand(1, 4, 2, 4, 4) >>> output = SpatialGradient3d()(input) >>> output.shape torch.Size([1, 4, 3, 2, 4, 4]) """ def __init__(self, mode: str = 'diff', order: int = 1) -> None: super().__init__() self.order: int = order self.mode: str = mode self.kernel = get_spatial_gradient_kernel3d(mode, order) return def __repr__(self) -> str: return self.__class__.__name__ + '(' 'order=' + str(self.order) + ', ' + 'mode=' + self.mode + ')' def forward(self, input: torch.Tensor) -> torch.Tensor: # type: ignore return spatial_gradient3d(input, self.mode, self.order) class Sobel(nn.Module): r"""Compute the Sobel operator and returns the magnitude per channel. Args: normalized: if True, L1 norm of the kernel is set to 1. eps: regularization number to avoid NaN during backprop. Return: the sobel edge gradient magnitudes map. Shape: - Input: :math:`(B, C, H, W)` - Output: :math:`(B, C, H, W)` Examples: >>> input = torch.rand(1, 3, 4, 4) >>> output = Sobel()(input) # 1x3x4x4 """ def __init__(self, normalized: bool = True, eps: float = 1e-6) -> None: super().__init__() self.normalized: bool = normalized self.eps: float = eps def __repr__(self) -> str: return self.__class__.__name__ + '(' 'normalized=' + str(self.normalized) + ')' def forward(self, input: torch.Tensor) -> torch.Tensor: return sobel(input, self.normalized, self.eps) ================================================ FILE: propainter/model/misc.py ================================================ import os import re import random import time import torch import torch.nn as nn import logging import numpy as np from os import path as osp from packaging import version def constant_init(module, val, bias=0): if hasattr(module, 'weight') and module.weight is not None: nn.init.constant_(module.weight, val) if hasattr(module, 'bias') and module.bias is not None: nn.init.constant_(module.bias, bias) initialized_logger = {} def get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=None): """Get the root logger. The logger will be initialized if it has not been initialized. By default a StreamHandler will be added. If `log_file` is specified, a FileHandler will also be added. Args: logger_name (str): root logger name. Default: 'basicsr'. log_file (str | None): The log filename. If specified, a FileHandler will be added to the root logger. log_level (int): The root logger level. Note that only the process of rank 0 is affected, while other processes will set the level to "Error" and be silent most of the time. Returns: logging.Logger: The root logger. """ logger = logging.getLogger(logger_name) # if the logger has been initialized, just return it if logger_name in initialized_logger: return logger format_str = '%(asctime)s %(levelname)s: %(message)s' stream_handler = logging.StreamHandler() stream_handler.setFormatter(logging.Formatter(format_str)) logger.addHandler(stream_handler) logger.propagate = False if log_file is not None: logger.setLevel(log_level) # add file handler # file_handler = logging.FileHandler(log_file, 'w') file_handler = logging.FileHandler(log_file, 'a') #Shangchen: keep the previous log file_handler.setFormatter(logging.Formatter(format_str)) file_handler.setLevel(log_level) logger.addHandler(file_handler) initialized_logger[logger_name] = True return logger required_version = version.parse("1.12.0") current_version = version.parse(torch.__version__) IS_HIGH_VERSION = current_version >= required_version def gpu_is_available(): if IS_HIGH_VERSION: if torch.backends.mps.is_available(): return True return True if torch.cuda.is_available() and torch.backends.cudnn.is_available() else False def get_device(gpu_id=None): if gpu_id is None: gpu_str = '' elif isinstance(gpu_id, int): gpu_str = f':{gpu_id}' else: raise TypeError('Input should be int value.') if IS_HIGH_VERSION: if torch.backends.mps.is_available(): return torch.device('mps'+gpu_str) return torch.device('cuda'+gpu_str if torch.cuda.is_available() and torch.backends.cudnn.is_available() else 'cpu') def set_random_seed(seed): """Set random seeds.""" random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) def get_time_str(): return time.strftime('%Y%m%d_%H%M%S', time.localtime()) def scandir(dir_path, suffix=None, recursive=False, full_path=False): """Scan a directory to find the interested files. Args: dir_path (str): Path of the directory. suffix (str | tuple(str), optional): File suffix that we are interested in. Default: None. recursive (bool, optional): If set to True, recursively scan the directory. Default: False. full_path (bool, optional): If set to True, include the dir_path. Default: False. Returns: A generator for all the interested files with relative pathes. """ if (suffix is not None) and not isinstance(suffix, (str, tuple)): raise TypeError('"suffix" must be a string or tuple of strings') root = dir_path def _scandir(dir_path, suffix, recursive): for entry in os.scandir(dir_path): if not entry.name.startswith('.') and entry.is_file(): if full_path: return_path = entry.path else: return_path = osp.relpath(entry.path, root) if suffix is None: yield return_path elif return_path.endswith(suffix): yield return_path else: if recursive: yield from _scandir(entry.path, suffix=suffix, recursive=recursive) else: continue return _scandir(dir_path, suffix=suffix, recursive=recursive) ================================================ FILE: propainter/model/modules/__init__.py ================================================ ================================================ FILE: propainter/model/modules/base_module.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F from functools import reduce class BaseNetwork(nn.Module): def __init__(self): super(BaseNetwork, self).__init__() def print_network(self): if isinstance(self, list): self = self[0] num_params = 0 for param in self.parameters(): num_params += param.numel() print( 'Network [%s] was created. Total number of parameters: %.1f million. ' 'To see the architecture, do print(network).' % (type(self).__name__, num_params / 1000000)) def init_weights(self, init_type='normal', gain=0.02): ''' initialize network's weights init_type: normal | xavier | kaiming | orthogonal https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/9451e70673400885567d08a9e97ade2524c700d0/models/networks.py#L39 ''' def init_func(m): classname = m.__class__.__name__ if classname.find('InstanceNorm2d') != -1: if hasattr(m, 'weight') and m.weight is not None: nn.init.constant_(m.weight.data, 1.0) if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias.data, 0.0) elif hasattr(m, 'weight') and (classname.find('Conv') != -1 or classname.find('Linear') != -1): if init_type == 'normal': nn.init.normal_(m.weight.data, 0.0, gain) elif init_type == 'xavier': nn.init.xavier_normal_(m.weight.data, gain=gain) elif init_type == 'xavier_uniform': nn.init.xavier_uniform_(m.weight.data, gain=1.0) elif init_type == 'kaiming': nn.init.kaiming_normal_(m.weight.data, a=0, mode='fan_in') elif init_type == 'orthogonal': nn.init.orthogonal_(m.weight.data, gain=gain) elif init_type == 'none': # uses pytorch's default init method m.reset_parameters() else: raise NotImplementedError( 'initialization method [%s] is not implemented' % init_type) if hasattr(m, 'bias') and m.bias is not None: nn.init.constant_(m.bias.data, 0.0) self.apply(init_func) # propagate to children for m in self.children(): if hasattr(m, 'init_weights'): m.init_weights(init_type, gain) class Vec2Feat(nn.Module): def __init__(self, channel, hidden, kernel_size, stride, padding): super(Vec2Feat, self).__init__() self.relu = nn.LeakyReLU(0.2, inplace=True) c_out = reduce((lambda x, y: x * y), kernel_size) * channel self.embedding = nn.Linear(hidden, c_out) self.kernel_size = kernel_size self.stride = stride self.padding = padding self.bias_conv = nn.Conv2d(channel, channel, kernel_size=3, stride=1, padding=1) def forward(self, x, t, output_size): b_, _, _, _, c_ = x.shape x = x.view(b_, -1, c_) feat = self.embedding(x) b, _, c = feat.size() feat = feat.view(b * t, -1, c).permute(0, 2, 1) feat = F.fold(feat, output_size=output_size, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding) feat = self.bias_conv(feat) return feat class FusionFeedForward(nn.Module): def __init__(self, dim, hidden_dim=1960, t2t_params=None): super(FusionFeedForward, self).__init__() # We set hidden_dim as a default to 1960 self.fc1 = nn.Sequential(nn.Linear(dim, hidden_dim)) self.fc2 = nn.Sequential(nn.GELU(), nn.Linear(hidden_dim, dim)) assert t2t_params is not None self.t2t_params = t2t_params self.kernel_shape = reduce((lambda x, y: x * y), t2t_params['kernel_size']) # 49 def forward(self, x, output_size): n_vecs = 1 for i, d in enumerate(self.t2t_params['kernel_size']): n_vecs *= int((output_size[i] + 2 * self.t2t_params['padding'][i] - (d - 1) - 1) / self.t2t_params['stride'][i] + 1) x = self.fc1(x) b, n, c = x.size() normalizer = x.new_ones(b, n, self.kernel_shape).view(-1, n_vecs, self.kernel_shape).permute(0, 2, 1) normalizer = F.fold(normalizer, output_size=output_size, kernel_size=self.t2t_params['kernel_size'], padding=self.t2t_params['padding'], stride=self.t2t_params['stride']) x = F.fold(x.view(-1, n_vecs, c).permute(0, 2, 1), output_size=output_size, kernel_size=self.t2t_params['kernel_size'], padding=self.t2t_params['padding'], stride=self.t2t_params['stride']) x = F.unfold(x / normalizer, kernel_size=self.t2t_params['kernel_size'], padding=self.t2t_params['padding'], stride=self.t2t_params['stride']).permute( 0, 2, 1).contiguous().view(b, n, c) x = self.fc2(x) return x ================================================ FILE: propainter/model/modules/deformconv.py ================================================ import torch import torch.nn as nn from torch.nn import init as init from torch.nn.modules.utils import _pair, _single import math class ModulatedDeformConv2d(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, deform_groups=1, bias=True): super(ModulatedDeformConv2d, self).__init__() self.in_channels = in_channels self.out_channels = out_channels self.kernel_size = _pair(kernel_size) self.stride = stride self.padding = padding self.dilation = dilation self.groups = groups self.deform_groups = deform_groups self.with_bias = bias # enable compatibility with nn.Conv2d self.transposed = False self.output_padding = _single(0) self.weight = nn.Parameter(torch.Tensor(out_channels, in_channels // groups, *self.kernel_size)) if bias: self.bias = nn.Parameter(torch.Tensor(out_channels)) else: self.register_parameter('bias', None) self.init_weights() def init_weights(self): n = self.in_channels for k in self.kernel_size: n *= k stdv = 1. / math.sqrt(n) self.weight.data.uniform_(-stdv, stdv) if self.bias is not None: self.bias.data.zero_() if hasattr(self, 'conv_offset'): self.conv_offset.weight.data.zero_() self.conv_offset.bias.data.zero_() def forward(self, x, offset, mask): pass ================================================ FILE: propainter/model/modules/flow_comp_raft.py ================================================ import argparse import torch import torch.nn as nn import torch.nn.functional as F from ...RAFT import RAFT from .flow_loss_utils import flow_warp, ternary_loss2 # except: # from propainter.RAFT import RAFT # from propainter.model.modules.flow_loss_utils import flow_warp, ternary_loss2 def initialize_RAFT(model_path='weights/raft-things.pth', device='cuda'): """Initializes the RAFT model. """ args = argparse.ArgumentParser() args.raft_model = model_path args.small = False args.mixed_precision = False args.alternate_corr = False model = torch.nn.DataParallel(RAFT(args)) model.load_state_dict(torch.load(args.raft_model, map_location='cpu')) model = model.module model.to(device) return model class RAFT_bi(nn.Module): """Flow completion loss""" def __init__(self, model_path='weights/raft-things.pth', device='cuda'): super().__init__() self.fix_raft = initialize_RAFT(model_path, device=device) for p in self.fix_raft.parameters(): p.requires_grad = False self.l1_criterion = nn.L1Loss() self.eval() def forward(self, gt_local_frames, iters=20): b, l_t, c, h, w = gt_local_frames.size() # print(gt_local_frames.shape) with torch.no_grad(): gtlf_1 = gt_local_frames[:, :-1, :, :, :].reshape(-1, c, h, w) gtlf_2 = gt_local_frames[:, 1:, :, :, :].reshape(-1, c, h, w) # print(gtlf_1.shape) _, gt_flows_forward = self.fix_raft(gtlf_1, gtlf_2, iters=iters, test_mode=True) _, gt_flows_backward = self.fix_raft(gtlf_2, gtlf_1, iters=iters, test_mode=True) gt_flows_forward = gt_flows_forward.view(b, l_t-1, 2, h, w) gt_flows_backward = gt_flows_backward.view(b, l_t-1, 2, h, w) return gt_flows_forward, gt_flows_backward ################################################################################## def smoothness_loss(flow, cmask): delta_u, delta_v, mask = smoothness_deltas(flow) loss_u = charbonnier_loss(delta_u, cmask) loss_v = charbonnier_loss(delta_v, cmask) return loss_u + loss_v def smoothness_deltas(flow): """ flow: [b, c, h, w] """ mask_x = create_mask(flow, [[0, 0], [0, 1]]) mask_y = create_mask(flow, [[0, 1], [0, 0]]) mask = torch.cat((mask_x, mask_y), dim=1) mask = mask.to(flow.device) filter_x = torch.tensor([[0, 0, 0.], [0, 1, -1], [0, 0, 0]]) filter_y = torch.tensor([[0, 0, 0.], [0, 1, 0], [0, -1, 0]]) weights = torch.ones([2, 1, 3, 3]) weights[0, 0] = filter_x weights[1, 0] = filter_y weights = weights.to(flow.device) flow_u, flow_v = torch.split(flow, split_size_or_sections=1, dim=1) delta_u = F.conv2d(flow_u, weights, stride=1, padding=1) delta_v = F.conv2d(flow_v, weights, stride=1, padding=1) return delta_u, delta_v, mask def second_order_loss(flow, cmask): delta_u, delta_v, mask = second_order_deltas(flow) loss_u = charbonnier_loss(delta_u, cmask) loss_v = charbonnier_loss(delta_v, cmask) return loss_u + loss_v def charbonnier_loss(x, mask=None, truncate=None, alpha=0.45, beta=1.0, epsilon=0.001): """ Compute the generalized charbonnier loss of the difference tensor x All positions where mask == 0 are not taken into account x: a tensor of shape [b, c, h, w] mask: a mask of shape [b, mc, h, w], where mask channels must be either 1 or the same as the number of channels of x. Entries should be 0 or 1 return: loss """ b, c, h, w = x.shape norm = b * c * h * w error = torch.pow(torch.square(x * beta) + torch.square(torch.tensor(epsilon)), alpha) if mask is not None: error = mask * error if truncate is not None: error = torch.min(error, truncate) return torch.sum(error) / norm def second_order_deltas(flow): """ consider the single flow first flow shape: [b, c, h, w] """ # create mask mask_x = create_mask(flow, [[0, 0], [1, 1]]) mask_y = create_mask(flow, [[1, 1], [0, 0]]) mask_diag = create_mask(flow, [[1, 1], [1, 1]]) mask = torch.cat((mask_x, mask_y, mask_diag, mask_diag), dim=1) mask = mask.to(flow.device) filter_x = torch.tensor([[0, 0, 0.], [1, -2, 1], [0, 0, 0]]) filter_y = torch.tensor([[0, 1, 0.], [0, -2, 0], [0, 1, 0]]) filter_diag1 = torch.tensor([[1, 0, 0.], [0, -2, 0], [0, 0, 1]]) filter_diag2 = torch.tensor([[0, 0, 1.], [0, -2, 0], [1, 0, 0]]) weights = torch.ones([4, 1, 3, 3]) weights[0] = filter_x weights[1] = filter_y weights[2] = filter_diag1 weights[3] = filter_diag2 weights = weights.to(flow.device) # split the flow into flow_u and flow_v, conv them with the weights flow_u, flow_v = torch.split(flow, split_size_or_sections=1, dim=1) delta_u = F.conv2d(flow_u, weights, stride=1, padding=1) delta_v = F.conv2d(flow_v, weights, stride=1, padding=1) return delta_u, delta_v, mask def create_mask(tensor, paddings): """ tensor shape: [b, c, h, w] paddings: [2 x 2] shape list, the first row indicates up and down paddings the second row indicates left and right paddings | | | x | | x * x | | x | | | """ shape = tensor.shape inner_height = shape[2] - (paddings[0][0] + paddings[0][1]) inner_width = shape[3] - (paddings[1][0] + paddings[1][1]) inner = torch.ones([inner_height, inner_width]) torch_paddings = [paddings[1][0], paddings[1][1], paddings[0][0], paddings[0][1]] # left, right, up and down mask2d = F.pad(inner, pad=torch_paddings) mask3d = mask2d.unsqueeze(0).repeat(shape[0], 1, 1) mask4d = mask3d.unsqueeze(1) return mask4d.detach() def ternary_loss(flow_comp, flow_gt, mask, current_frame, shift_frame, scale_factor=1): if scale_factor != 1: current_frame = F.interpolate(current_frame, scale_factor=1 / scale_factor, mode='bilinear') shift_frame = F.interpolate(shift_frame, scale_factor=1 / scale_factor, mode='bilinear') warped_sc = flow_warp(shift_frame, flow_gt.permute(0, 2, 3, 1)) noc_mask = torch.exp(-50. * torch.sum(torch.abs(current_frame - warped_sc), dim=1).pow(2)).unsqueeze(1) warped_comp_sc = flow_warp(shift_frame, flow_comp.permute(0, 2, 3, 1)) loss = ternary_loss2(current_frame, warped_comp_sc, noc_mask, mask) return loss class FlowLoss(nn.Module): def __init__(self): super().__init__() self.l1_criterion = nn.L1Loss() def forward(self, pred_flows, gt_flows, masks, frames): # pred_flows: b t-1 2 h w loss = 0 warp_loss = 0 h, w = pred_flows[0].shape[-2:] masks = [masks[:,:-1,...].contiguous(), masks[:, 1:, ...].contiguous()] frames0 = frames[:,:-1,...] frames1 = frames[:,1:,...] current_frames = [frames0, frames1] next_frames = [frames1, frames0] for i in range(len(pred_flows)): # print(pred_flows[i].shape) combined_flow = pred_flows[i] * masks[i] + gt_flows[i] * (1-masks[i]) l1_loss = self.l1_criterion(pred_flows[i] * masks[i], gt_flows[i] * masks[i]) / torch.mean(masks[i]) l1_loss += self.l1_criterion(pred_flows[i] * (1-masks[i]), gt_flows[i] * (1-masks[i])) / torch.mean((1-masks[i])) smooth_loss = smoothness_loss(combined_flow.reshape(-1,2,h,w), masks[i].reshape(-1,1,h,w)) smooth_loss2 = second_order_loss(combined_flow.reshape(-1,2,h,w), masks[i].reshape(-1,1,h,w)) warp_loss_i = ternary_loss(combined_flow.reshape(-1,2,h,w), gt_flows[i].reshape(-1,2,h,w), masks[i].reshape(-1,1,h,w), current_frames[i].reshape(-1,3,h,w), next_frames[i].reshape(-1,3,h,w)) loss += l1_loss + smooth_loss + smooth_loss2 warp_loss += warp_loss_i return loss, warp_loss def edgeLoss(preds_edges, edges): """ Args: preds_edges: with shape [b, c, h , w] edges: with shape [b, c, h, w] Returns: Edge losses """ mask = (edges > 0.5).float() b, c, h, w = mask.shape num_pos = torch.sum(mask, dim=[1, 2, 3]).float() # Shape: [b,]. num_neg = c * h * w - num_pos # Shape: [b,]. neg_weights = (num_neg / (num_pos + num_neg)).unsqueeze(1).unsqueeze(2).unsqueeze(3) pos_weights = (num_pos / (num_pos + num_neg)).unsqueeze(1).unsqueeze(2).unsqueeze(3) weight = neg_weights * mask + pos_weights * (1 - mask) # weight for debug losses = F.binary_cross_entropy_with_logits(preds_edges.float(), edges.float(), weight=weight, reduction='none') loss = torch.mean(losses) return loss class EdgeLoss(nn.Module): def __init__(self): super().__init__() def forward(self, pred_edges, gt_edges, masks): # pred_flows: b t-1 1 h w loss = 0 h, w = pred_edges[0].shape[-2:] masks = [masks[:,:-1,...].contiguous(), masks[:, 1:, ...].contiguous()] for i in range(len(pred_edges)): # print(f'edges_{i}', torch.sum(gt_edges[i])) # debug combined_edge = pred_edges[i] * masks[i] + gt_edges[i] * (1-masks[i]) edge_loss = (edgeLoss(pred_edges[i].reshape(-1,1,h,w), gt_edges[i].reshape(-1,1,h,w)) \ + 5 * edgeLoss(combined_edge.reshape(-1,1,h,w), gt_edges[i].reshape(-1,1,h,w))) loss += edge_loss return loss class FlowSimpleLoss(nn.Module): def __init__(self): super().__init__() self.l1_criterion = nn.L1Loss() def forward(self, pred_flows, gt_flows): # pred_flows: b t-1 2 h w loss = 0 h, w = pred_flows[0].shape[-2:] h_orig, w_orig = gt_flows[0].shape[-2:] pred_flows = [f.view(-1, 2, h, w) for f in pred_flows] gt_flows = [f.view(-1, 2, h_orig, w_orig) for f in gt_flows] ds_factor = 1.0*h/h_orig gt_flows = [F.interpolate(f, scale_factor=ds_factor, mode='area') * ds_factor for f in gt_flows] for i in range(len(pred_flows)): loss += self.l1_criterion(pred_flows[i], gt_flows[i]) return loss ================================================ FILE: propainter/model/modules/flow_loss_utils.py ================================================ import torch import numpy as np import torch.nn as nn import torch.nn.functional as F def flow_warp(x, flow, interpolation='bilinear', padding_mode='zeros', align_corners=True): """Warp an image or a feature map with optical flow. Args: x (Tensor): Tensor with size (n, c, h, w). flow (Tensor): Tensor with size (n, h, w, 2). The last dimension is a two-channel, denoting the width and height relative offsets. Note that the values are not normalized to [-1, 1]. interpolation (str): Interpolation mode: 'nearest' or 'bilinear'. Default: 'bilinear'. padding_mode (str): Padding mode: 'zeros' or 'border' or 'reflection'. Default: 'zeros'. align_corners (bool): Whether align corners. Default: True. Returns: Tensor: Warped image or feature map. """ if x.size()[-2:] != flow.size()[1:3]: raise ValueError(f'The spatial sizes of input ({x.size()[-2:]}) and ' f'flow ({flow.size()[1:3]}) are not the same.') _, _, h, w = x.size() # create mesh grid device = flow.device grid_y, grid_x = torch.meshgrid(torch.arange(0, h, device=device), torch.arange(0, w, device=device)) grid = torch.stack((grid_x, grid_y), 2).type_as(x) # (w, h, 2) grid.requires_grad = False grid_flow = grid + flow # scale grid_flow to [-1,1] grid_flow_x = 2.0 * grid_flow[:, :, :, 0] / max(w - 1, 1) - 1.0 grid_flow_y = 2.0 * grid_flow[:, :, :, 1] / max(h - 1, 1) - 1.0 grid_flow = torch.stack((grid_flow_x, grid_flow_y), dim=3) output = F.grid_sample(x, grid_flow, mode=interpolation, padding_mode=padding_mode, align_corners=align_corners) return output # def image_warp(image, flow): # b, c, h, w = image.size() # device = image.device # flow = torch.cat([flow[:, 0:1, :, :] / ((w - 1.0) / 2.0), flow[:, 1:2, :, :] / ((h - 1.0) / 2.0)], dim=1) # normalize to [-1~1](from upper left to lower right # flow = flow.permute(0, 2, 3, 1) # if you wanna use grid_sample function, the channel(band) shape of show must be in the last dimension # x = np.linspace(-1, 1, w) # y = np.linspace(-1, 1, h) # X, Y = np.meshgrid(x, y) # grid = torch.cat((torch.from_numpy(X.astype('float32')).unsqueeze(0).unsqueeze(3), # torch.from_numpy(Y.astype('float32')).unsqueeze(0).unsqueeze(3)), 3).to(device) # output = torch.nn.functional.grid_sample(image, grid + flow, mode='bilinear', padding_mode='zeros') # return output def length_sq(x): return torch.sum(torch.square(x), dim=1, keepdim=True) def fbConsistencyCheck(flow_fw, flow_bw, alpha1=0.01, alpha2=0.5): flow_bw_warped = flow_warp(flow_bw, flow_fw.permute(0, 2, 3, 1)) # wb(wf(x)) flow_fw_warped = flow_warp(flow_fw, flow_bw.permute(0, 2, 3, 1)) # wf(wb(x)) flow_diff_fw = flow_fw + flow_bw_warped # wf + wb(wf(x)) flow_diff_bw = flow_bw + flow_fw_warped # wb + wf(wb(x)) mag_sq_fw = length_sq(flow_fw) + length_sq(flow_bw_warped) # |wf| + |wb(wf(x))| mag_sq_bw = length_sq(flow_bw) + length_sq(flow_fw_warped) # |wb| + |wf(wb(x))| occ_thresh_fw = alpha1 * mag_sq_fw + alpha2 occ_thresh_bw = alpha1 * mag_sq_bw + alpha2 fb_occ_fw = (length_sq(flow_diff_fw) > occ_thresh_fw).float() fb_occ_bw = (length_sq(flow_diff_bw) > occ_thresh_bw).float() return fb_occ_fw, fb_occ_bw # fb_occ_fw -> frame2 area occluded by frame1, fb_occ_bw -> frame1 area occluded by frame2 def rgb2gray(image): gray_image = image[:, 0] * 0.299 + image[:, 1] * 0.587 + 0.110 * image[:, 2] gray_image = gray_image.unsqueeze(1) return gray_image def ternary_transform(image, max_distance=1): device = image.device patch_size = 2 * max_distance + 1 intensities = rgb2gray(image) * 255 out_channels = patch_size * patch_size w = np.eye(out_channels).reshape(out_channels, 1, patch_size, patch_size) weights = torch.from_numpy(w).float().to(device) patches = F.conv2d(intensities, weights, stride=1, padding=1) transf = patches - intensities transf_norm = transf / torch.sqrt(0.81 + torch.square(transf)) return transf_norm def hamming_distance(t1, t2): dist = torch.square(t1 - t2) dist_norm = dist / (0.1 + dist) dist_sum = torch.sum(dist_norm, dim=1, keepdim=True) return dist_sum def create_mask(mask, paddings): """ padding: [[top, bottom], [left, right]] """ shape = mask.shape inner_height = shape[2] - (paddings[0][0] + paddings[0][1]) inner_width = shape[3] - (paddings[1][0] + paddings[1][1]) inner = torch.ones([inner_height, inner_width]) mask2d = F.pad(inner, pad=[paddings[1][0], paddings[1][1], paddings[0][0], paddings[0][1]]) mask3d = mask2d.unsqueeze(0) mask4d = mask3d.unsqueeze(0).repeat(shape[0], 1, 1, 1) return mask4d.detach() def ternary_loss2(frame1, warp_frame21, confMask, masks, max_distance=1): """ Args: frame1: torch tensor, with shape [b * t, c, h, w] warp_frame21: torch tensor, with shape [b * t, c, h, w] confMask: confidence mask, with shape [b * t, c, h, w] masks: torch tensor, with shape [b * t, c, h, w] max_distance: maximum distance. Returns: ternary loss """ t1 = ternary_transform(frame1) t21 = ternary_transform(warp_frame21) dist = hamming_distance(t1, t21) loss = torch.mean(dist * confMask * masks) / torch.mean(masks) return loss ================================================ FILE: propainter/model/modules/sparse_transformer.py ================================================ import math from functools import reduce import torch import torch.nn as nn import torch.nn.functional as F class SoftSplit(nn.Module): def __init__(self, channel, hidden, kernel_size, stride, padding): super(SoftSplit, self).__init__() self.kernel_size = kernel_size self.stride = stride self.padding = padding self.t2t = nn.Unfold(kernel_size=kernel_size, stride=stride, padding=padding) c_in = reduce((lambda x, y: x * y), kernel_size) * channel self.embedding = nn.Linear(c_in, hidden) def forward(self, x, b, output_size): f_h = int((output_size[0] + 2 * self.padding[0] - (self.kernel_size[0] - 1) - 1) / self.stride[0] + 1) f_w = int((output_size[1] + 2 * self.padding[1] - (self.kernel_size[1] - 1) - 1) / self.stride[1] + 1) feat = self.t2t(x) feat = feat.permute(0, 2, 1) # feat shape [b*t, num_vec, ks*ks*c] feat = self.embedding(feat) # feat shape after embedding [b, t*num_vec, hidden] feat = feat.view(b, -1, f_h, f_w, feat.size(2)) return feat class SoftComp(nn.Module): def __init__(self, channel, hidden, kernel_size, stride, padding): super(SoftComp, self).__init__() self.relu = nn.LeakyReLU(0.2, inplace=True) c_out = reduce((lambda x, y: x * y), kernel_size) * channel self.embedding = nn.Linear(hidden, c_out) self.kernel_size = kernel_size self.stride = stride self.padding = padding self.bias_conv = nn.Conv2d(channel, channel, kernel_size=3, stride=1, padding=1) def forward(self, x, t, output_size): b_, _, _, _, c_ = x.shape x = x.view(b_, -1, c_) feat = self.embedding(x) b, _, c = feat.size() feat = feat.view(b * t, -1, c).permute(0, 2, 1) feat = F.fold(feat, output_size=output_size, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding) feat = self.bias_conv(feat) return feat class FusionFeedForward(nn.Module): def __init__(self, dim, hidden_dim=1960, t2t_params=None): super(FusionFeedForward, self).__init__() # We set hidden_dim as a default to 1960 self.fc1 = nn.Sequential(nn.Linear(dim, hidden_dim)) self.fc2 = nn.Sequential(nn.GELU(), nn.Linear(hidden_dim, dim)) assert t2t_params is not None self.t2t_params = t2t_params self.kernel_shape = reduce((lambda x, y: x * y), t2t_params['kernel_size']) # 49 def forward(self, x, output_size): n_vecs = 1 for i, d in enumerate(self.t2t_params['kernel_size']): n_vecs *= int((output_size[i] + 2 * self.t2t_params['padding'][i] - (d - 1) - 1) / self.t2t_params['stride'][i] + 1) x = self.fc1(x) b, n, c = x.size() normalizer = x.new_ones(b, n, self.kernel_shape).view(-1, n_vecs, self.kernel_shape).permute(0, 2, 1) normalizer = F.fold(normalizer, output_size=output_size, kernel_size=self.t2t_params['kernel_size'], padding=self.t2t_params['padding'], stride=self.t2t_params['stride']) x = F.fold(x.view(-1, n_vecs, c).permute(0, 2, 1), output_size=output_size, kernel_size=self.t2t_params['kernel_size'], padding=self.t2t_params['padding'], stride=self.t2t_params['stride']) x = F.unfold(x / normalizer, kernel_size=self.t2t_params['kernel_size'], padding=self.t2t_params['padding'], stride=self.t2t_params['stride']).permute( 0, 2, 1).contiguous().view(b, n, c) x = self.fc2(x) return x def window_partition(x, window_size, n_head): """ Args: x: shape is (B, T, H, W, C) window_size (tuple[int]): window size Returns: windows: (B, num_windows_h, num_windows_w, n_head, T, window_size, window_size, C//n_head) """ B, T, H, W, C = x.shape x = x.view(B, T, H // window_size[0], window_size[0], W // window_size[1], window_size[1], n_head, C//n_head) windows = x.permute(0, 2, 4, 6, 1, 3, 5, 7).contiguous() return windows class SparseWindowAttention(nn.Module): def __init__(self, dim, n_head, window_size, pool_size=(4,4), qkv_bias=True, attn_drop=0., proj_drop=0., pooling_token=True): super().__init__() assert dim % n_head == 0 # key, query, value projections for all heads self.key = nn.Linear(dim, dim, qkv_bias) self.query = nn.Linear(dim, dim, qkv_bias) self.value = nn.Linear(dim, dim, qkv_bias) # regularization self.attn_drop = nn.Dropout(attn_drop) self.proj_drop = nn.Dropout(proj_drop) # output projection self.proj = nn.Linear(dim, dim) self.n_head = n_head self.window_size = window_size self.pooling_token = pooling_token if self.pooling_token: ks, stride = pool_size, pool_size self.pool_layer = nn.Conv2d(dim, dim, kernel_size=ks, stride=stride, padding=(0, 0), groups=dim) self.pool_layer.weight.data.fill_(1. / (pool_size[0] * pool_size[1])) self.pool_layer.bias.data.fill_(0) # self.expand_size = tuple(i // 2 for i in window_size) self.expand_size = tuple((i + 1) // 2 for i in window_size) if any(i > 0 for i in self.expand_size): # get mask for rolled k and rolled v mask_tl = torch.ones(self.window_size[0], self.window_size[1]) mask_tl[:-self.expand_size[0], :-self.expand_size[1]] = 0 mask_tr = torch.ones(self.window_size[0], self.window_size[1]) mask_tr[:-self.expand_size[0], self.expand_size[1]:] = 0 mask_bl = torch.ones(self.window_size[0], self.window_size[1]) mask_bl[self.expand_size[0]:, :-self.expand_size[1]] = 0 mask_br = torch.ones(self.window_size[0], self.window_size[1]) mask_br[self.expand_size[0]:, self.expand_size[1]:] = 0 masrool_k = torch.stack((mask_tl, mask_tr, mask_bl, mask_br), 0).flatten(0) self.register_buffer("valid_ind_rolled", masrool_k.nonzero(as_tuple=False).view(-1)) self.max_pool = nn.MaxPool2d(window_size, window_size, (0, 0)) def forward(self, x, mask=None, T_ind=None, attn_mask=None): b, t, h, w, c = x.shape # 20 36 w_h, w_w = self.window_size[0], self.window_size[1] c_head = c // self.n_head n_wh = math.ceil(h / self.window_size[0]) n_ww = math.ceil(w / self.window_size[1]) new_h = n_wh * self.window_size[0] # 20 new_w = n_ww * self.window_size[1] # 36 pad_r = new_w - w pad_b = new_h - h # reverse order if pad_r > 0 or pad_b > 0: x = F.pad(x,(0, 0, 0, pad_r, 0, pad_b, 0, 0), mode='constant', value=0) mask = F.pad(mask,(0, 0, 0, pad_r, 0, pad_b, 0, 0), mode='constant', value=0) # calculate query, key, values for all heads in batch and move head forward to be the batch dim q = self.query(x) k = self.key(x) v = self.value(x) win_q = window_partition(q.contiguous(), self.window_size, self.n_head).view(b, n_wh*n_ww, self.n_head, t, w_h*w_w, c_head) win_k = window_partition(k.contiguous(), self.window_size, self.n_head).view(b, n_wh*n_ww, self.n_head, t, w_h*w_w, c_head) win_v = window_partition(v.contiguous(), self.window_size, self.n_head).view(b, n_wh*n_ww, self.n_head, t, w_h*w_w, c_head) # roll_k and roll_v if any(i > 0 for i in self.expand_size): (k_tl, v_tl) = map(lambda a: torch.roll(a, shifts=(-self.expand_size[0], -self.expand_size[1]), dims=(2, 3)), (k, v)) (k_tr, v_tr) = map(lambda a: torch.roll(a, shifts=(-self.expand_size[0], self.expand_size[1]), dims=(2, 3)), (k, v)) (k_bl, v_bl) = map(lambda a: torch.roll(a, shifts=(self.expand_size[0], -self.expand_size[1]), dims=(2, 3)), (k, v)) (k_br, v_br) = map(lambda a: torch.roll(a, shifts=(self.expand_size[0], self.expand_size[1]), dims=(2, 3)), (k, v)) (k_tl_windows, k_tr_windows, k_bl_windows, k_br_windows) = map( lambda a: window_partition(a, self.window_size, self.n_head).view(b, n_wh*n_ww, self.n_head, t, w_h*w_w, c_head), (k_tl, k_tr, k_bl, k_br)) (v_tl_windows, v_tr_windows, v_bl_windows, v_br_windows) = map( lambda a: window_partition(a, self.window_size, self.n_head).view(b, n_wh*n_ww, self.n_head, t, w_h*w_w, c_head), (v_tl, v_tr, v_bl, v_br)) rool_k = torch.cat((k_tl_windows, k_tr_windows, k_bl_windows, k_br_windows), 4).contiguous() rool_v = torch.cat((v_tl_windows, v_tr_windows, v_bl_windows, v_br_windows), 4).contiguous() # [b, n_wh*n_ww, n_head, t, w_h*w_w, c_head] # mask out tokens in current window rool_k = rool_k[:, :, :, :, self.valid_ind_rolled] rool_v = rool_v[:, :, :, :, self.valid_ind_rolled] roll_N = rool_k.shape[4] rool_k = rool_k.view(b, n_wh*n_ww, self.n_head, t, roll_N, c // self.n_head) rool_v = rool_v.view(b, n_wh*n_ww, self.n_head, t, roll_N, c // self.n_head) win_k = torch.cat((win_k, rool_k), dim=4) win_v = torch.cat((win_v, rool_v), dim=4) else: win_k = win_k win_v = win_v # pool_k and pool_v if self.pooling_token: pool_x = self.pool_layer(x.view(b*t, new_h, new_w, c).permute(0,3,1,2)) _, _, p_h, p_w = pool_x.shape pool_x = pool_x.permute(0,2,3,1).view(b, t, p_h, p_w, c) # pool_k pool_k = self.key(pool_x).unsqueeze(1).repeat(1, n_wh*n_ww, 1, 1, 1, 1) # [b, n_wh*n_ww, t, p_h, p_w, c] pool_k = pool_k.view(b, n_wh*n_ww, t, p_h, p_w, self.n_head, c_head).permute(0,1,5,2,3,4,6) pool_k = pool_k.contiguous().view(b, n_wh*n_ww, self.n_head, t, p_h*p_w, c_head) win_k = torch.cat((win_k, pool_k), dim=4) # pool_v pool_v = self.value(pool_x).unsqueeze(1).repeat(1, n_wh*n_ww, 1, 1, 1, 1) # [b, n_wh*n_ww, t, p_h, p_w, c] pool_v = pool_v.view(b, n_wh*n_ww, t, p_h, p_w, self.n_head, c_head).permute(0,1,5,2,3,4,6) pool_v = pool_v.contiguous().view(b, n_wh*n_ww, self.n_head, t, p_h*p_w, c_head) win_v = torch.cat((win_v, pool_v), dim=4) # [b, n_wh*n_ww, n_head, t, w_h*w_w, c_head] out = torch.zeros_like(win_q) l_t = mask.size(1) mask = self.max_pool(mask.view(b * l_t, new_h, new_w)) mask = mask.view(b, l_t, n_wh*n_ww) mask = torch.sum(mask, dim=1) # [b, n_wh*n_ww] for i in range(win_q.shape[0]): ### For masked windows mask_ind_i = mask[i].nonzero(as_tuple=False).view(-1) # mask out quary in current window # [b, n_wh*n_ww, n_head, t, w_h*w_w, c_head] mask_n = len(mask_ind_i) if mask_n > 0: win_q_t = win_q[i, mask_ind_i].view(mask_n, self.n_head, t*w_h*w_w, c_head) win_k_t = win_k[i, mask_ind_i] win_v_t = win_v[i, mask_ind_i] # mask out key and value if T_ind is not None: # key [n_wh*n_ww, n_head, t, w_h*w_w, c_head] win_k_t = win_k_t[:, :, T_ind.view(-1)].view(mask_n, self.n_head, -1, c_head) # value win_v_t = win_v_t[:, :, T_ind.view(-1)].view(mask_n, self.n_head, -1, c_head) else: win_k_t = win_k_t.view(n_wh*n_ww, self.n_head, t*w_h*w_w, c_head) win_v_t = win_v_t.view(n_wh*n_ww, self.n_head, t*w_h*w_w, c_head) att_t = (win_q_t @ win_k_t.transpose(-2, -1)) * (1.0 / math.sqrt(win_q_t.size(-1))) att_t = F.softmax(att_t, dim=-1) att_t = self.attn_drop(att_t) y_t = att_t @ win_v_t out[i, mask_ind_i] = y_t.view(-1, self.n_head, t, w_h*w_w, c_head) ### For unmasked windows unmask_ind_i = (mask[i] == 0).nonzero(as_tuple=False).view(-1) # mask out quary in current window # [b, n_wh*n_ww, n_head, t, w_h*w_w, c_head] win_q_s = win_q[i, unmask_ind_i] win_k_s = win_k[i, unmask_ind_i, :, :, :w_h*w_w] win_v_s = win_v[i, unmask_ind_i, :, :, :w_h*w_w] att_s = (win_q_s @ win_k_s.transpose(-2, -1)) * (1.0 / math.sqrt(win_q_s.size(-1))) att_s = F.softmax(att_s, dim=-1) att_s = self.attn_drop(att_s) y_s = att_s @ win_v_s out[i, unmask_ind_i] = y_s # re-assemble all head outputs side by side out = out.view(b, n_wh, n_ww, self.n_head, t, w_h, w_w, c_head) out = out.permute(0, 4, 1, 5, 2, 6, 3, 7).contiguous().view(b, t, new_h, new_w, c) if pad_r > 0 or pad_b > 0: out = out[:, :, :h, :w, :] # output projection out = self.proj_drop(self.proj(out)) return out class TemporalSparseTransformer(nn.Module): def __init__(self, dim, n_head, window_size, pool_size, norm_layer=nn.LayerNorm, t2t_params=None): super().__init__() self.window_size = window_size self.attention = SparseWindowAttention(dim, n_head, window_size, pool_size) self.norm1 = norm_layer(dim) self.norm2 = norm_layer(dim) self.mlp = FusionFeedForward(dim, t2t_params=t2t_params) def forward(self, x, fold_x_size, mask=None, T_ind=None): """ Args: x: image tokens, shape [B T H W C] fold_x_size: fold feature size, shape [60 108] mask: mask tokens, shape [B T H W 1] Returns: out_tokens: shape [B T H W C] """ B, T, H, W, C = x.shape # 20 36 shortcut = x x = self.norm1(x) att_x = self.attention(x, mask, T_ind) # FFN x = shortcut + att_x y = self.norm2(x) x = x + self.mlp(y.view(B, T * H * W, C), fold_x_size).view(B, T, H, W, C) return x class TemporalSparseTransformerBlock(nn.Module): def __init__(self, dim, n_head, window_size, pool_size, depths, t2t_params=None): super().__init__() blocks = [] for i in range(depths): blocks.append( TemporalSparseTransformer(dim, n_head, window_size, pool_size, t2t_params=t2t_params) ) self.transformer = nn.Sequential(*blocks) self.depths = depths def forward(self, x, fold_x_size, l_mask=None, t_dilation=2): """ Args: x: image tokens, shape [B T H W C] fold_x_size: fold feature size, shape [60 108] l_mask: local mask tokens, shape [B T H W 1] Returns: out_tokens: shape [B T H W C] """ assert self.depths % t_dilation == 0, 'wrong t_dilation input.' T = x.size(1) T_ind = [torch.arange(i, T, t_dilation) for i in range(t_dilation)] * (self.depths // t_dilation) for i in range(0, self.depths): x = self.transformer[i](x, fold_x_size, l_mask, T_ind[i]) return x ================================================ FILE: propainter/model/modules/spectral_norm.py ================================================ """ Spectral Normalization from https://arxiv.org/abs/1802.05957 """ import torch from torch.nn.functional import normalize class SpectralNorm(object): # Invariant before and after each forward call: # u = normalize(W @ v) # NB: At initialization, this invariant is not enforced _version = 1 # At version 1: # made `W` not a buffer, # added `v` as a buffer, and # made eval mode use `W = u @ W_orig @ v` rather than the stored `W`. def __init__(self, name='weight', n_power_iterations=1, dim=0, eps=1e-12): self.name = name self.dim = dim if n_power_iterations <= 0: raise ValueError( 'Expected n_power_iterations to be positive, but ' 'got n_power_iterations={}'.format(n_power_iterations)) self.n_power_iterations = n_power_iterations self.eps = eps def reshape_weight_to_matrix(self, weight): weight_mat = weight if self.dim != 0: # permute dim to front weight_mat = weight_mat.permute( self.dim, *[d for d in range(weight_mat.dim()) if d != self.dim]) height = weight_mat.size(0) return weight_mat.reshape(height, -1) def compute_weight(self, module, do_power_iteration): # NB: If `do_power_iteration` is set, the `u` and `v` vectors are # updated in power iteration **in-place**. This is very important # because in `DataParallel` forward, the vectors (being buffers) are # broadcast from the parallelized module to each module replica, # which is a new module object created on the fly. And each replica # runs its own spectral norm power iteration. So simply assigning # the updated vectors to the module this function runs on will cause # the update to be lost forever. And the next time the parallelized # module is replicated, the same randomly initialized vectors are # broadcast and used! # # Therefore, to make the change propagate back, we rely on two # important behaviors (also enforced via tests): # 1. `DataParallel` doesn't clone storage if the broadcast tensor # is already on correct device; and it makes sure that the # parallelized module is already on `device[0]`. # 2. If the out tensor in `out=` kwarg has correct shape, it will # just fill in the values. # Therefore, since the same power iteration is performed on all # devices, simply updating the tensors in-place will make sure that # the module replica on `device[0]` will update the _u vector on the # parallized module (by shared storage). # # However, after we update `u` and `v` in-place, we need to **clone** # them before using them to normalize the weight. This is to support # backproping through two forward passes, e.g., the common pattern in # GAN training: loss = D(real) - D(fake). Otherwise, engine will # complain that variables needed to do backward for the first forward # (i.e., the `u` and `v` vectors) are changed in the second forward. weight = getattr(module, self.name + '_orig') u = getattr(module, self.name + '_u') v = getattr(module, self.name + '_v') weight_mat = self.reshape_weight_to_matrix(weight) if do_power_iteration: with torch.no_grad(): for _ in range(self.n_power_iterations): # Spectral norm of weight equals to `u^T W v`, where `u` and `v` # are the first left and right singular vectors. # This power iteration produces approximations of `u` and `v`. v = normalize(torch.mv(weight_mat.t(), u), dim=0, eps=self.eps, out=v) u = normalize(torch.mv(weight_mat, v), dim=0, eps=self.eps, out=u) if self.n_power_iterations > 0: # See above on why we need to clone u = u.clone() v = v.clone() sigma = torch.dot(u, torch.mv(weight_mat, v)) weight = weight / sigma return weight def remove(self, module): with torch.no_grad(): weight = self.compute_weight(module, do_power_iteration=False) delattr(module, self.name) delattr(module, self.name + '_u') delattr(module, self.name + '_v') delattr(module, self.name + '_orig') module.register_parameter(self.name, torch.nn.Parameter(weight.detach())) def __call__(self, module, inputs): setattr( module, self.name, self.compute_weight(module, do_power_iteration=module.training)) def _solve_v_and_rescale(self, weight_mat, u, target_sigma): # Tries to returns a vector `v` s.t. `u = normalize(W @ v)` # (the invariant at top of this class) and `u @ W @ v = sigma`. # This uses pinverse in case W^T W is not invertible. v = torch.chain_matmul(weight_mat.t().mm(weight_mat).pinverse(), weight_mat.t(), u.unsqueeze(1)).squeeze(1) return v.mul_(target_sigma / torch.dot(u, torch.mv(weight_mat, v))) @staticmethod def apply(module, name, n_power_iterations, dim, eps): for k, hook in module._forward_pre_hooks.items(): if isinstance(hook, SpectralNorm) and hook.name == name: raise RuntimeError( "Cannot register two spectral_norm hooks on " "the same parameter {}".format(name)) fn = SpectralNorm(name, n_power_iterations, dim, eps) weight = module._parameters[name] with torch.no_grad(): weight_mat = fn.reshape_weight_to_matrix(weight) h, w = weight_mat.size() # randomly initialize `u` and `v` u = normalize(weight.new_empty(h).normal_(0, 1), dim=0, eps=fn.eps) v = normalize(weight.new_empty(w).normal_(0, 1), dim=0, eps=fn.eps) delattr(module, fn.name) module.register_parameter(fn.name + "_orig", weight) # We still need to assign weight back as fn.name because all sorts of # things may assume that it exists, e.g., when initializing weights. # However, we can't directly assign as it could be an nn.Parameter and # gets added as a parameter. Instead, we register weight.data as a plain # attribute. setattr(module, fn.name, weight.data) module.register_buffer(fn.name + "_u", u) module.register_buffer(fn.name + "_v", v) module.register_forward_pre_hook(fn) module._register_state_dict_hook(SpectralNormStateDictHook(fn)) module._register_load_state_dict_pre_hook( SpectralNormLoadStateDictPreHook(fn)) return fn # This is a top level class because Py2 pickle doesn't like inner class nor an # instancemethod. class SpectralNormLoadStateDictPreHook(object): # See docstring of SpectralNorm._version on the changes to spectral_norm. def __init__(self, fn): self.fn = fn # For state_dict with version None, (assuming that it has gone through at # least one training forward), we have # # u = normalize(W_orig @ v) # W = W_orig / sigma, where sigma = u @ W_orig @ v # # To compute `v`, we solve `W_orig @ x = u`, and let # v = x / (u @ W_orig @ x) * (W / W_orig). def __call__(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs): fn = self.fn version = local_metadata.get('spectral_norm', {}).get(fn.name + '.version', None) if version is None or version < 1: with torch.no_grad(): weight_orig = state_dict[prefix + fn.name + '_orig'] # weight = state_dict.pop(prefix + fn.name) # sigma = (weight_orig / weight).mean() weight_mat = fn.reshape_weight_to_matrix(weight_orig) u = state_dict[prefix + fn.name + '_u'] # v = fn._solve_v_and_rescale(weight_mat, u, sigma) # state_dict[prefix + fn.name + '_v'] = v # This is a top level class because Py2 pickle doesn't like inner class nor an # instancemethod. class SpectralNormStateDictHook(object): # See docstring of SpectralNorm._version on the changes to spectral_norm. def __init__(self, fn): self.fn = fn def __call__(self, module, state_dict, prefix, local_metadata): if 'spectral_norm' not in local_metadata: local_metadata['spectral_norm'] = {} key = self.fn.name + '.version' if key in local_metadata['spectral_norm']: raise RuntimeError( "Unexpected key in metadata['spectral_norm']: {}".format(key)) local_metadata['spectral_norm'][key] = self.fn._version def spectral_norm(module, name='weight', n_power_iterations=1, eps=1e-12, dim=None): r"""Applies spectral normalization to a parameter in the given module. .. math:: \mathbf{W}_{SN} = \dfrac{\mathbf{W}}{\sigma(\mathbf{W})}, \sigma(\mathbf{W}) = \max_{\mathbf{h}: \mathbf{h} \ne 0} \dfrac{\|\mathbf{W} \mathbf{h}\|_2}{\|\mathbf{h}\|_2} Spectral normalization stabilizes the training of discriminators (critics) in Generative Adversarial Networks (GANs) by rescaling the weight tensor with spectral norm :math:`\sigma` of the weight matrix calculated using power iteration method. If the dimension of the weight tensor is greater than 2, it is reshaped to 2D in power iteration method to get spectral norm. This is implemented via a hook that calculates spectral norm and rescales weight before every :meth:`~Module.forward` call. See `Spectral Normalization for Generative Adversarial Networks`_ . .. _`Spectral Normalization for Generative Adversarial Networks`: https://arxiv.org/abs/1802.05957 Args: module (nn.Module): containing module name (str, optional): name of weight parameter n_power_iterations (int, optional): number of power iterations to calculate spectral norm eps (float, optional): epsilon for numerical stability in calculating norms dim (int, optional): dimension corresponding to number of outputs, the default is ``0``, except for modules that are instances of ConvTranspose{1,2,3}d, when it is ``1`` Returns: The original module with the spectral norm hook Example:: >>> m = spectral_norm(nn.Linear(20, 40)) >>> m Linear(in_features=20, out_features=40, bias=True) >>> m.weight_u.size() torch.Size([40]) """ if dim is None: if isinstance(module, (torch.nn.ConvTranspose1d, torch.nn.ConvTranspose2d, torch.nn.ConvTranspose3d)): dim = 1 else: dim = 0 SpectralNorm.apply(module, name, n_power_iterations, dim, eps) return module def remove_spectral_norm(module, name='weight'): r"""Removes the spectral normalization reparameterization from a module. Args: module (Module): containing module name (str, optional): name of weight parameter Example: >>> m = spectral_norm(nn.Linear(40, 10)) >>> remove_spectral_norm(m) """ for k, hook in module._forward_pre_hooks.items(): if isinstance(hook, SpectralNorm) and hook.name == name: hook.remove(module) del module._forward_pre_hooks[k] return module raise ValueError("spectral_norm of '{}' not found in {}".format( name, module)) def use_spectral_norm(module, use_sn=False): if use_sn: return spectral_norm(module) return module ================================================ FILE: propainter/model/propainter.py ================================================ ''' Towards An End-to-End Framework for Video Inpainting ''' import torch import torch.nn as nn import torch.nn.functional as F import torchvision from einops import rearrange from .modules.base_module import BaseNetwork from .modules.sparse_transformer import TemporalSparseTransformerBlock, SoftSplit, SoftComp from .modules.spectral_norm import spectral_norm as _spectral_norm from .modules.flow_loss_utils import flow_warp from .modules.deformconv import ModulatedDeformConv2d from .misc import constant_init # except: # from propainter.model.modules.base_module import BaseNetwork # from propainter.model.modules.sparse_transformer import TemporalSparseTransformerBlock, SoftSplit, SoftComp # from propainter.model.modules.spectral_norm import spectral_norm as _spectral_norm # from propainter.model.modules.flow_loss_utils import flow_warp # from propainter.model.modules.deformconv import ModulatedDeformConv2d # from propainter.model.misc import constant_init def length_sq(x): return torch.sum(torch.square(x), dim=1, keepdim=True) def fbConsistencyCheck(flow_fw, flow_bw, alpha1=0.01, alpha2=0.5): #debug flow_bw_warped = flow_warp(flow_bw, flow_fw.permute(0, 2, 3, 1)) # wb(wf(x)) flow_diff_fw = flow_fw + flow_bw_warped # wf + wb(wf(x)) mag_sq_fw = length_sq(flow_fw) + length_sq(flow_bw_warped) # |wf| + |wb(wf(x))| occ_thresh_fw = alpha1 * mag_sq_fw + alpha2 # fb_valid_fw = (length_sq(flow_diff_fw) < occ_thresh_fw).float() fb_valid_fw = (length_sq(flow_diff_fw) < occ_thresh_fw).to(flow_fw) return fb_valid_fw class DeformableAlignment(ModulatedDeformConv2d): """Second-order deformable alignment module.""" def __init__(self, *args, **kwargs): # self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 10) self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 3) super(DeformableAlignment, self).__init__(*args, **kwargs) self.conv_offset = nn.Sequential( nn.Conv2d(2*self.out_channels + 2 + 1 + 2, self.out_channels, 3, 1, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(self.out_channels, 27 * self.deform_groups, 3, 1, 1), ) self.init_offset() def init_offset(self): constant_init(self.conv_offset[-1], val=0, bias=0) def forward(self, x, cond_feat, flow): out = self.conv_offset(cond_feat) o1, o2, mask = torch.chunk(out, 3, dim=1) # offset offset = self.max_residue_magnitude * torch.tanh(torch.cat((o1, o2), dim=1)) offset = offset + flow.flip(1).repeat(1, offset.size(1) // 2, 1, 1) # mask mask = torch.sigmoid(mask) return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, self.dilation, mask) class BidirectionalPropagation(nn.Module): def __init__(self, channel, learnable=True): super(BidirectionalPropagation, self).__init__() self.deform_align = nn.ModuleDict() self.backbone = nn.ModuleDict() self.channel = channel self.prop_list = ['backward_1', 'forward_1'] self.learnable = learnable if self.learnable: for i, module in enumerate(self.prop_list): self.deform_align[module] = DeformableAlignment( channel, channel, 3, padding=1, deform_groups=16) self.backbone[module] = nn.Sequential( nn.Conv2d(2*channel+2, channel, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(channel, channel, 3, 1, 1), ) self.fuse = nn.Sequential( nn.Conv2d(2*channel+2, channel, 3, 1, 1), nn.LeakyReLU(negative_slope=0.2, inplace=True), nn.Conv2d(channel, channel, 3, 1, 1), ) def binary_mask(self, mask, th=0.1): mask[mask>th] = 1 mask[mask<=th] = 0 # return mask.float() return mask.to(mask) def forward(self, x, flows_forward, flows_backward, mask, interpolation='bilinear', direction='forward'): """ x shape : [b, t, c, h, w] return [b, t, c, h, w] """ # For backward warping # pred_flows_forward for backward feature propagation # pred_flows_backward for forward feature propagation b, t, c, h, w = x.shape feats, masks = {}, {} feats['input'] = [x[:, i, :, :, :] for i in range(0, t)] masks['input'] = [mask[:, i, :, :, :] for i in range(0, t)] prop_list = ['backward_1', 'forward_1'] cache_list = ['input'] + prop_list for p_i, module_name in enumerate(prop_list): feats[module_name] = [] masks[module_name] = [] if 'backward' in module_name: frame_idx = range(0, t) frame_idx = frame_idx[::-1] flow_idx = frame_idx flows_for_prop = flows_forward flows_for_check = flows_backward else: frame_idx = range(0, t) flow_idx = range(-1, t - 1) flows_for_prop = flows_backward flows_for_check = flows_forward len_frames_idx = len(frame_idx) for i, idx in enumerate(frame_idx): feat_current = feats[cache_list[p_i]][idx] mask_current = masks[cache_list[p_i]][idx] if i == 0: feat_prop = feat_current mask_prop = mask_current else: flow_prop = flows_for_prop[:, flow_idx[i], :, :, :] flow_check = flows_for_check[:, flow_idx[i], :, :, :] flow_vaild_mask = fbConsistencyCheck(flow_prop, flow_check) feat_warped = flow_warp(feat_prop, flow_prop.permute(0, 2, 3, 1), interpolation) feat_warped = torch.clamp(feat_warped, min=-1.0, max=1.0) if self.learnable: cond = torch.cat([feat_current, feat_warped, flow_prop, flow_vaild_mask, mask_current], dim=1) feat_prop = self.deform_align[module_name](feat_prop, cond, flow_prop) mask_prop = mask_current else: mask_prop_valid = flow_warp(mask_prop, flow_prop.permute(0, 2, 3, 1)) mask_prop_valid = self.binary_mask(mask_prop_valid) union_vaild_mask = self.binary_mask(mask_current*flow_vaild_mask*(1-mask_prop_valid)) feat_prop = union_vaild_mask * feat_warped + (1-union_vaild_mask) * feat_current # update mask mask_prop = self.binary_mask(mask_current*(1-(flow_vaild_mask*(1-mask_prop_valid)))) # refine if self.learnable: feat = torch.cat([feat_current, feat_prop, mask_current], dim=1) feat_prop = feat_prop + self.backbone[module_name](feat) # feat_prop = self.backbone[module_name](feat_prop) feats[module_name].append(feat_prop) masks[module_name].append(mask_prop) # end for if 'backward' in module_name: feats[module_name] = feats[module_name][::-1] masks[module_name] = masks[module_name][::-1] outputs_b = torch.stack(feats['backward_1'], dim=1).view(-1, c, h, w) outputs_f = torch.stack(feats['forward_1'], dim=1).view(-1, c, h, w) if self.learnable: mask_in = mask.view(-1, 2, h, w) masks_b, masks_f = None, None outputs = self.fuse(torch.cat([outputs_b, outputs_f, mask_in], dim=1)) + x.view(-1, c, h, w) else: if direction == 'forward': masks_b = torch.stack(masks['backward_1'], dim=1) masks_f = torch.stack(masks['forward_1'], dim=1) outputs = outputs_f else: masks_b = torch.stack(masks['backward_1'], dim=1) masks_f = torch.stack(masks['forward_1'], dim=1) outputs = outputs_b return outputs_b.view(b, -1, c, h, w), outputs_f.view(b, -1, c, h, w), \ outputs.view(b, -1, c, h, w), masks_b return outputs_b.view(b, -1, c, h, w), outputs_f.view(b, -1, c, h, w), \ outputs.view(b, -1, c, h, w), masks_f class Encoder(nn.Module): def __init__(self): super(Encoder, self).__init__() self.group = [1, 2, 4, 8, 1] self.layers = nn.ModuleList([ nn.Conv2d(5, 64, kernel_size=3, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(256, 384, kernel_size=3, stride=1, padding=1, groups=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(640, 512, kernel_size=3, stride=1, padding=1, groups=2), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(768, 384, kernel_size=3, stride=1, padding=1, groups=4), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(640, 256, kernel_size=3, stride=1, padding=1, groups=8), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(512, 128, kernel_size=3, stride=1, padding=1, groups=1), nn.LeakyReLU(0.2, inplace=True) ]) def forward(self, x): bt, c, _, _ = x.size() # h, w = h//4, w//4 out = x for i, layer in enumerate(self.layers): if i == 8: x0 = out _, _, h, w = x0.size() if i > 8 and i % 2 == 0: g = self.group[(i - 8) // 2] x = x0.view(bt, g, -1, h, w) o = out.view(bt, g, -1, h, w) out = torch.cat([x, o], 2).view(bt, -1, h, w) out = layer(out) return out class deconv(nn.Module): def __init__(self, input_channel, output_channel, kernel_size=3, padding=0): super().__init__() self.conv = nn.Conv2d(input_channel, output_channel, kernel_size=kernel_size, stride=1, padding=padding) def forward(self, x): x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) return self.conv(x) class InpaintGenerator(BaseNetwork): def __init__(self, init_weights=True, model_path=None): super(InpaintGenerator, self).__init__() channel = 128 hidden = 512 # encoder self.encoder = Encoder() # decoder self.decoder = nn.Sequential( deconv(channel, 128, kernel_size=3, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(128, 64, kernel_size=3, stride=1, padding=1), nn.LeakyReLU(0.2, inplace=True), deconv(64, 64, kernel_size=3, padding=1), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(64, 3, kernel_size=3, stride=1, padding=1)) # soft split and soft composition kernel_size = (7, 7) padding = (3, 3) stride = (3, 3) t2t_params = { 'kernel_size': kernel_size, 'stride': stride, 'padding': padding } self.ss = SoftSplit(channel, hidden, kernel_size, stride, padding) self.sc = SoftComp(channel, hidden, kernel_size, stride, padding) self.max_pool = nn.MaxPool2d(kernel_size, stride, padding) # feature propagation module self.img_prop_module = BidirectionalPropagation(3, learnable=False) self.feat_prop_module = BidirectionalPropagation(128, learnable=True) depths = 8 num_heads = 4 window_size = (5, 9) pool_size = (4, 4) self.transformers = TemporalSparseTransformerBlock(dim=hidden, n_head=num_heads, window_size=window_size, pool_size=pool_size, depths=depths, t2t_params=t2t_params) if init_weights: self.init_weights() if model_path is not None: # print('Pretrained ProPainter has loaded...') ckpt = torch.load(model_path, map_location='cpu') self.load_state_dict(ckpt, strict=True) # print network parameter number # self.print_network() def img_propagation(self, masked_frames, completed_flows, masks, interpolation='nearest', direction = 'forward'): _, _, prop_frames, updated_masks = self.img_prop_module(masked_frames, completed_flows[0], completed_flows[1], masks, interpolation, direction) return prop_frames, updated_masks def forward(self, masked_frames, completed_flows, masks_in, masks_updated, num_local_frames, interpolation='bilinear', t_dilation=2): """ Args: masks_in: original mask masks_updated: updated mask after image propagation """ l_t = num_local_frames b, t, _, ori_h, ori_w = masked_frames.size() # extracting features enc_feat = self.encoder(torch.cat([masked_frames.view(b * t, 3, ori_h, ori_w), masks_in.view(b * t, 1, ori_h, ori_w), masks_updated.view(b * t, 1, ori_h, ori_w)], dim=1)) _, c, h, w = enc_feat.size() local_feat = enc_feat.view(b, t, c, h, w)[:, :l_t, ...] ref_feat = enc_feat.view(b, t, c, h, w)[:, l_t:, ...] fold_feat_size = (h, w) ds_flows_f = F.interpolate(completed_flows[0].view(-1, 2, ori_h, ori_w), scale_factor=1/4, mode='bilinear', align_corners=False).view(b, l_t-1, 2, h, w)/4.0 ds_flows_b = F.interpolate(completed_flows[1].view(-1, 2, ori_h, ori_w), scale_factor=1/4, mode='bilinear', align_corners=False).view(b, l_t-1, 2, h, w)/4.0 ds_mask_in = F.interpolate(masks_in.reshape(-1, 1, ori_h, ori_w), scale_factor=1/4, mode='nearest').view(b, t, 1, h, w) ds_mask_in_local = ds_mask_in[:, :l_t] ds_mask_updated_local = F.interpolate(masks_updated[:,:l_t].reshape(-1, 1, ori_h, ori_w), scale_factor=1/4, mode='nearest').view(b, l_t, 1, h, w) if self.training: mask_pool_l = self.max_pool(ds_mask_in.view(-1, 1, h, w)) mask_pool_l = mask_pool_l.view(b, t, 1, mask_pool_l.size(-2), mask_pool_l.size(-1)) else: mask_pool_l = self.max_pool(ds_mask_in_local.view(-1, 1, h, w)) mask_pool_l = mask_pool_l.view(b, l_t, 1, mask_pool_l.size(-2), mask_pool_l.size(-1)) prop_mask_in = torch.cat([ds_mask_in_local, ds_mask_updated_local], dim=2) _, _, local_feat, _ = self.feat_prop_module(local_feat, ds_flows_f, ds_flows_b, prop_mask_in, interpolation) enc_feat = torch.cat((local_feat, ref_feat), dim=1) trans_feat = self.ss(enc_feat.view(-1, c, h, w), b, fold_feat_size) mask_pool_l = rearrange(mask_pool_l, 'b t c h w -> b t h w c').contiguous() trans_feat = self.transformers(trans_feat, fold_feat_size, mask_pool_l, t_dilation=t_dilation) trans_feat = self.sc(trans_feat, t, fold_feat_size) trans_feat = trans_feat.view(b, t, -1, h, w) enc_feat = enc_feat + trans_feat if self.training: output = self.decoder(enc_feat.view(-1, c, h, w)) output = torch.tanh(output).view(b, t, 3, ori_h, ori_w) else: output = self.decoder(enc_feat[:, :l_t].view(-1, c, h, w)) output = torch.tanh(output).view(b, l_t, 3, ori_h, ori_w) return output # ###################################################################### # Discriminator for Temporal Patch GAN # ###################################################################### class Discriminator(BaseNetwork): def __init__(self, in_channels=3, use_sigmoid=False, use_spectral_norm=True, init_weights=True): super(Discriminator, self).__init__() self.use_sigmoid = use_sigmoid nf = 32 self.conv = nn.Sequential( spectral_norm( nn.Conv3d(in_channels=in_channels, out_channels=nf * 1, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=1, bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(64, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm( nn.Conv3d(nf * 1, nf * 2, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(128, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm( nn.Conv3d(nf * 2, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm( nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm( nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), nn.Conv3d(nf * 4, nf * 4, kernel_size=(3, 5, 5), stride=(1, 2, 2), padding=(1, 2, 2))) if init_weights: self.init_weights() def forward(self, xs): # T, C, H, W = xs.shape (old) # B, T, C, H, W (new) xs_t = torch.transpose(xs, 1, 2) feat = self.conv(xs_t) if self.use_sigmoid: feat = torch.sigmoid(feat) out = torch.transpose(feat, 1, 2) # B, T, C, H, W return out class Discriminator_2D(BaseNetwork): def __init__(self, in_channels=3, use_sigmoid=False, use_spectral_norm=True, init_weights=True): super(Discriminator_2D, self).__init__() self.use_sigmoid = use_sigmoid nf = 32 self.conv = nn.Sequential( spectral_norm( nn.Conv3d(in_channels=in_channels, out_channels=nf * 1, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(64, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm( nn.Conv3d(nf * 1, nf * 2, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(128, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm( nn.Conv3d(nf * 2, nf * 4, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm( nn.Conv3d(nf * 4, nf * 4, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), spectral_norm( nn.Conv3d(nf * 4, nf * 4, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 2, 2), bias=not use_spectral_norm), use_spectral_norm), # nn.InstanceNorm2d(256, track_running_stats=False), nn.LeakyReLU(0.2, inplace=True), nn.Conv3d(nf * 4, nf * 4, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 2, 2))) if init_weights: self.init_weights() def forward(self, xs): # T, C, H, W = xs.shape (old) # B, T, C, H, W (new) xs_t = torch.transpose(xs, 1, 2) feat = self.conv(xs_t) if self.use_sigmoid: feat = torch.sigmoid(feat) out = torch.transpose(feat, 1, 2) # B, T, C, H, W return out def spectral_norm(module, mode=True): if mode: return _spectral_norm(module) return module ================================================ FILE: propainter/model/recurrent_flow_completion.py ================================================ import torch import torch.nn as nn import torch.nn.functional as F import torchvision from .modules.deformconv import ModulatedDeformConv2d from .misc import constant_init # except: # from propainter.model.modules.deformconv import ModulatedDeformConv2d # from propainter.model.misc import constant_init class SecondOrderDeformableAlignment(ModulatedDeformConv2d): """Second-order deformable alignment module.""" def __init__(self, *args, **kwargs): self.max_residue_magnitude = kwargs.pop('max_residue_magnitude', 5) super(SecondOrderDeformableAlignment, self).__init__(*args, **kwargs) self.conv_offset = nn.Sequential( nn.Conv2d(3 * self.out_channels, self.out_channels, 3, 1, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(self.out_channels, self.out_channels, 3, 1, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(self.out_channels, 27 * self.deform_groups, 3, 1, 1), ) self.init_offset() def init_offset(self): constant_init(self.conv_offset[-1], val=0, bias=0) def forward(self, x, extra_feat): out = self.conv_offset(extra_feat) o1, o2, mask = torch.chunk(out, 3, dim=1) # offset offset = self.max_residue_magnitude * torch.tanh(torch.cat((o1, o2), dim=1)) offset_1, offset_2 = torch.chunk(offset, 2, dim=1) offset = torch.cat([offset_1, offset_2], dim=1) # mask mask = torch.sigmoid(mask) return torchvision.ops.deform_conv2d(x, offset, self.weight, self.bias, self.stride, self.padding, self.dilation, mask) class BidirectionalPropagation(nn.Module): def __init__(self, channel): super(BidirectionalPropagation, self).__init__() modules = ['backward_', 'forward_'] self.deform_align = nn.ModuleDict() self.backbone = nn.ModuleDict() self.channel = channel for i, module in enumerate(modules): self.deform_align[module] = SecondOrderDeformableAlignment( 2 * channel, channel, 3, padding=1, deform_groups=16) self.backbone[module] = nn.Sequential( nn.Conv2d((2 + i) * channel, channel, 3, 1, 1), nn.LeakyReLU(negative_slope=0.1, inplace=True), nn.Conv2d(channel, channel, 3, 1, 1), ) self.fusion = nn.Conv2d(2 * channel, channel, 1, 1, 0) def forward(self, x): """ x shape : [b, t, c, h, w] return [b, t, c, h, w] """ b, t, c, h, w = x.shape feats = {} feats['spatial'] = [x[:, i, :, :, :] for i in range(0, t)] for module_name in ['backward_', 'forward_']: feats[module_name] = [] frame_idx = range(0, t) mapping_idx = list(range(0, len(feats['spatial']))) mapping_idx += mapping_idx[::-1] if 'backward' in module_name: frame_idx = frame_idx[::-1] feat_prop = x.new_zeros(b, self.channel, h, w) for i, idx in enumerate(frame_idx): feat_current = feats['spatial'][mapping_idx[idx]] if i > 0: cond_n1 = feat_prop # initialize second-order features feat_n2 = torch.zeros_like(feat_prop) cond_n2 = torch.zeros_like(cond_n1) if i > 1: # second-order features feat_n2 = feats[module_name][-2] cond_n2 = feat_n2 cond = torch.cat([cond_n1, feat_current, cond_n2], dim=1) # condition information, cond(flow warped 1st/2nd feature) feat_prop = torch.cat([feat_prop, feat_n2], dim=1) # two order feat_prop -1 & -2 feat_prop = self.deform_align[module_name](feat_prop, cond) # fuse current features feat = [feat_current] + \ [feats[k][idx] for k in feats if k not in ['spatial', module_name]] \ + [feat_prop] feat = torch.cat(feat, dim=1) # embed current features feat_prop = feat_prop + self.backbone[module_name](feat) feats[module_name].append(feat_prop) # end for if 'backward' in module_name: feats[module_name] = feats[module_name][::-1] outputs = [] for i in range(0, t): align_feats = [feats[k].pop(0) for k in feats if k != 'spatial'] align_feats = torch.cat(align_feats, dim=1) outputs.append(self.fusion(align_feats)) return torch.stack(outputs, dim=1) + x class deconv(nn.Module): def __init__(self, input_channel, output_channel, kernel_size=3, padding=0): super().__init__() self.conv = nn.Conv2d(input_channel, output_channel, kernel_size=kernel_size, stride=1, padding=padding) def forward(self, x): x = F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=True) return self.conv(x) class P3DBlock(nn.Module): def __init__(self, in_channels, out_channels, kernel_size, stride, padding, use_residual=0, bias=True): super().__init__() self.conv1 = nn.Sequential( nn.Conv3d(in_channels, out_channels, kernel_size=(1, kernel_size, kernel_size), stride=(1, stride, stride), padding=(0, padding, padding), bias=bias), nn.LeakyReLU(0.2, inplace=True) ) self.conv2 = nn.Sequential( nn.Conv3d(out_channels, out_channels, kernel_size=(3, 1, 1), stride=(1, 1, 1), padding=(2, 0, 0), dilation=(2, 1, 1), bias=bias) ) self.use_residual = use_residual def forward(self, feats): feat1 = self.conv1(feats) feat2 = self.conv2(feat1) if self.use_residual: output = feats + feat2 else: output = feat2 return output class EdgeDetection(nn.Module): def __init__(self, in_ch=2, out_ch=1, mid_ch=16): super().__init__() self.projection = nn.Sequential( nn.Conv2d(in_ch, mid_ch, 3, 1, 1), nn.LeakyReLU(0.2, inplace=True) ) self.mid_layer_1 = nn.Sequential( nn.Conv2d(mid_ch, mid_ch, 3, 1, 1), nn.LeakyReLU(0.2, inplace=True) ) self.mid_layer_2 = nn.Sequential( nn.Conv2d(mid_ch, mid_ch, 3, 1, 1) ) self.l_relu = nn.LeakyReLU(0.01, inplace=True) self.out_layer = nn.Conv2d(mid_ch, out_ch, 1, 1, 0) def forward(self, flow): flow = self.projection(flow) edge = self.mid_layer_1(flow) edge = self.mid_layer_2(edge) edge = self.l_relu(flow + edge) edge = self.out_layer(edge) edge = torch.sigmoid(edge) return edge class RecurrentFlowCompleteNet(nn.Module): def __init__(self, model_path=None): super().__init__() self.downsample = nn.Sequential( nn.Conv3d(3, 32, kernel_size=(1, 5, 5), stride=(1, 2, 2), padding=(0, 2, 2), padding_mode='replicate'), nn.LeakyReLU(0.2, inplace=True) ) self.encoder1 = nn.Sequential( P3DBlock(32, 32, 3, 1, 1), nn.LeakyReLU(0.2, inplace=True), P3DBlock(32, 64, 3, 2, 1), nn.LeakyReLU(0.2, inplace=True) ) # 4x self.encoder2 = nn.Sequential( P3DBlock(64, 64, 3, 1, 1), nn.LeakyReLU(0.2, inplace=True), P3DBlock(64, 128, 3, 2, 1), nn.LeakyReLU(0.2, inplace=True) ) # 8x self.mid_dilation = nn.Sequential( nn.Conv3d(128, 128, (1, 3, 3), (1, 1, 1), padding=(0, 3, 3), dilation=(1, 3, 3)), # p = d*(k-1)/2 nn.LeakyReLU(0.2, inplace=True), nn.Conv3d(128, 128, (1, 3, 3), (1, 1, 1), padding=(0, 2, 2), dilation=(1, 2, 2)), nn.LeakyReLU(0.2, inplace=True), nn.Conv3d(128, 128, (1, 3, 3), (1, 1, 1), padding=(0, 1, 1), dilation=(1, 1, 1)), nn.LeakyReLU(0.2, inplace=True) ) # feature propagation module self.feat_prop_module = BidirectionalPropagation(128) self.decoder2 = nn.Sequential( nn.Conv2d(128, 128, 3, 1, 1), nn.LeakyReLU(0.2, inplace=True), deconv(128, 64, 3, 1), nn.LeakyReLU(0.2, inplace=True) ) # 4x self.decoder1 = nn.Sequential( nn.Conv2d(64, 64, 3, 1, 1), nn.LeakyReLU(0.2, inplace=True), deconv(64, 32, 3, 1), nn.LeakyReLU(0.2, inplace=True) ) # 2x self.upsample = nn.Sequential( nn.Conv2d(32, 32, 3, padding=1), nn.LeakyReLU(0.2, inplace=True), deconv(32, 2, 3, 1) ) # edge loss self.edgeDetector = EdgeDetection(in_ch=2, out_ch=1, mid_ch=16) # Need to initial the weights of MSDeformAttn specifically for m in self.modules(): if isinstance(m, SecondOrderDeformableAlignment): m.init_offset() if model_path is not None: # print('Pretrained flow completion model has loaded...') ckpt = torch.load(model_path, map_location='cpu') self.load_state_dict(ckpt, strict=True) def forward(self, masked_flows, masks): # masked_flows: b t-1 2 h w # masks: b t-1 2 h w b, t, _, h, w = masked_flows.size() masked_flows = masked_flows.permute(0,2,1,3,4) masks = masks.permute(0,2,1,3,4) inputs = torch.cat((masked_flows, masks), dim=1) x = self.downsample(inputs) feat_e1 = self.encoder1(x) feat_e2 = self.encoder2(feat_e1) # b c t h w feat_mid = self.mid_dilation(feat_e2) # b c t h w feat_mid = feat_mid.permute(0,2,1,3,4) # b t c h w feat_prop = self.feat_prop_module(feat_mid) feat_prop = feat_prop.view(-1, 128, h//8, w//8) # b*t c h w _, c, _, h_f, w_f = feat_e1.shape feat_e1 = feat_e1.permute(0,2,1,3,4).contiguous().view(-1, c, h_f, w_f) # b*t c h w feat_d2 = self.decoder2(feat_prop) + feat_e1 _, c, _, h_f, w_f = x.shape x = x.permute(0,2,1,3,4).contiguous().view(-1, c, h_f, w_f) # b*t c h w feat_d1 = self.decoder1(feat_d2) flow = self.upsample(feat_d1) if self.training: edge = self.edgeDetector(flow) edge = edge.view(b, t, 1, h, w) else: edge = None flow = flow.view(b, t, 2, h, w) return flow, edge def forward_bidirect_flow(self, masked_flows_bi, masks): """ Args: masked_flows_bi: [masked_flows_f, masked_flows_b] | (b t-1 2 h w), (b t-1 2 h w) masks: b t 1 h w """ masks_forward = masks[:, :-1, ...].contiguous() masks_backward = masks[:, 1:, ...].contiguous() # mask flow masked_flows_forward = masked_flows_bi[0] * (1-masks_forward) masked_flows_backward = masked_flows_bi[1] * (1-masks_backward) # -- completion -- # forward pred_flows_forward, pred_edges_forward = self.forward(masked_flows_forward, masks_forward) # backward masked_flows_backward = torch.flip(masked_flows_backward, dims=[1]) masks_backward = torch.flip(masks_backward, dims=[1]) pred_flows_backward, pred_edges_backward = self.forward(masked_flows_backward, masks_backward) pred_flows_backward = torch.flip(pred_flows_backward, dims=[1]) if self.training: pred_edges_backward = torch.flip(pred_edges_backward, dims=[1]) return [pred_flows_forward, pred_flows_backward], [pred_edges_forward, pred_edges_backward] def combine_flow(self, masked_flows_bi, pred_flows_bi, masks): masks_forward = masks[:, :-1, ...].contiguous() masks_backward = masks[:, 1:, ...].contiguous() pred_flows_forward = pred_flows_bi[0] * masks_forward + masked_flows_bi[0] * (1-masks_forward) pred_flows_backward = pred_flows_bi[1] * masks_backward + masked_flows_bi[1] * (1-masks_backward) return pred_flows_forward, pred_flows_backward ================================================ FILE: propainter/model/vgg_arch.py ================================================ import os import torch from collections import OrderedDict from torch import nn as nn from torchvision.models import vgg as vgg VGG_PRETRAIN_PATH = 'experiments/pretrained_models/vgg19-dcbb9e9d.pth' NAMES = { 'vgg11': [ 'conv1_1', 'relu1_1', 'pool1', 'conv2_1', 'relu2_1', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5' ], 'vgg13': [ 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'pool5' ], 'vgg16': [ 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'pool5' ], 'vgg19': [ 'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1', 'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2', 'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3', 'relu3_3', 'conv3_4', 'relu3_4', 'pool3', 'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3', 'relu4_3', 'conv4_4', 'relu4_4', 'pool4', 'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3', 'relu5_3', 'conv5_4', 'relu5_4', 'pool5' ] } def insert_bn(names): """Insert bn layer after each conv. Args: names (list): The list of layer names. Returns: list: The list of layer names with bn layers. """ names_bn = [] for name in names: names_bn.append(name) if 'conv' in name: position = name.replace('conv', '') names_bn.append('bn' + position) return names_bn class VGGFeatureExtractor(nn.Module): """VGG network for feature extraction. In this implementation, we allow users to choose whether use normalization in the input feature and the type of vgg network. Note that the pretrained path must fit the vgg type. Args: layer_name_list (list[str]): Forward function returns the corresponding features according to the layer_name_list. Example: {'relu1_1', 'relu2_1', 'relu3_1'}. vgg_type (str): Set the type of vgg network. Default: 'vgg19'. use_input_norm (bool): If True, normalize the input image. Importantly, the input feature must in the range [0, 1]. Default: True. range_norm (bool): If True, norm images with range [-1, 1] to [0, 1]. Default: False. requires_grad (bool): If true, the parameters of VGG network will be optimized. Default: False. remove_pooling (bool): If true, the max pooling operations in VGG net will be removed. Default: False. pooling_stride (int): The stride of max pooling operation. Default: 2. """ def __init__(self, layer_name_list, vgg_type='vgg19', use_input_norm=True, range_norm=False, requires_grad=False, remove_pooling=False, pooling_stride=2): super(VGGFeatureExtractor, self).__init__() self.layer_name_list = layer_name_list self.use_input_norm = use_input_norm self.range_norm = range_norm self.names = NAMES[vgg_type.replace('_bn', '')] if 'bn' in vgg_type: self.names = insert_bn(self.names) # only borrow layers that will be used to avoid unused params max_idx = 0 for v in layer_name_list: idx = self.names.index(v) if idx > max_idx: max_idx = idx if os.path.exists(VGG_PRETRAIN_PATH): vgg_net = getattr(vgg, vgg_type)(pretrained=False) state_dict = torch.load(VGG_PRETRAIN_PATH, map_location=lambda storage, loc: storage) vgg_net.load_state_dict(state_dict) else: vgg_net = getattr(vgg, vgg_type)(pretrained=True) features = vgg_net.features[:max_idx + 1] modified_net = OrderedDict() for k, v in zip(self.names, features): if 'pool' in k: # if remove_pooling is true, pooling operation will be removed if remove_pooling: continue else: # in some cases, we may want to change the default stride modified_net[k] = nn.MaxPool2d(kernel_size=2, stride=pooling_stride) else: modified_net[k] = v self.vgg_net = nn.Sequential(modified_net) if not requires_grad: self.vgg_net.eval() for param in self.parameters(): param.requires_grad = False else: self.vgg_net.train() for param in self.parameters(): param.requires_grad = True if self.use_input_norm: # the mean is for image with range [0, 1] self.register_buffer('mean', torch.Tensor([0.485, 0.456, 0.406]).view(1, 3, 1, 1)) # the std is for image with range [0, 1] self.register_buffer('std', torch.Tensor([0.229, 0.224, 0.225]).view(1, 3, 1, 1)) def forward(self, x): """Forward function. Args: x (Tensor): Input tensor with shape (n, c, h, w). Returns: Tensor: Forward results. """ if self.range_norm: x = (x + 1) / 2 if self.use_input_norm: x = (x - self.mean) / self.std output = {} for key, layer in self.vgg_net._modules.items(): x = layer(x) if key in self.layer_name_list: output[key] = x.clone() return output ================================================ FILE: propainter/utils/__init__.py ================================================ ================================================ FILE: propainter/utils/download_util.py ================================================ import math import os import requests from torch.hub import download_url_to_file, get_dir from tqdm import tqdm from urllib.parse import urlparse def sizeof_fmt(size, suffix='B'): """Get human readable file size. Args: size (int): File size. suffix (str): Suffix. Default: 'B'. Return: str: Formated file siz. """ for unit in ['', 'K', 'M', 'G', 'T', 'P', 'E', 'Z']: if abs(size) < 1024.0: return f'{size:3.1f} {unit}{suffix}' size /= 1024.0 return f'{size:3.1f} Y{suffix}' def download_file_from_google_drive(file_id, save_path): """Download files from google drive. Ref: https://stackoverflow.com/questions/25010369/wget-curl-large-file-from-google-drive # noqa E501 Args: file_id (str): File id. save_path (str): Save path. """ session = requests.Session() URL = 'https://docs.google.com/uc?export=download' params = {'id': file_id} response = session.get(URL, params=params, stream=True) token = get_confirm_token(response) if token: params['confirm'] = token response = session.get(URL, params=params, stream=True) # get file size response_file_size = session.get(URL, params=params, stream=True, headers={'Range': 'bytes=0-2'}) print(response_file_size) if 'Content-Range' in response_file_size.headers: file_size = int(response_file_size.headers['Content-Range'].split('/')[1]) else: file_size = None save_response_content(response, save_path, file_size) def get_confirm_token(response): for key, value in response.cookies.items(): if key.startswith('download_warning'): return value return None def save_response_content(response, destination, file_size=None, chunk_size=32768): if file_size is not None: pbar = tqdm(total=math.ceil(file_size / chunk_size), unit='chunk') readable_file_size = sizeof_fmt(file_size) else: pbar = None with open(destination, 'wb') as f: downloaded_size = 0 for chunk in response.iter_content(chunk_size): downloaded_size += chunk_size if pbar is not None: pbar.update(1) pbar.set_description(f'Download {sizeof_fmt(downloaded_size)} / {readable_file_size}') if chunk: # filter out keep-alive new chunks f.write(chunk) if pbar is not None: pbar.close() def load_file_from_url(url, model_dir=None, progress=True, file_name=None): """Load file form http url, will download models if necessary. Ref:https://github.com/1adrianb/face-alignment/blob/master/face_alignment/utils.py Args: url (str): URL to be downloaded. model_dir (str): The path to save the downloaded model. Should be a full path. If None, use pytorch hub_dir. Default: None. progress (bool): Whether to show the download progress. Default: True. file_name (str): The downloaded file name. If None, use the file name in the url. Default: None. Returns: str: The path to the downloaded file. """ if model_dir is None: # use the pytorch hub_dir hub_dir = get_dir() model_dir = os.path.join(hub_dir, 'checkpoints') os.makedirs(model_dir, exist_ok=True) parts = urlparse(url) filename = os.path.basename(parts.path) if file_name is not None: filename = file_name cached_file = os.path.abspath(os.path.join(model_dir, filename)) if not os.path.exists(cached_file): print(f'Downloading: "{url}" to {cached_file}\n') download_url_to_file(url, cached_file, hash_prefix=None, progress=progress) return cached_file ================================================ FILE: propainter/utils/file_client.py ================================================ from abc import ABCMeta, abstractmethod class BaseStorageBackend(metaclass=ABCMeta): """Abstract class of storage backends. All backends need to implement two apis: ``get()`` and ``get_text()``. ``get()`` reads the file as a byte stream and ``get_text()`` reads the file as texts. """ @abstractmethod def get(self, filepath): pass @abstractmethod def get_text(self, filepath): pass class MemcachedBackend(BaseStorageBackend): """Memcached storage backend. Attributes: server_list_cfg (str): Config file for memcached server list. client_cfg (str): Config file for memcached client. sys_path (str | None): Additional path to be appended to `sys.path`. Default: None. """ def __init__(self, server_list_cfg, client_cfg, sys_path=None): if sys_path is not None: import sys sys.path.append(sys_path) try: import mc except ImportError: raise ImportError('Please install memcached to enable MemcachedBackend.') self.server_list_cfg = server_list_cfg self.client_cfg = client_cfg self._client = mc.MemcachedClient.GetInstance(self.server_list_cfg, self.client_cfg) # mc.pyvector servers as a point which points to a memory cache self._mc_buffer = mc.pyvector() def get(self, filepath): filepath = str(filepath) import mc self._client.Get(filepath, self._mc_buffer) value_buf = mc.ConvertBuffer(self._mc_buffer) return value_buf def get_text(self, filepath): raise NotImplementedError class HardDiskBackend(BaseStorageBackend): """Raw hard disks storage backend.""" def get(self, filepath): filepath = str(filepath) with open(filepath, 'rb') as f: value_buf = f.read() return value_buf def get_text(self, filepath): filepath = str(filepath) with open(filepath, 'r') as f: value_buf = f.read() return value_buf class LmdbBackend(BaseStorageBackend): """Lmdb storage backend. Args: db_paths (str | list[str]): Lmdb database paths. client_keys (str | list[str]): Lmdb client keys. Default: 'default'. readonly (bool, optional): Lmdb environment parameter. If True, disallow any write operations. Default: True. lock (bool, optional): Lmdb environment parameter. If False, when concurrent access occurs, do not lock the database. Default: False. readahead (bool, optional): Lmdb environment parameter. If False, disable the OS filesystem readahead mechanism, which may improve random read performance when a database is larger than RAM. Default: False. Attributes: db_paths (list): Lmdb database path. _client (list): A list of several lmdb envs. """ def __init__(self, db_paths, client_keys='default', readonly=True, lock=False, readahead=False, **kwargs): try: import lmdb except ImportError: raise ImportError('Please install lmdb to enable LmdbBackend.') if isinstance(client_keys, str): client_keys = [client_keys] if isinstance(db_paths, list): self.db_paths = [str(v) for v in db_paths] elif isinstance(db_paths, str): self.db_paths = [str(db_paths)] assert len(client_keys) == len(self.db_paths), ('client_keys and db_paths should have the same length, ' f'but received {len(client_keys)} and {len(self.db_paths)}.') self._client = {} for client, path in zip(client_keys, self.db_paths): self._client[client] = lmdb.open(path, readonly=readonly, lock=lock, readahead=readahead, **kwargs) def get(self, filepath, client_key): """Get values according to the filepath from one lmdb named client_key. Args: filepath (str | obj:`Path`): Here, filepath is the lmdb key. client_key (str): Used for distinguishing differnet lmdb envs. """ filepath = str(filepath) assert client_key in self._client, (f'client_key {client_key} is not ' 'in lmdb clients.') client = self._client[client_key] with client.begin(write=False) as txn: value_buf = txn.get(filepath.encode('ascii')) return value_buf def get_text(self, filepath): raise NotImplementedError class FileClient(object): """A general file client to access files in different backend. The client loads a file or text in a specified backend from its path and return it as a binary file. it can also register other backend accessor with a given name and backend class. Attributes: backend (str): The storage backend type. Options are "disk", "memcached" and "lmdb". client (:obj:`BaseStorageBackend`): The backend object. """ _backends = { 'disk': HardDiskBackend, 'memcached': MemcachedBackend, 'lmdb': LmdbBackend, } def __init__(self, backend='disk', **kwargs): if backend not in self._backends: raise ValueError(f'Backend {backend} is not supported. Currently supported ones' f' are {list(self._backends.keys())}') self.backend = backend self.client = self._backends[backend](**kwargs) def get(self, filepath, client_key='default'): # client_key is used only for lmdb, where different fileclients have # different lmdb environments. if self.backend == 'lmdb': return self.client.get(filepath, client_key) else: return self.client.get(filepath) def get_text(self, filepath): return self.client.get_text(filepath) ================================================ FILE: propainter/utils/flow_util.py ================================================ import cv2 import numpy as np import os import torch.nn.functional as F def resize_flow(flow, newh, neww): oldh, oldw = flow.shape[0:2] flow = cv2.resize(flow, (neww, newh), interpolation=cv2.INTER_LINEAR) flow[:, :, 0] *= neww / oldw flow[:, :, 1] *= newh / oldh return flow def resize_flow_pytorch(flow, newh, neww): oldh, oldw = flow.shape[-2:] flow = F.interpolate(flow, (newh, neww), mode='bilinear') flow[:, :, 0] *= neww / oldw flow[:, :, 1] *= newh / oldh return flow def imwrite(img, file_path, params=None, auto_mkdir=True): if auto_mkdir: dir_name = os.path.abspath(os.path.dirname(file_path)) os.makedirs(dir_name, exist_ok=True) return cv2.imwrite(file_path, img, params) def flowread(flow_path, quantize=False, concat_axis=0, *args, **kwargs): """Read an optical flow map. Args: flow_path (ndarray or str): Flow path. quantize (bool): whether to read quantized pair, if set to True, remaining args will be passed to :func:`dequantize_flow`. concat_axis (int): The axis that dx and dy are concatenated, can be either 0 or 1. Ignored if quantize is False. Returns: ndarray: Optical flow represented as a (h, w, 2) numpy array """ if quantize: assert concat_axis in [0, 1] cat_flow = cv2.imread(flow_path, cv2.IMREAD_UNCHANGED) if cat_flow.ndim != 2: raise IOError(f'{flow_path} is not a valid quantized flow file, its dimension is {cat_flow.ndim}.') assert cat_flow.shape[concat_axis] % 2 == 0 dx, dy = np.split(cat_flow, 2, axis=concat_axis) flow = dequantize_flow(dx, dy, *args, **kwargs) else: with open(flow_path, 'rb') as f: try: header = f.read(4).decode('utf-8') except Exception: raise IOError(f'Invalid flow file: {flow_path}') else: if header != 'PIEH': raise IOError(f'Invalid flow file: {flow_path}, header does not contain PIEH') w = np.fromfile(f, np.int32, 1).squeeze() h = np.fromfile(f, np.int32, 1).squeeze() # flow = np.fromfile(f, np.float32, w * h * 2).reshape((h, w, 2)) flow = np.fromfile(f, np.float16, w * h * 2).reshape((h, w, 2)) return flow.astype(np.float32) def flowwrite(flow, filename, quantize=False, concat_axis=0, *args, **kwargs): """Write optical flow to file. If the flow is not quantized, it will be saved as a .flo file losslessly, otherwise a jpeg image which is lossy but of much smaller size. (dx and dy will be concatenated horizontally into a single image if quantize is True.) Args: flow (ndarray): (h, w, 2) array of optical flow. filename (str): Output filepath. quantize (bool): Whether to quantize the flow and save it to 2 jpeg images. If set to True, remaining args will be passed to :func:`quantize_flow`. concat_axis (int): The axis that dx and dy are concatenated, can be either 0 or 1. Ignored if quantize is False. """ dir_name = os.path.abspath(os.path.dirname(filename)) os.makedirs(dir_name, exist_ok=True) if not quantize: with open(filename, 'wb') as f: f.write('PIEH'.encode('utf-8')) np.array([flow.shape[1], flow.shape[0]], dtype=np.int32).tofile(f) # flow = flow.astype(np.float32) flow = flow.astype(np.float16) flow.tofile(f) f.flush() else: assert concat_axis in [0, 1] dx, dy = quantize_flow(flow, *args, **kwargs) dxdy = np.concatenate((dx, dy), axis=concat_axis) # os.makedirs(os.path.dirname(filename), exist_ok=True) cv2.imwrite(filename, dxdy) # imwrite(dxdy, filename) def quantize_flow(flow, max_val=0.02, norm=True): """Quantize flow to [0, 255]. After this step, the size of flow will be much smaller, and can be dumped as jpeg images. Args: flow (ndarray): (h, w, 2) array of optical flow. max_val (float): Maximum value of flow, values beyond [-max_val, max_val] will be truncated. norm (bool): Whether to divide flow values by image width/height. Returns: tuple[ndarray]: Quantized dx and dy. """ h, w, _ = flow.shape dx = flow[..., 0] dy = flow[..., 1] if norm: dx = dx / w # avoid inplace operations dy = dy / h # use 255 levels instead of 256 to make sure 0 is 0 after dequantization. flow_comps = [quantize(d, -max_val, max_val, 255, np.uint8) for d in [dx, dy]] return tuple(flow_comps) def dequantize_flow(dx, dy, max_val=0.02, denorm=True): """Recover from quantized flow. Args: dx (ndarray): Quantized dx. dy (ndarray): Quantized dy. max_val (float): Maximum value used when quantizing. denorm (bool): Whether to multiply flow values with width/height. Returns: ndarray: Dequantized flow. """ assert dx.shape == dy.shape assert dx.ndim == 2 or (dx.ndim == 3 and dx.shape[-1] == 1) dx, dy = [dequantize(d, -max_val, max_val, 255) for d in [dx, dy]] if denorm: dx *= dx.shape[1] dy *= dx.shape[0] flow = np.dstack((dx, dy)) return flow def quantize(arr, min_val, max_val, levels, dtype=np.int64): """Quantize an array of (-inf, inf) to [0, levels-1]. Args: arr (ndarray): Input array. min_val (scalar): Minimum value to be clipped. max_val (scalar): Maximum value to be clipped. levels (int): Quantization levels. dtype (np.type): The type of the quantized array. Returns: tuple: Quantized array. """ if not (isinstance(levels, int) and levels > 1): raise ValueError(f'levels must be a positive integer, but got {levels}') if min_val >= max_val: raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})') arr = np.clip(arr, min_val, max_val) - min_val quantized_arr = np.minimum(np.floor(levels * arr / (max_val - min_val)).astype(dtype), levels - 1) return quantized_arr def dequantize(arr, min_val, max_val, levels, dtype=np.float64): """Dequantize an array. Args: arr (ndarray): Input array. min_val (scalar): Minimum value to be clipped. max_val (scalar): Maximum value to be clipped. levels (int): Quantization levels. dtype (np.type): The type of the dequantized array. Returns: tuple: Dequantized array. """ if not (isinstance(levels, int) and levels > 1): raise ValueError(f'levels must be a positive integer, but got {levels}') if min_val >= max_val: raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})') dequantized_arr = (arr + 0.5).astype(dtype) * (max_val - min_val) / levels + min_val return dequantized_arr ================================================ FILE: propainter/utils/img_util.py ================================================ import cv2 import math import numpy as np import os import torch from torchvision.utils import make_grid def img2tensor(imgs, bgr2rgb=True, float32=True): """Numpy array to tensor. Args: imgs (list[ndarray] | ndarray): Input images. bgr2rgb (bool): Whether to change bgr to rgb. float32 (bool): Whether to change to float32. Returns: list[tensor] | tensor: Tensor images. If returned results only have one element, just return tensor. """ def _totensor(img, bgr2rgb, float32): if img.shape[2] == 3 and bgr2rgb: if img.dtype == 'float64': img = img.astype('float32') img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) img = torch.from_numpy(img.transpose(2, 0, 1)) if float32: img = img.float() return img if isinstance(imgs, list): return [_totensor(img, bgr2rgb, float32) for img in imgs] else: return _totensor(imgs, bgr2rgb, float32) def tensor2img(tensor, rgb2bgr=True, out_type=np.uint8, min_max=(0, 1)): """Convert torch Tensors into image numpy arrays. After clamping to [min, max], values will be normalized to [0, 1]. Args: tensor (Tensor or list[Tensor]): Accept shapes: 1) 4D mini-batch Tensor of shape (B x 3/1 x H x W); 2) 3D Tensor of shape (3/1 x H x W); 3) 2D Tensor of shape (H x W). Tensor channel should be in RGB order. rgb2bgr (bool): Whether to change rgb to bgr. out_type (numpy type): output types. If ``np.uint8``, transform outputs to uint8 type with range [0, 255]; otherwise, float type with range [0, 1]. Default: ``np.uint8``. min_max (tuple[int]): min and max values for clamp. Returns: (Tensor or list): 3D ndarray of shape (H x W x C) OR 2D ndarray of shape (H x W). The channel order is BGR. """ if not (torch.is_tensor(tensor) or (isinstance(tensor, list) and all(torch.is_tensor(t) for t in tensor))): raise TypeError(f'tensor or list of tensors expected, got {type(tensor)}') if torch.is_tensor(tensor): tensor = [tensor] result = [] for _tensor in tensor: _tensor = _tensor.squeeze(0).float().detach().cpu().clamp_(*min_max) _tensor = (_tensor - min_max[0]) / (min_max[1] - min_max[0]) n_dim = _tensor.dim() if n_dim == 4: img_np = make_grid(_tensor, nrow=int(math.sqrt(_tensor.size(0))), normalize=False).numpy() img_np = img_np.transpose(1, 2, 0) if rgb2bgr: img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) elif n_dim == 3: img_np = _tensor.numpy() img_np = img_np.transpose(1, 2, 0) if img_np.shape[2] == 1: # gray image img_np = np.squeeze(img_np, axis=2) else: if rgb2bgr: img_np = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR) elif n_dim == 2: img_np = _tensor.numpy() else: raise TypeError('Only support 4D, 3D or 2D tensor. ' f'But received with dimension: {n_dim}') if out_type == np.uint8: # Unlike MATLAB, numpy.unit8() WILL NOT round by default. img_np = (img_np * 255.0).round() img_np = img_np.astype(out_type) result.append(img_np) if len(result) == 1: result = result[0] return result def tensor2img_fast(tensor, rgb2bgr=True, min_max=(0, 1)): """This implementation is slightly faster than tensor2img. It now only supports torch tensor with shape (1, c, h, w). Args: tensor (Tensor): Now only support torch tensor with (1, c, h, w). rgb2bgr (bool): Whether to change rgb to bgr. Default: True. min_max (tuple[int]): min and max values for clamp. """ output = tensor.squeeze(0).detach().clamp_(*min_max).permute(1, 2, 0) output = (output - min_max[0]) / (min_max[1] - min_max[0]) * 255 output = output.type(torch.uint8).cpu().numpy() if rgb2bgr: output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output def imfrombytes(content, flag='color', float32=False): """Read an image from bytes. Args: content (bytes): Image bytes got from files or other streams. flag (str): Flags specifying the color type of a loaded image, candidates are `color`, `grayscale` and `unchanged`. float32 (bool): Whether to change to float32., If True, will also norm to [0, 1]. Default: False. Returns: ndarray: Loaded image array. """ img_np = np.frombuffer(content, np.uint8) imread_flags = {'color': cv2.IMREAD_COLOR, 'grayscale': cv2.IMREAD_GRAYSCALE, 'unchanged': cv2.IMREAD_UNCHANGED} img = cv2.imdecode(img_np, imread_flags[flag]) if float32: img = img.astype(np.float32) / 255. return img def imwrite(img, file_path, params=None, auto_mkdir=True): """Write image to file. Args: img (ndarray): Image array to be written. file_path (str): Image file path. params (None or list): Same as opencv's :func:`imwrite` interface. auto_mkdir (bool): If the parent folder of `file_path` does not exist, whether to create it automatically. Returns: bool: Successful or not. """ if auto_mkdir: dir_name = os.path.abspath(os.path.dirname(file_path)) os.makedirs(dir_name, exist_ok=True) return cv2.imwrite(file_path, img, params) def crop_border(imgs, crop_border): """Crop borders of images. Args: imgs (list[ndarray] | ndarray): Images with shape (h, w, c). crop_border (int): Crop border for each end of height and weight. Returns: list[ndarray]: Cropped images. """ if crop_border == 0: return imgs else: if isinstance(imgs, list): return [v[crop_border:-crop_border, crop_border:-crop_border, ...] for v in imgs] else: return imgs[crop_border:-crop_border, crop_border:-crop_border, ...] ================================================ FILE: pyproject.toml ================================================ [project] name = "comfyui_diffueraser" description = "DiffuEraser is a diffusion model for video Inpainting, you can use it in ComfyUI" version = "1.0.0" license = { file = "LICENSE" } dependencies = ["torch", "torchvision", "torchaudio", "diffusers", "accelerate", "opencv-python", "imageio", "#matplotlib", "transformers", "einops", "#datasets", "#numpy==1.26.4", "#pillow==10.4.0", "#tqdm==4.66.4", "#urllib3==2.2.2", "#zipp==3.19.2", "peft", "#scipy==1.13.1", "#av==14.0.1"] [project.urls] Repository = "https://github.com/smthemex/ComfyUI_DiffuEraser" # Used by Comfy Registry https://comfyregistry.org [tool.comfy] PublisherId = "smthemex" DisplayName = "ComfyUI_DiffuEraser" Icon = "" ================================================ FILE: requirements.txt ================================================ torch torchvision torchaudio diffusers accelerate opencv-python imageio #matplotlib transformers einops #datasets #numpy==1.26.4 #pillow==10.4.0 #tqdm==4.66.4 #urllib3==2.2.2 #zipp==3.19.2 peft #scipy==1.13.1 #av==14.0.1 ================================================ FILE: run_diffueraser.py ================================================ import torch import os import time import random from .libs.diffueraser import DiffuEraser from .propainter.inference import Propainter, get_device import folder_paths import gc def load_diffueraser(sd_repo,pre_model_path, ckpt_path,original_config_file,pcm_lora_path,device): device = get_device() model=DiffuEraser(device) model.load_model(sd_repo, pre_model_path,ckpt_path,original_config_file, pcm_lora_path,) return model def load_propainter(fix_raft_path,flow_path,ProPainter_path,device="cpu"): model=Propainter(device) model.load_propainter(fix_raft_path,flow_path,ProPainter_path) return model ================================================ FILE: sd15_repo/feature_extractor/preprocessor_config.json ================================================ { "crop_size": { "height": 224, "width": 224 }, "do_center_crop": true, "do_convert_rgb": true, "do_normalize": true, "do_rescale": true, "do_resize": true, "feature_extractor_type": "CLIPFeatureExtractor", "image_mean": [ 0.48145466, 0.4578275, 0.40821073 ], "image_processor_type": "CLIPFeatureExtractor", "image_std": [ 0.26862954, 0.26130258, 0.27577711 ], "resample": 3, "rescale_factor": 0.00392156862745098, "size": { "shortest_edge": 224 } } ================================================ FILE: sd15_repo/model_index.json ================================================ { "_class_name": "StableDiffusionPipeline", "_diffusers_version": "0.21.0.dev0", "_name_or_path": "lykon-models/dreamshaper-8", "feature_extractor": [ "transformers", "CLIPFeatureExtractor" ], "requires_safety_checker": true, "safety_checker": [ "stable_diffusion", "StableDiffusionSafetyChecker" ], "scheduler": [ "diffusers", "DEISMultistepScheduler" ], "text_encoder": [ "transformers", "CLIPTextModel" ], "tokenizer": [ "transformers", "CLIPTokenizer" ], "unet": [ "diffusers", "UNet2DConditionModel" ], "vae": [ "diffusers", "AutoencoderKL" ] } ================================================ FILE: sd15_repo/safety_checker/config.json ================================================ { "_name_or_path": "/home/patrick/.cache/huggingface/hub/models--lykon-models--dreamshaper-8/snapshots/7e855e3f481832419503d1fa18d4a4379597f04b/safety_checker", "architectures": [ "StableDiffusionSafetyChecker" ], "initializer_factor": 1.0, "logit_scale_init_value": 2.6592, "model_type": "clip", "projection_dim": 768, "text_config": { "dropout": 0.0, "hidden_size": 768, "intermediate_size": 3072, "model_type": "clip_text_model", "num_attention_heads": 12 }, "torch_dtype": "float16", "transformers_version": "4.33.0.dev0", "vision_config": { "dropout": 0.0, "hidden_size": 1024, "intermediate_size": 4096, "model_type": "clip_vision_model", "num_attention_heads": 16, "num_hidden_layers": 24, "patch_size": 14 } } ================================================ FILE: sd15_repo/scheduler/scheduler_config.json ================================================ { "_class_name": "DEISMultistepScheduler", "_diffusers_version": "0.21.0.dev0", "algorithm_type": "deis", "beta_end": 0.012, "beta_schedule": "scaled_linear", "beta_start": 0.00085, "clip_sample": false, "dynamic_thresholding_ratio": 0.995, "lower_order_final": true, "num_train_timesteps": 1000, "prediction_type": "epsilon", "sample_max_value": 1.0, "set_alpha_to_one": false, "skip_prk_steps": true, "solver_order": 2, "solver_type": "logrho", "steps_offset": 1, "thresholding": false, "timestep_spacing": "leading", "trained_betas": null, "use_karras_sigmas": false } ================================================ FILE: sd15_repo/text_encoder/config.json ================================================ { "_name_or_path": "/home/patrick/.cache/huggingface/hub/models--lykon-models--dreamshaper-8/snapshots/7e855e3f481832419503d1fa18d4a4379597f04b/text_encoder", "architectures": [ "CLIPTextModel" ], "attention_dropout": 0.0, "bos_token_id": 0, "dropout": 0.0, "eos_token_id": 2, "hidden_act": "quick_gelu", "hidden_size": 768, "initializer_factor": 1.0, "initializer_range": 0.02, "intermediate_size": 3072, "layer_norm_eps": 1e-05, "max_position_embeddings": 77, "model_type": "clip_text_model", "num_attention_heads": 12, "num_hidden_layers": 12, "pad_token_id": 1, "projection_dim": 768, "torch_dtype": "float16", "transformers_version": "4.33.0.dev0", "vocab_size": 49408 } ================================================ FILE: sd15_repo/tokenizer/merges.txt ================================================ #version: 0.2 i n t h a n r e a r e r th e in g o u o n s t o r e n o n a l a t e r i t i n t o r o i s l e i c a t an d e d o f c h o r e s i l e l s t a c o m a m l o a n a y s h r i l i t i f or n e ð Ł r a h a d e o l v e s i u r a l s e ' s u n d i b e l a w h o o d ay e n m a n o l e t o ou r i r g h w it i t y o a s s p th is t s at i yo u wit h a d i s a b l y w e th e t e a s a g v i p p s u h o m y . . b u c om s e er s m e m e al l c on m o k e g e ou t en t c o f e v er a r f ro a u p o c e gh t ar e s s fro m c h t r ou n on e b y d o t h w or er e k e p ro f or d s b o t a w e g o h e t er in g d e b e ati on m or a y e x il l p e k s s c l u f u q u v er ðŁ ĺ j u m u at e an d v e k ing m ar o p h i .. . p re a d r u th at j o o f c e ne w a m a p g re s s d u no w y e t ing y our it y n i c i p ar g u f i a f p er t er u p s o g i on s g r g e b r p l ' t m i in e we e b i u s sh o ha ve to day a v m an en t ac k ur e ou r â Ģ c u l d lo o i m ic e s om f in re d re n oo d w as ti on p i i r th er t y p h ar d e c ! ! m on mor e w ill t ra c an c ol p u t e w n m b s o it i ju st n ing h ere t u p a p r bu t wh at al ly f ir m in c a an t s a t ed e v m ent f a ge t am e ab out g ra no t ha pp ay s m an h is ti me li ke g h ha s th an lo ve ar t st e d ing h e c re w s w at d er it e s er ac e ag e en d st r a w st or r e c ar el l al l p s f ri p ho p or d o a k w i f re wh o sh i b oo s on el l wh en il l ho w gre at w in e l b l s si al i som e ðŁ Ĵ t on d er le s p la ï ¸ e d s ch h u on g d on k i s h an n c or . . oun d a z in e ar y fu l st u ou ld st i g o se e ab le ar s l l m is b er c k w a en ts n o si g f e fir st e t sp e ac k i f ou s ' m st er a pp an g an ce an s g ood b re e ver the y t ic com e of f b ack as e ing s ol d i ght f o h er happ y p ic it s v ing u s m at h om d y e m s k y ing the ir le d r y u l h ar c k t on on al h el r ic b ir vi e w ay t ri d a p le b ro st o oo l ni ght tr u b a re ad re s ye ar f r t or al s c oun c la t ure v el at ed le c en d th ing v o ic i be st c an wor k la st af ter en ce p ri p e e s i l âĢ ¦ d re y s o ver i es ðŁ ij com m t w in k s un c l li fe t t a ch l and s y t re t al p ol s m du c s al f t ' re ch e w ar t ur ati ons ac h m s il e p m ou gh at e st ar wee k ! !! c lu th ere n er t om s el ï¸ ı wor ld v es c am go t in ter of f u m ton ight o ther h ou loo k j e i d si on be au at t el i or t re c f f st er su pp g en be en il y te am m m i c pe op it t at s on ly mb er en g b ri m p k now b ur b ar in s lo w sh e ro w â Ŀ t ro peop le vi a lo w ag a be t x t f ac ch ar e ar w al s en f am b le n ati is h n or g ame li ve s co le y d on ic k b all ver y the se p an i a at ing c r a re g ir ma ke st re sho w . " f l u p d r than ks il li w om st s i g s ur ever y c ur vie w le t in to mo st n a in di g ar ha d s ou v ed an t iti on ma de f ol un i it ed ðŁ ı ic al th r read y ch ec d ra k es boo k e p si c mor ning ne ws c au c t w ell an c pho to th an or s bir th g g ou t ne xt som e en ing stor y ch ri do wn hom e f fe fre e d a b or f il ci al than k si de le ar qu e l ine t en at es ye ars m y pho to beau ti ri ght n u for m shi p b an th er d ays g am as on g y ðŁ İ birth day se t ic k e t st ill com ing ta ke ðŁ ĩ b b s ol s on d en e p mu sic the m de n wh y f oo c ra am az w n h ol t ting w r u e ma g c ro l an c lo b ra a k s ing c al re ad ' ve jo h b ab d ri b lo bi g er ic in t t or tr y l a le g hou se m ic v al beauti ful l itt chec k ne w ver s s w ar i pla y h er âĢ ĵ w in m a con gr sch ool f un . @ he al ic h d el wh ere l on ke t tw o mu ch wat ch v en d ed a st k ed b as go ing m p e ver w ays ro o de sig l y s ed to p l in ch an to o it ing d ent gh ts t y sp o ne ed b lu in st be ing âĿ ¤ w el l s hi m m ay st ing n a el y litt le g a n at tom or m c h on w ant a ir pi c am eric p er le ss wee k ve l a h c ap ch am g er ti m tomor row ne ss st ate h al ser v z e o s p at v is ex c s in f f c ity c en an y b el su mm t in w ould loo king k o ce le fam ily m er po w hel p bu s c o c le sel f en s ic s th o an i ch o le ad b s t wee th ink for e ch il vi de di d al e ch i v il en ds w ing p as ' ll v ol s a g s man y j ec be fore gra ph n y ur ing w il d d bu il f av st ed tr an l ing ou d d ge fi el nati onal st a c er w ere in a se ason c ou n ed amaz ing ti ons cele br n s a th he ad s day d ar lo c v in an other g oo s at n y jo in pre s s es s ing an a in ing .. .. c our ï¸ ı ac t cau se li ght am s t a b al f c hi gh off ici t t chri st d ic d ay ra l h or : ) vi si n am o b ma s gh t re ally t un fin d thr ough por t u t ti ve st y n e or e ðŁĺ Ĥ supp ort ne ver ev en ðŁ Ķ h a y a l d u k r an j am wi th me di d es ne y ch ing al e h y k in ! ! d y pl ace al so b le wh ich bl ack b li s ay par k pl ay ir e vide o week end a il ke y p t w ard fri day d in ine ss g ro b en al ways t ball ag o m il c y pro duc di sc un der ple ase sp or fu ll e y ðŁ Ļ is e iti es c at k no u se fo re k er ar t hi gh op en s an e f our s sh ed st ri d ro aga in i m ðŁ ĵ en jo fu n ge tting p en g er c li an y ever y e u wom en â ľ e st c ould r y " @ th ou sh a comm un b er d ents di s wh ile aw ay di o h am g la d ate k a mis s un ch w on in f roo m g a re al ex per di rec sh ould sp r g ol l ong bet ter or i e y i ence il s z z h an f ound v s â Ļ po st ti c par t m en ren ce ce ss v ic s il sho p ðŁĺ Ĥ f ood v al sti c y ou s ays e lec st ar o c l and i d c tion fiel d s of st art wat er fri ends on es ðŁ Į f la f ar wh ite par ty in st gr ou t v every one m ent j a ch a pr in an ts d uring l at l ar we st th en k a y oun in sp in te we en visi t aga inst re le he ad c es to wn loo ks th re re gi ren t pro jec gir l se ar w o m om c ar h un pu bli d i p le c all c ri u m for d per fe fri end h ard ssi on te st pla ying ar ound be cause ke ts me et sat ur ar ti wor k j un v en r un me mber por t su per t wit s am el s t ly ad v ati ve at h s ure av ail la r s qu ar ds ev ent m en l l o ver lo gy it al tim es m al b ack c oo ma king st ru â ģ it u sh ar g an c as s n summ er pic ture f an h in christ mas c y pr oud cham pi desig n pp ing ho pe c a avail able ma y we d photo graph spe cial sal e sto p er y a we al ity hi story am a pre si b ru wor king d one d r k en fe at w ood ate st sun day mo vi vel y s le f ace sp ec stu dents b y ha m sp on bus iness d at i e i p so ci g lo h and re cor r s me e ke ep p ur heal th sh e com ple go d da vi col lec li st r a clu b t ers in clu th ings pl an â ĺ joh n sh ing at ul so on blu e g or satur day w on congr atul se e âĿ¤ ï¸ı tho se ðŁĺ į fin al d ou it h o wn ro ad t our a st indi a ti l n d f er fav or su l lear n fir e ju st grou p a h r ac bo dy u r c are à ¸ p lo o h po s gi ve te ch su b c ent er ing y m il ity f ic lon don v ir gu ys b a ðŁ ¤ bab y sc re ðŁĺ į tru mp un der chan ge i an col le ss es l er ss ed n ice ann oun pow er s ar a king min i s li s wee k ar fu l c ru ac tion a ther ) . st and de vel a a g an le ft lo l re l tran s m ents in t e f man ag di g gen er do wn p au ti v k u th ur k en st on f ans tal k twee t t oo sty le pro te se con fr on awe some g l p al ne t s or la u g on sin ce t ty ser ies me mor b eli fil m di d di es o t congratul ations p ra e ve w oo offici al su c in cre b on par t pp ed cla ss si ve bo y cu l perfe ct t ou d am wel come foo tball h i p ap wa it ad a congr ats youn g exc ited re ce j an v a re d st ra medi a ' d do es le t mu l ill s gre en m el to ge fu ture ye ster vers ity for m ta in i de ch es ki ds qu i ha ha de ta bi g favor ite gir ls con tin do m sear ch u al a ir d ers mon th c er yester day commun ity ad e do g vil le ic es d eli sy ste ru n is m he art c up en ti fe w presi dent e ds un til fe sti o k f lo sa id ol e me d tra vel  £ ph one toge ther fa st lo t gam es sh ir bet ween y es th ers do ing m ac at or b and fol low projec t devel op di ffe con fe spe ci ca st y s bo ard r d i al sh oo r am ha ving sh are fol low on e n ame m r pu t disc u or y c ame ou s s ite twit ter t b t it fin ally z ed su per com pan us ing all s li st r is sho t g al t ar de l joh n âĢ Ķ some thing ra m inte re wh e b it ðŁ į stre et oun d a i tic kets movi e re al k y ta king o pp c c l am m oun in ve bl ack us ed on line y or loc al gu e c ks o w ge st bo ys illi on con t re ci in ed eu ro no w se en p h te ach de f sou th su ch aw ard mu st is su ca re fe el p lu l atest spor ts we b te x e ment s k fi c w an te ch o t bo x n er fre e t al a sh c ase ho t won der mee ting er a ch all ðŁ IJ jo b il i c ool j our th s m o f el di e mic ha e le te am serv ice st and ma kes p ing ear ly com es e k ho li v ers ag ue s au thre e mon day fa shi some one th ro se a b ad supp or tur n ur y m ing photograph y n ic mar k pre tty ss ing wat ching me mb ar ri coun ty be ach fr an cen ter pol ice b at publi c t an pre ss s af s y ge ts ro y n ers y our bu y st ers sho w as ed chil dre af ric in es sp ace sc ri h all pa in ar ing hom e m ur heal th ch ed s and rece i gu y e a americ an re si childre n - - i ri ing ton coun try ro ss le n ann a boo ks b c e ce d om lo vely k h pe t g y g ri st age off ice ro ck m on b ay t able su n m ed th in l or f low ( @ uni versity stor e fron t goo d z a vo te nor th he y an im or der mi d with out a de re member mar ket ? ? mu s tra ining e duc bu t co ver st an sc en b la bre ak l ou s ame g old a in o s bo th l it ver n a i al bu p a enjo y be g ell ing thur sday inf o s an americ a ha ir te l mar ch con cer colle ge confe rence ap p h our ch ang â ļ s our ol s we ather w ar p hi festi val secon d cu te pr ac en er str y le a pol it s av se n o w m i ne ar ou ght z e co ffe w illi d an se y davi d e se f an de ci the at no v ati on tr ac sc i re view c el e m u n ju ly or ig ti on d ru form er st ay af ter in v too k dat a b al tu es d an ev ening ðŁĺĤ ðŁĺĤ d ol u res pro vi t s e st sig n j ac u k s ong ye t bo w in du j ap h oo po int any one z y i st h ur it al buil ding wom an ch ur j er per for co ach le ague ce ss ne t i mag nati on br it qu e aw ards ag es wor ks c ed man ce l ate ig n mon ey tru e i i t ell pl ac p ac as y wor ld be hin im port read ing gra m gi ving me t h it for ward st om pres ent jun e so cial no on mar t hal f s we go vern k er deta ils li sh _ _ ac y si a ber t f all ! !!! ) , th i d iti sp ort k ing f it st af c at mu se cen tr y er con tro b loo wal k ac tu did n li m lear ning re search wed ne au th h ours k y f ar h en .. .. it ch ri l str ong sk y que sti jam es r on d g f ur c in do es app ro mar ke tu res ful ly ch at behin d te m fin i mis sion b att fe el he av every thing b ar w ish pre mi i ma exper ience e ach re port swee t tic s spr ing re spon syste m vic tor l in sa w al ready gh ter f le ã ĥ br ing albu m - - ell s st an to m inter national w ent an ni mat ch pp er st one sm all ra in fashi on are a v an ag ram k o thou ght wor th v an m er coffe e it es g n arti st c on ar ch c ir se cre gr ound is o h and co m bri dge h s x i l ink pu l sp l r ace f li ri ver g as di sco d al play er f it photo s it y o k j or tr a ap ril ad s a di sol u beau ty do or me ss up date ali a sch o en ed mom ent sco t sc ience i or ti es ac ross ous ly sh es does n p age wat er m illion cla ssi l ic ca st form ation micha el ell o s mo in ts vi sion op ening ld n au str tues day win ner po ssi r ound shir t di t b o u es il led al ong tri p star ting im pro k an per son no t re co ne eds c le li e re st r ing win ter si mp mo m be er fac e tor s us a collec tion ge or se ssion tr ying la s la ke j en orig in stu dent se cur v in pic s ex pe com p gon na e qu b ad le y a u memb ers bre ak w all gi c din ner bu l insp ir r i min d ic a win ning tal king t ren s is t en wonder ful s now he ar th om no thing gu i st in blo g fe st b un le e war ds ch ance dre ss re n pau l p es tech no ru ssi c ard e ast mar i w ine t i la w str ic k i ap e au gu pro fe as h cour se ma il ren tly d un m un lo ve is land dri ve s l end ed ma in lo st nat ure âĿ¤ ï¸ı ch ic re por p in pr o st ation ce p ta kes compan y go es on d ma ch ra dio d ad ro ck j a p ay champi on e e in de tt a ati c t ab beli eve ener gy z i t at wor d on ce re sul y l and re an o inst agram clo se t am cu stom w a con om sho ws li fe k in ro b t age n ation al most list en sa ve re li ac e mar y tre e for get j ack wa iting direc tor h ill bor n te mp f l st e on a sing le wedne sday un ited in o @ _ ne l celebr ate en ding de al j i can ada hu ge tr ack âĢ ¢ f y fan ta an g yor k rele ase p un ep iso wor ds t our p ack i gh classi c perfor mance ke t after noon recor d win s pro ble âĿ ¤ f our b ed ban k d ance s la cal led mi ght a p pa st ðŁ ļ diffe rent it e gi ft ssi ve chur ch c us pro gram ho tel ic e ma d secur ity en ge d c en ough st a e ty de ad g un he ar m ir hu man gre ss oun ds pi ece bre aking gar den fi ght vie ws f ish star ted run ning gre en ser i s m as k d or de ath e conom er i ir d s er l unch âģ ¦ bo x nat u ba se b an f al glo bal wil d wo w out side mo ve le ad an al muse um on g ha w pow er than k b ac char ac cam pa dig ital r o op er de v w ol p ati f a m ale pap er ill ing c s â ĥ educ ation ta ken e ffe m ou s ad " . bas ed staf f inclu ding li ving a c ch ina mo b stor m lu ck ph il o o y n tra vel k el ti al pr ice boo k import ant bi o p ool ny c f ab lo ad ? ! chall enge cr y ser ve we ar bu s ta in nu mber ro r k at i z th ough ho sp m m fa ir ut es ho t po p fi ed cam p develop ment li br c ali em s âģ¦ @ b ol is ed stand ing mo del it a g le bro wn ima ge ve red for ce o il par tic sh u da ily la w se c cla ss cam p holi day cl in k ers pres ent gam e incre di er ship inter view b ill du e and y ab o in nov ke y ac ade p il mo der st ars br and f er wee ks con si pr e sa fe wr it di um la unch marke ting ann ual as si cour t la dy c ted and a in side chil d opp or sm ith centr e gu e âģ © f ren st y for t ent ly is n ke ep to ber on y bo y al d col la de mo le vel com pet ad o b our fanta stic m ate s u sou th oppor tun vers ary lat er bu d face book la un ster n p it ! " ma j gr am tb t fi re happ y a ks wh ole actu ally ill er ell a lo ts al ex an ge lan ds ðŁĺ Ń en ter r ou episo de p ed in ten sh ire wh o pl an h o ca ke we st mag az fre sh c c n ar ch ris wr iting w er n om l o mi dd dre am o l ti onal de b > > be come s i gr and all ing hi stor ri de i red saf e que en ci l in tro vi l d ani .. . ar tic st at sh ort or ing sel fi mis si do c b it g all b om i re se lec d ition ðŁĶ ¥ fri end be at gh ting ðŁĺ Ĭ pe ace ex hi ant a ab ility il lu j on qu ality tri bu m es play ers fa ir cu t c ab suc cess b i su s pro mo sch e an ge ic o comm it cat ch ill a kin d feel ing qu o s ay anni versary spo t mo ther an e p end your self op s app le min utes p o gr and ri es ha ha care er ed ition de c ric k am i concer t iti ve ge ous d ly t te adv ent i g li ghts ak er sk y âĥ £ r ay fini shed w ay s d ac coun ðŁĴ ķ ck y ch el lit er pain ting lo s st un techno logy n as ma r b il afric a ki e ey es gol f plu s ni a it ec serv ices wed ding kno wn te le .. ... star ts pa ren w ants ati onal mon ths win do fav our er t magaz ine ex clu re ve b c origin al e ss n al an ti st ro t ice stu dy à ¤ v ac nation al fi ve ra in ve ment u te ver se em er ar my possi ble gue ss val ley ther n cro w m r col or on to pic k cle ar dar k t ac wan ted it ting can cer govern ment di e ri se z ing col d f oun stu dio str ation bro ther a head sh el mic ro ic ally d au sig ned vi ol a x as se i o w re spl ay ch ick augu st pl at ti ps sp i hu man e asy lo gi mi ke gro w ag re w w sh ad mo tiv wi de tur ns om g v ar de fin su g j im ðŁĶ ¥ t d campa ign nam ed re tweet co p t v le av k is dou ble s mar issu e vil la in formation li es sto ck n t di stric sh or mi x er o se p me x see ing li ve re min co de g ur s c wil d l un h ood spo t fa ther fore ver up d tra f f ly ne ed gra du tra in ma ke s ab be y si ze lead er tal ks e u lo g fo x gor geous le ss le ts sur pri my self no te li ves f ru lo ved se ver de m j i so c h old do gs n i â ŀ lea ve air port ben ef ex pl shi ps comple te ach i gre at vin tage j ack ro c woo d pri v off er ey e ver sion te a co ach off ic w ell g en s at h h you th o x ? " m t mi x g g d le natu ral buil d break fast thin king theat re mo on ber g go als geor ge en e exc ell il ing tun e y ed g ate m it net work jo e h ello f b tu be we aring ath le stru c har d gla ss g ers thro w g es b t indu stry manag ement ali st go al stre am y el a vi ici ous o thers s ki chri sti bir d e sc m in tr o l t j an im p ri ghts sh a or gan cent ral ar a ro ll favour ite che ster el se p ay car s m ine ste p prac tice maj or h ang ðŁĺ ĺ n on v ari eng ine vol un di a i led arch itec p ink d s th y wa sh web site ba g contro l el li f ra an sw d ence y u r on ol a g in dr in li c cou ple sp ar g on cre ate c t celebr ating de ep e at te e vo ice dro p vis it at ors sta dium f t w is ro l gra de fam il po ints re pre w as traf fic jap an or g hon or tex as man u âĻ ¥ safe ty re r b ag em plo rele ased re gu ak a n av ro le sen ior spec t cro ss lin es be st p ack s in ti e mis sing sun set li ber is ing j ay sk i champion ship ac tiv la dies play ed y y pu bl al o pri de s r pa ki lu x sur vi ck ed e ts cho col austr alia par is mi les h at ment al al a me an mob ile en a in si f ound chi ef t ag incredi ble re turn à © goo gle fren ch cre w hal lo ali an j az ch er sil ver nor th eng lish base ball c af lim ited follow ing app reci ear th k ir ve mber w ed p tion g ed oc tober fl ori c r en cy ga ve lor d stu ff ber ry po st sm ile bro ad st ate gg er me ans ic y gu n y o ma ster bur g han ds ni e / / uni on brit ish big gest distric t am ing h il o ce per son pas s en vir scho ols arri ved anc es insp ired ex pla be n libr ary bo tt am p ste ph cont act b ang m s cali for t old batt le b b chic ago âľ ¨ str ate sh i de ce - ) ad d la b j ones leg end cast le ing er st ance be l ur a re fu lead ers po t se x h ic artic le ki d fr ance x x ex e gui de volun te pr int al i ce o twee ts w x scen e vol u ant i h an as soci shar ing ro se mini ster sh er in ste cle an demo cr po ster sk in p sy pro per cra zy i am o re in i any thing po d mo ving cl ick ex plo com b cra ft f i bloo d is ra publ ic d ent ol ym eng land a si ch er fac t envir on har ry g one me dic enjo ying just ice j r indi an wi fe s ound t es dra wing p al ide a cr it ju li il er war m cl ar thou ghts def en coun cil intro duc di ed jan u an i s end li er m l intere sting tra de win d b ay s ac anc y sour ce b es org ani ar ly lar ge ff ici ta g u t de sp o es tit le sy m pic tures op en wom en sho wing ri a le ast lead ership cur rent elec tr val ent list ening c key gener al de ser du ce ; ) c ent ðŁĺį ðŁĺį sco tt po or selfi e ev ents i on wr ong de v h ill sep te cul ture l ine sor ry s ent si ster ce pt k ri no vember ar i announ ce z ation br an g ent d u l en per s f m mart in o p e mb om e midd le suc cess pe ter janu ary f lu rac ing d av bi ke ðŁı » pe t shoo t profe ssi feat uring septe mber now playing sta ur z a on ic qu ick bas ke spe aking mil it z er chick en b ell s ad co ast lo ving y ers d j pan el ver age s wit ic ks b ou califor nia s am paren ts er o k illed ph ys jo bs mi gr an th e mo hallo ween and er c m compet ition e ag s ket sp ir may be exclu sive app e jour ney scre en for d i o h ate u g sou l her o soci ety sy n gu it n h d j as es im pre ti me sal es d d f ts summ it stun ning om s tur ned cle an sof t be at re staur de red en ces ma gic di o sh ine gu est health y exhi b stor ies po pu n is el a bel ow fun ny resul ts s ne cur rently ar d down load f light m al f ine p ad ch u ent ed h at ðŁij ı ste ve j o mar k r at b all p c p on b by o li ar ts as ure bow l att ack mi c de ar ran ge en ter chocol ate br illi ac cess , " ? ?? ch ap con st t n mat ter blu e gall ery em p work shop lead ing y ours baske tball w anna th u _ _ mar ri sle ep bi a ch e ma d imp act o wn si r chan nel euro pe e sp k itch hosp ital w ra roy al f s ne u qu ar ne y ac ks ch ase pp y st al at ely ti m dece mber r are per form cre am we ight ch oo ni ght ha ven fr anc kh an buil t hel ping tru st ty pe gol den ta x s now s wi di sa 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