Repository: Tencent-Hunyuan/Hunyuan3D-2 Branch: main Commit: f8db63096c82 Files: 100 Total size: 570.5 KB Directory structure: gitextract_k0kyt9hn/ ├── .gitignore ├── .readthedocs.yaml ├── LICENSE ├── NOTICE ├── README.md ├── README_ja_jp.md ├── README_zh_cn.md ├── api_server.py ├── assets/ │ ├── 1.glb │ ├── example_prompts.txt │ ├── modelviewer-template.html │ └── modelviewer-textured-template.html ├── blender_addon.py ├── docs/ │ ├── Makefile │ ├── README.md │ ├── make.bat │ ├── requirements.txt │ └── source/ │ ├── _static/ │ │ └── css/ │ │ └── custom.css │ ├── citation.md │ ├── conf.py │ ├── index.md │ ├── installation/ │ │ └── index.md │ ├── modelzoo.md │ └── started/ │ ├── api.md │ ├── blender.md │ ├── code.md │ ├── comfyui.md │ ├── gradio.md │ ├── index.md │ └── studio.md ├── examples/ │ ├── fast_shape_gen_multiview.py │ ├── fast_shape_gen_with_flashvdm.py │ ├── fast_texture_gen_multiview.py │ ├── faster_shape_gen_with_flashvdm_mini_turbo.py │ ├── shape_gen.py │ ├── shape_gen_mini.py │ ├── shape_gen_multiview.py │ ├── shape_gen_v2_1.py │ ├── textured_shape_gen.py │ ├── textured_shape_gen_mini.py │ └── textured_shape_gen_multiview.py ├── gradio_app.py ├── hy3dgen/ │ ├── __init__.py │ ├── rembg.py │ ├── shapegen/ │ │ ├── __init__.py │ │ ├── models/ │ │ │ ├── __init__.py │ │ │ ├── autoencoders/ │ │ │ │ ├── __init__.py │ │ │ │ ├── attention_blocks.py │ │ │ │ ├── attention_processors.py │ │ │ │ ├── model.py │ │ │ │ ├── surface_extractors.py │ │ │ │ └── volume_decoders.py │ │ │ ├── conditioner.py │ │ │ └── denoisers/ │ │ │ ├── __init__.py │ │ │ ├── hunyuan3ddit.py │ │ │ ├── hunyuandit.py │ │ │ └── moe_layers.py │ │ ├── pipelines.py │ │ ├── postprocessors.py │ │ ├── preprocessors.py │ │ ├── schedulers.py │ │ ├── surface_loaders.py │ │ └── utils.py │ ├── texgen/ │ │ ├── __init__.py │ │ ├── custom_rasterizer/ │ │ │ ├── custom_rasterizer/ │ │ │ │ ├── __init__.py │ │ │ │ ├── io_glb.py │ │ │ │ ├── io_obj.py │ │ │ │ └── render.py │ │ │ ├── lib/ │ │ │ │ └── custom_rasterizer_kernel/ │ │ │ │ ├── __init__.py │ │ │ │ ├── grid_neighbor.cpp │ │ │ │ ├── rasterizer.cpp │ │ │ │ ├── rasterizer.h │ │ │ │ └── rasterizer_gpu.cu │ │ │ └── setup.py │ │ ├── differentiable_renderer/ │ │ │ ├── __init__.py │ │ │ ├── camera_utils.py │ │ │ ├── compile_mesh_painter.bat │ │ │ ├── mesh_processor.cpp │ │ │ ├── mesh_processor.py │ │ │ ├── mesh_render.py │ │ │ ├── mesh_utils.py │ │ │ └── setup.py │ │ ├── hunyuanpaint/ │ │ │ ├── __init__.py │ │ │ ├── pipeline.py │ │ │ └── unet/ │ │ │ ├── __init__.py │ │ │ └── modules.py │ │ ├── pipelines.py │ │ └── utils/ │ │ ├── __init__.py │ │ ├── alignImg4Tex_utils.py │ │ ├── counter_utils.py │ │ ├── dehighlight_utils.py │ │ ├── imagesuper_utils.py │ │ ├── multiview_utils.py │ │ ├── simplify_mesh_utils.py │ │ └── uv_warp_utils.py │ └── text2image.py ├── minimal_demo.py ├── minimal_vae_demo.py ├── requirements.txt └── setup.py ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ !hy3dgen/texgen/custom_rasterizer/lib/ lib64/ parts/ sdist/ var/ wheels/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ cover/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder .pybuilder/ target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv # For a library or package, you might want to ignore these files since the code is # intended to run in multiple environments; 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To override or circumvent the safety guardrails and safeguards We have put in place; 6. For the purpose of exploiting, harming or attempting to exploit or harm minors in any way; 7. To generate or disseminate verifiably false information and/or content with the purpose of harming others or influencing elections; 8. To generate or facilitate false online engagement, including fake reviews and other means of fake online engagement; 9. To intentionally defame, disparage or otherwise harass others; 10. To generate and/or disseminate malware (including ransomware) or any other content to be used for the purpose of harming electronic systems; 11. To generate or disseminate personal identifiable information with the purpose of harming others; 12. 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Copyright (C) 2025 Tencent. All rights reserved. The below software and/or models in this distribution may have been modified by Tencent ("Tencent Modifications"). All Tencent Modifications are Copyright (C) Tencent. Hunyuan 3D 2.0 is licensed under the TENCENT HUNYUAN 3D 2.0 COMMUNITY LICENSE AGREEMENT except for the third-party components listed below, which is licensed under different terms. Hunyuan 3D 2.0 does not impose any additional limitations beyond what is outlined in the respective licenses of these third-party components. Users must comply with all terms and conditions of original licenses of these third-party components and must ensure that the usage of the third party components adheres to all relevant laws and regulations. For avoidance of doubts, Hunyuan 3D 2.0 means inference-enabling code, parameters, and weights of this Model only, which are made publicly available by Tencent in accordance with TENCENT HUNYUAN 3D 2.0 COMMUNITY LICENSE AGREEMENT. Other dependencies and licenses: Open Source Model Licensed under the MIT and CreativeML Open RAIL++-M License: -------------------------------------------------------------------- 1. Stable Diffusion Copyright (c) 2022 Stability AI Terms of the MIT and CreativeML Open RAIL++-M License: -------------------------------------------------------------------- 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. CreativeML Open RAIL++-M License dated November 24, 2022 Section I: PREAMBLE Multimodal generative models are being widely adopted and used, and have the potential to transform the way artists, among other individuals, conceive and benefit from AI or ML technologies as a tool for content creation. Notwithstanding the current and potential benefits that these artifacts can bring to society at large, there are also concerns about potential misuses of them, either due to their technical limitations or ethical considerations. In short, this license strives for both the open and responsible downstream use of the accompanying model. When it comes to the open character, we took inspiration from open source permissive licenses regarding the grant of IP rights. Referring to the downstream responsible use, we added use-based restrictions not permitting the use of the Model in very specific scenarios, in order for the licensor to be able to enforce the license in case potential misuses of the Model may occur. At the same time, we strive to promote open and responsible research on generative models for art and content generation. Even though downstream derivative versions of the model could be released under different licensing terms, the latter will always have to include - at minimum - the same use-based restrictions as the ones in the original license (this license). 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“ Living out everyone’s imagination on creating and manipulating 3D assets.”

https://github.com/user-attachments/assets/a2cbc5b8-be22-49d7-b1c3-7aa2b20ba460 ## 🔥 News - July 26, 2025: 🤗 We release the first open-source, simulation-capable, immersive 3D world generation model, [HunyuanWorld-1.0](https://github.com/Tencent-Hunyuan/HunyuanWorld-1.0)! - June 23, 2025: 📄 Release the system technical report of [Hunyuan3D 2.5](https://arxiv.org/abs/2506.16504). - June 13, 2025: 🤗 Release [Hunyuan3D-2.1](https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1), fully open-sourced with new PBR model, VAE encoder, and all training code. - Apr 1, 2025: 🤗 Release turbo paint model [Hunyuan3D-Paint-v2-0-Turbo](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-paint-v2-0-turbo), and multiview texture generation pipeline, try it [here](examples/fast_texture_gen_multiview.py)! Stay tuned for our new texture generation model [RomanTex](https://github.com/oakshy/RomanTex) and PBR material generation [MaterialMVP](https://github.com/ZebinHe/MaterialMVP/)! - Mar 19, 2025: 🤗 Release turbo model [Hunyuan3D-2-Turbo](https://huggingface.co/tencent/Hunyuan3D-2/), [Hunyuan3D-2mini-Turbo](https://huggingface.co/tencent/Hunyuan3D-2mini/) and [FlashVDM](https://github.com/Tencent/FlashVDM). - Mar 18, 2025: 🤗 Release multiview shape model [Hunyuan3D-2mv](https://huggingface.co/tencent/Hunyuan3D-2mv) and 0.6B shape model [Hunyuan3D-2mini](https://huggingface.co/tencent/Hunyuan3D-2mini). - Feb 14, 2025: 🛠️ Release texture enhancement module, please obtain high-definition textures via [here](minimal_demo.py)! - Feb 3, 2025: 🐎 Release [Hunyuan3D-DiT-v2-0-Fast](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-dit-v2-0-fast), our guidance distillation model that could half the dit inference time, see [here](minimal_demo.py) for usage. - Jan 27, 2025: 🛠️ Release Blender addon for Hunyuan3D 2.0, Check it out [here](#blender-addon). - Jan 23, 2025: 💬 We thank community members for creating [Windows installation tool](https://github.com/YanWenKun/Hunyuan3D-2-WinPortable), ComfyUI support with [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper) and [ComfyUI-3D-Pack](https://github.com/MrForExample/ComfyUI-3D-Pack) and other awesome [extensions](#community-resources). - Jan 21, 2025: 💬 Enjoy exciting 3D generation on our website [Hunyuan3D Studio](https://3d.hunyuan.tencent.com)! - Jan 21, 2025: 🤗 Release inference code and pretrained models of [Hunyuan3D 2.0](https://huggingface.co/tencent/Hunyuan3D-2). Please give it a try via [huggingface space](https://huggingface.co/spaces/tencent/Hunyuan3D-2) and our [official site](https://3d.hunyuan.tencent.com)! > Join our **[Wechat](#)** and **[Discord](https://discord.gg/dNBrdrGGMa)** group to discuss and find help from us. | Wechat Group | Xiaohongshu | X | Discord | |--------------------------------------------------|-------------------------------------------------------|---------------------------------------------|---------------------------------------------------| | | | | | ## **Abstract** We present Hunyuan3D 2.0, an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. This system includes two foundation components: a large-scale shape generation model - Hunyuan3D-DiT, and a large-scale texture synthesis model - Hunyuan3D-Paint. The shape generative model, built on a scalable flow-based diffusion transformer, aims to create geometry that properly aligns with a given condition image, laying a solid foundation for downstream applications. The texture synthesis model, benefiting from strong geometric and diffusion priors, produces high-resolution and vibrant texture maps for either generated or hand-crafted meshes. Furthermore, we build Hunyuan3D-Studio - a versatile, user-friendly production platform that simplifies the re-creation process of 3D assets. It allows both professional and amateur users to manipulate or even animate their meshes efficiently. We systematically evaluate our models, showing that Hunyuan3D 2.0 outperforms previous state-of-the-art models, including the open-source models and closed-source models in geometry details, condition alignment, texture quality, and e.t.c.

## ☯️ **Hunyuan3D 2.0** ### Architecture Hunyuan3D 2.0 features a two-stage generation pipeline, starting with the creation of a bare mesh, followed by the synthesis of a texture map for that mesh. This strategy is effective for decoupling the difficulties of shape and texture generation and also provides flexibility for texturing either generated or handcrafted meshes.

### Performance We have evaluated Hunyuan3D 2.0 with other open-source as well as close-source 3d-generation methods. The numerical results indicate that Hunyuan3D 2.0 surpasses all baselines in the quality of generated textured 3D assets and the condition following ability. | Model | CMMD(⬇) | FID_CLIP(⬇) | FID(⬇) | CLIP-score(⬆) | |-------------------------|-----------|-------------|-------------|---------------| | Top Open-source Model1 | 3.591 | 54.639 | 289.287 | 0.787 | | Top Close-source Model1 | 3.600 | 55.866 | 305.922 | 0.779 | | Top Close-source Model2 | 3.368 | 49.744 | 294.628 | 0.806 | | Top Close-source Model3 | 3.218 | 51.574 | 295.691 | 0.799 | | Hunyuan3D 2.0 | **3.193** | **49.165** | **282.429** | **0.809** | Generation results of Hunyuan3D 2.0:

## 🎁 Models Zoo It takes 6 GB VRAM for shape generation and 16 GB for shape and texture generation in total. Hunyuan3D-2-1 Series | Model | Description | Date | Size | Huggingface | |----------------------|-------------------------------|------------|------|-----------------------------------------------------------------------------------------| | Hunyuan3D-DiT-v2-1 | Mini Image to Shape Model | 2025-06-13 | 3.0B | [Download](https://huggingface.co/tencent/Hunyuan3D-2.1/tree/main/hunyuan3d-dit-v2-1) | | Hunyuan3D-Paint-v2-1 | Texture Generation Model | 2025-06-13 | 1.3B | [Download](https://huggingface.co/tencent/Hunyuan3D-2.1/tree/main/hunyuan3d-paintpbr-v2-1) | Hunyuan3D-2mini Series | Model | Description | Date | Size | Huggingface | |-----------------------------|-------------------------------|------------|------|--------------------------------------------------------------------------------------------------| | Hunyuan3D-DiT-v2-mini-Turbo | Step Distillation Version | 2025-03-19 | 0.6B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mini/tree/main/hunyuan3d-dit-v2-mini-turbo) | | Hunyuan3D-DiT-v2-mini-Fast | Guidance Distillation Version | 2025-03-18 | 0.6B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mini/tree/main/hunyuan3d-dit-v2-mini-fast) | | Hunyuan3D-DiT-v2-mini | Mini Image to Shape Model | 2025-03-18 | 0.6B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mini/tree/main/hunyuan3d-dit-v2-mini) | Hunyuan3D-2mv Series | Model | Description | Date | Size | Huggingface | |---------------------------|--------------------------------|------------|------|----------------------------------------------------------------------------------------------| | Hunyuan3D-DiT-v2-mv-Turbo | Step Distillation Version | 2025-03-19 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mv/tree/main/hunyuan3d-dit-v2-mv-turbo) | | Hunyuan3D-DiT-v2-mv-Fast | Guidance Distillation Version | 2025-03-18 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mv/tree/main/hunyuan3d-dit-v2-mv-fast) | | Hunyuan3D-DiT-v2-mv | Multiview Image to Shape Model | 2025-03-18 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mv/tree/main/hunyuan3d-dit-v2-mv) | Hunyuan3D-2 Series | Model | Description | Date | Size | Huggingface | |----------------------------|-----------------------------|------------|------|-------------------------------------------------------------------------------------------| | Hunyuan3D-DiT-v2-0-Turbo | Step Distillation Model | 2025-03-19 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-dit-v2-0-turbo) | | Hunyuan3D-DiT-v2-0-Fast | Guidance Distillation Model | 2025-02-03 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-dit-v2-0-fast) | | Hunyuan3D-DiT-v2-0 | Image to Shape Model | 2025-01-21 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-dit-v2-0) | | Hunyuan3D-Paint-v2-0 | Texture Generation Model | 2025-01-21 | 1.3B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-paint-v2-0) | | Hunyuan3D-Paint-v2-0-Turbo | Distillation Texure Model | 2025-04-01 | 1.3B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-paint-v2-0-turbo) | | Hunyuan3D-Delight-v2-0 | Image Delight Model | 2025-01-21 | 1.3B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-delight-v2-0) | ## 🤗 Get Started with Hunyuan3D 2.0 Hunyuan3D 2.0 supports Macos, Windows, Linux. You may follow the next steps to use Hunyuan3D 2.0 via: - [Code](#code-usage) - [Gradio App](#gradio-app) - [API Server](#api-server) - [Blender Addon](#blender-addon) - [Official Site](#official-site) ### Install Requirements Please install Pytorch via the [official](https://pytorch.org/) site. Then install the other requirements via ```bash pip install -r requirements.txt pip install -e . # for texture cd hy3dgen/texgen/custom_rasterizer python3 setup.py install cd ../../.. cd hy3dgen/texgen/differentiable_renderer python3 setup.py install ``` ### Code Usage We designed a diffusers-like API to use our shape generation model - Hunyuan3D-DiT and texture synthesis model - Hunyuan3D-Paint. You could assess **Hunyuan3D-DiT** via: ```python from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(image='assets/demo.png')[0] ``` The output mesh is a [trimesh object](https://trimesh.org/trimesh.html), which you could save to glb/obj (or other format) file. For **Hunyuan3D-Paint**, do the following: ```python from hy3dgen.texgen import Hunyuan3DPaintPipeline from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline # let's generate a mesh first pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(image='assets/demo.png')[0] pipeline = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(mesh, image='assets/demo.png') ``` Please visit [examples](examples) folder for more advanced usage, such as **multiview image to 3D generation** and * *texture generation for handcrafted mesh**. ### Gradio App You could also host a [Gradio](https://www.gradio.app/) App in your own computer via: Standard Version ```bash # Hunyuan3D-2mini python3 gradio_app.py --model_path tencent/Hunyuan3D-2mini --subfolder hunyuan3d-dit-v2-mini --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode # Hunyuan3D-2mv python3 gradio_app.py --model_path tencent/Hunyuan3D-2mv --subfolder hunyuan3d-dit-v2-mv --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode # Hunyuan3D-2 python3 gradio_app.py --model_path tencent/Hunyuan3D-2 --subfolder hunyuan3d-dit-v2-0 --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode ``` Turbo Version ```bash # Hunyuan3D-2mini python3 gradio_app.py --model_path tencent/Hunyuan3D-2mini --subfolder hunyuan3d-dit-v2-mini-turbo --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode --enable_flashvdm # Hunyuan3D-2mv python3 gradio_app.py --model_path tencent/Hunyuan3D-2mv --subfolder hunyuan3d-dit-v2-mv-turbo --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode --enable_flashvdm # Hunyuan3D-2 python3 gradio_app.py --model_path tencent/Hunyuan3D-2 --subfolder hunyuan3d-dit-v2-0-turbo --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode --enable_flashvdm ``` ### API Server You could launch an API server locally, which you could post web request for Image/Text to 3D, Texturing existing mesh, and e.t.c. ```bash python api_server.py --host 0.0.0.0 --port 8080 ``` A demo post request for image to 3D without texture. ```bash img_b64_str=$(base64 -i assets/demo.png) curl -X POST "http://localhost:8080/generate" \ -H "Content-Type: application/json" \ -d '{ "image": "'"$img_b64_str"'", }' \ -o test2.glb ``` ### Blender Addon With an API server launched, you could also directly use Hunyuan3D 2.0 in your blender with our [Blender Addon](blender_addon.py). Please follow our tutorial to install and use. https://github.com/user-attachments/assets/8230bfb5-32b1-4e48-91f4-a977c54a4f3e ### Official Site Don't forget to visit [Hunyuan3D](https://3d.hunyuan.tencent.com) for quick use, if you don't want to host yourself. ## 📑 Open-Source Plan - [x] Inference Code - [x] Model Checkpoints - [x] Technical Report - [x] ComfyUI - [x] Finetuning - [ ] TensorRT Version ## 🔗 BibTeX If you found this repository helpful, please cite our reports: ```bibtex @misc{lai2025hunyuan3d25highfidelity3d, title={Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details}, author={Tencent Hunyuan3D Team}, year={2025}, eprint={2506.16504}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2506.16504}, } @misc{hunyuan3d22025tencent, title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation}, author={Tencent Hunyuan3D Team}, year={2025}, eprint={2501.12202}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{yang2024hunyuan3d, title={Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation}, author={Tencent Hunyuan3D Team}, year={2024}, eprint={2411.02293}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## Community Resources Thanks for the contributions of community members, here we have these great extensions of Hunyuan3D 2.0: - [ComfyUI-3D-Pack](https://github.com/MrForExample/ComfyUI-3D-Pack) - [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper) - [Hunyuan3D-2-for-windows](https://github.com/sdbds/Hunyuan3D-2-for-windows) - [📦 A bundle for running on Windows | 整合包](https://github.com/YanWenKun/Hunyuan3D-2-WinPortable) - [Hunyuan3D-2GP](https://github.com/deepbeepmeep/Hunyuan3D-2GP) - [Kaggle Notebook](https://github.com/darkon12/Hunyuan3D-2GP_Kaggle) ## Acknowledgements We would like to thank the contributors to the [Trellis](https://github.com/microsoft/TRELLIS), [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers), [HuggingFace](https://huggingface.co), [CraftsMan3D](https://github.com/wyysf-98/CraftsMan3D), and [Michelangelo](https://github.com/NeuralCarver/Michelangelo/tree/main) repositories, for their open research and exploration. ## Star History Star History Chart ================================================ FILE: README_ja_jp.md ================================================ [Read in English](README.md) [中文阅读](README_zh_cn.md)

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“ 3Dアセットの作成と操作において、すべての人の想像力を実現します。”

## 🔥 ニュース - 2025年2月14日: 🛠️ テクスチャ強化モジュールをリリースしました。HD テクスチャ生成を [体験](minimal_demo.py) してください。 - 2025年1月21日: 💬 私たちのウェブサイト [Hunyuan3D Studio](https://3d.hunyuan.tencent.com) でエキサイティングな3D生成を楽しんでください！ - 2025年1月21日: 💬 [Hunyuan3D 2.0](https://huggingface.co/tencent/Hunyuan3D-2) の推論コードと事前学習モデルをリリースしました。 - 2025年1月21日: 💬 Hunyuan3D 2.0をリリースしました。 [huggingface space](https://huggingface.co/spaces/tencent/Hunyuan3D-2) や [公式サイト](https://3d.hunyuan.tencent.com) でお試しください！ ## **概要** Hunyuan3D 2.0は、高解像度のテクスチャ付き3Dアセットを生成するための高度な大規模3D合成システムです。 このシステムには、2つの基盤コンポーネントが含まれています：大規模形状生成モデル - Hunyuan3D-DiT、および大規模 テクスチャ合成モデル - Hunyuan3D-Paint。 形状生成モデルは、スケーラブルなフローに基づく拡散トランスフォーマーに基づいて構築されており、与えられた条件画像に適切に 一致するジオメトリを作成することを目的としており、下流のアプリケーションのための堅固な基盤を提供します。 テクスチャ合成モデルは、強力なジオメトリおよび拡散の事前知識を活用して、生成されたまたは手作りのメッシュのために高解像度で鮮やかな テクスチャマップを生成します。 さらに、Hunyuan3D-Studioを構築しました。これは、3Dアセットの再作成プロセスを簡素化する多用途で使いやすい制作プラットフォームです。 プロフェッショナルおよびアマチュアユー���ーの両方がメッシュを効率的に操作したり、アニメーション化したりすることができます。 私たちはモデルを体系的に評価し、Hunyuan3D 2.0が以前の最先端モデルを上回ることを示しました。 オープンソースモデルとクローズドソースモデルの両方で、ジオメトリの詳細、条件の整合性、テクスチャの品質などの点で優れています。

## ☯️ **Hunyuan3D 2.0** ### アーキテクチャ Hunyuan3D 2.0は、ベアメッシュの作成から始まり、そのメッシュのテクスチャマップの合成に至る2段階の生成パイプライン���特徴としています。 この戦略は、形状とテクスチャの生成の難しさを分離するのに効果的であり、生成されたメッシュまたは手作りのメッシュのテクスチャリングに柔軟性を提供します。

### パフォーマンス Hunyuan3D 2.0を他のオープンソースおよびクローズドソースの3D生成方法と比較して評価しました。 数値結果は、Hunyuan3D 2.0が生成されたテクスチャ付き3Dアセットの品質と条件の遵守能力においてすべてのベースラインを上回っていることを示しています。 | モデル | CMMD(⬇) | FID_CLIP(⬇) | FID(⬇) | CLIP-score(⬆) | |-------------------------|-----------|-------------|-------------|---------------| | トップオープンソースモデル1 | 3.591 | 54.639 | 289.287 | 0.787 | | トップクローズドソースモデル1 | 3.600 | 55.866 | 305.922 | 0.779 | | トップクローズドソースモデル2 | 3.368 | 49.744 | 294.628 | 0.806 | | トップクローズドソースモデル3 | 3.218 | 51.574 | 295.691 | 0.799 | | Hunyuan3D 2.0 | **3.193** | **49.165** | **282.429** | **0.809** | Hunyuan3D 2.0の生成結果：

### 事前学習モデル | モデル | 日付 | Huggingface | |----------------------|------------|--------------------------------------------------------| | Hunyuan3D-DiT-v2-0 | 2025-01-21 | [ダウンロード](https://huggingface.co/tencent/Hunyuan3D-2) | | Hunyuan3D-Paint-v2-0 | 2025-01-21 | [ダウンロード](https://huggingface.co/tencent/Hunyuan3D-2) | ## 🤗 Hunyuan3D 2.0の使い方 次の手順に従って、コードまたはGradioアプリを使用してHunyuan3D 2.0を使用できます。 ### 必要なものをインストール 公式サイトからPytorchをインストールしてください。次に、他の必要なものを以下の方法でインストールします。 ```bash pip install -r requirements.txt # for texture cd hy3dgen/texgen/custom_rasterizer python3 setup.py install cd hy3dgen/texgen/differentiable_renderer python3 setup.py install ``` ### APIの使い方 形状生成モデル - Hunyuan3D-DiTおよびテクスチャ合成モデル - Hunyuan3D-Paintを使用するためのdiffusersのようなAPIを設計しました。 **Hunyuan3D-DiT**にアクセスするには、次のようにします： ```python from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(image='assets/demo.png')[0] ``` 出力メッシュは[trimeshオブジェクト](https://trimesh.org/trimesh.html)であり、glb/obj（または他の形式）ファイルに保存できます。 **Hunyuan3D-Paint**の場合は、次のようにします： ```python from hy3dgen.texgen import Hunyuan3DPaintPipeline from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline # まずメッシュを生成しましょう pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(image='assets/demo.png')[0] pipeline = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(mesh, image='assets/demo.png') ``` より高度な使用法については、[minimal_demo.py](minimal_demo.py)を参照してください。例えば、**テキストから3D**や**手作りメッシュのテクスチャ生成**などです。 ### Gradioアプリ 次の方法で自分のコンピュータで[Gradio](https://www.gradio.app/)アプリをホストすることもできます： ```bash python3 gradio_app.py ``` 自分でホストしたくない場合は、[Hunyuan3D](https://3d.hunyuan.tencent.com)を訪れてすぐに使用してください。 ## 📑 オープンソース計画 - [x] 推論コード - [x] モデルチェックポイント - [x] 技術報告書 - [ ] ComfyUI - [ ] TensorRTバージョン ## 🔗 BibTeX このリポジトリが役に立った場合は、以下の方法で報告書を引用してください： ```bibtex @misc{hunyuan3d22025tencent, title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation}, author={Tencent Hunyuan3D Team}, year={2025}, eprint={2501.12202}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{yang2024hunyuan3d, title={Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation}, author={Tencent Hunyuan3D Team}, year={2024}, eprint={2411.02293}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## 謝辞 [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers), [HuggingFace](https://huggingface.co), [CraftsMan3D](https://github.com/wyysf-98/CraftsMan3D), and [Michelangelo](https://github.com/NeuralCarver/Michelangelo/tree/main) リポジトリの貢献者に感謝します。 ## スター履歴 Star History Chart ================================================ FILE: README_zh_cn.md ================================================ [Read in English](README.md) [日本語で読む](README_ja_jp.md)

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> 新年快乐! ![happynewyear](https://github.com/user-attachments/assets/69aa40a7-8657-4c2b-8efd-99eda6c26fe4) > 加入我们的 **[微信群](#)** and **[Discord 社区](https://discord.gg/dNBrdrGGMa)** 讨论，获取最新进展以及帮助吧. | Wechat Group | Xiaohongshu | X | Discord | | ------------------------------------------------ | ----------------------------------------------------- | ------------------------------------------- | ------------------------------------------------- | | | | | | ---

“通过 3D 创作与编辑让每个人的想象变成现实。”

## 🔥 最新消息 - Jul 26, 2025: 🤗 我们发布了业界首个开源且兼容主流图形管线的3D世界生成模型 [HunyuanWorld-1.0](https://github.com/Tencent-Hunyuan/HunyuanWorld-1.0)! - Jun 13, 2025: 🤗 我们发布了业界首个完全开源支持物理渲染纹理的3D生成模型 [Hunyuan3D 2.1](https://github.com/Tencent-Hunyuan/Hunyuan3D-2.1)! - Feb 14, 2025: 🛠️ 发布纹理增强模块，欢迎[体验](minimal_demo.py)高清纹理生成. - Jan 27, 2025: 🛠️ 发布 Blender 插件，欢迎[体验](#blender-addon). - Jan 23, 2025: 💬 感谢社区成员的 [Windows 安装工具](https://github.com/YanWenKun/Hunyuan3D-2-WinPortable), ComfyUI 支持 [ComfyUI-Hunyuan3DWrapper](https://github.com/kijai/ComfyUI-Hunyuan3DWrapper)， [ComfyUI-3D-Pack](https://github.com/MrForExample/ComfyUI-3D-Pack) 以及其他出色的 [扩展功能](#community-resources). - Jan 21, 2025: 💬 欢迎来我们的门户网站 [Hunyuan3D Studio](https://3d.hunyuan.tencent.com) 体验更多3D生成功能! - Jan 21, 2025: 💬 我们开源了 [Hunyuan3D 2.0](https://huggingface.co/tencent/Hunyuan3D-2)的推理代码和预训练权重. - Jan 21, 2025: 💬 我们发布了 [Hunyuan3D 2.0](https://huggingface.co/spaces/tencent/Hunyuan3D-2). 快来试试吧! ## 概览 混元 3D 2.0 是一款先进的大规模 3D 资产创作系统，它可以用于生成带有高分辨率纹理贴图的高保真度3D模型。该系统包含两个基础组件：一个大规模几何生成模型 — 混元 3D-DiT，以及一个大规模纹理生成模型 — 混元 3D-Paint。 几何生成模型基于流扩散的扩散模型构建，旨在生成与给定条件图像精确匹配的几何模型，为下游应用奠定坚实基础。 纹理生成模型得益于强大的几何和扩散模型先验知识，能够为AI生成的或手工制作的网格模型生成高分辨率且生动逼真的纹理贴图。 此外，我们打造了混元 3D 功能矩阵，一个功能多样、易于使用的创作平台，简化了 3D 模型的制作以及修改过程。它使专业用户和业余爱好者都能高效地对3D模型进行操作，甚至制作动画。 我们对该系统进行了系统评估，结果表明混元 3D 2.0 在几何细节、条件匹配、纹理质量等方面均优于以往的最先进的开源以及闭源模型。

## ☯️ **Hunyuan3D 2.0** ### 模型架构 混元 3D 2.0 采用了一个两阶段的生成过程，它首先创建一个无纹理的几何模型，然后为该几何模型合成纹理贴图。这种策略有效地将形状生成和纹理生成的难点分离开来，同时也为生成的几何模型或手工制作的几何模型进行纹理处理提供了灵活性。

### 性能评估 我们将混元 3D 2.0 与其他开源及闭源的 3D 生成方法进行了评估对比。 数值结果表明，在生成的带纹理 3D 模型的质量以及对给定条件的遵循能力方面，混元 3D 2.0 超越了所有的基准模型。 | Model | CMMD(⬇) | FID_CLIP(⬇) | FID(⬇) | CLIP-score(⬆) | | ----------------------- | --------- | ----------- | ----------- | ------------- | | Top Open-source Model1 | 3.591 | 54.639 | 289.287 | 0.787 | | Top Close-source Model1 | 3.600 | 55.866 | 305.922 | 0.779 | | Top Close-source Model2 | 3.368 | 49.744 | 294.628 | 0.806 | | Top Close-source Model3 | 3.218 | 51.574 | 295.691 | 0.799 | | Hunyuan3D 2.0 | **3.193** | **49.165** | **282.429** | **0.809** | 一些 Hunyuan3D 2.0 的生成结果:

### 预训练模型 | 模型名称 | 发布日期 | 参数 | Huggingface | | ---------------------- | ---------- | ---- | ----------------------------------------------------------------------------------- | | Hunyuan3D-DiT-v2-0 | 2025-01-21 | 2.6B | [下载](https://huggingface.co/tencent/Hunyuan3D-2) | | Hunyuan3D-Paint-v2-0 | 2025-01-21 | 1.3B | [下载](https://huggingface.co/tencent/Hunyuan3D-2) | | Hunyuan3D-Delight-v2-0 | 2025-01-21 | 1.3B | [下载](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-delight-v2-0) | ## 🤗快速入门 Hunyuan3D 2.0 你可以按照以下步骤，通过代码或 Gradio 来使用混元 3D 2.0。 - [代码使用](#代码使用方法) - [Gradio](#gradio-app-使用方法) - [API服务器](#api-服务器) - [Blender插件](#blender-插件) - [官方网站](#官方网站) ### 依赖包安装 请通过官方网站安装 PyTorch。然后通过以下方式安装其他所需的依赖项。 ```bash pip install -r requirements.txt # for texture cd hy3dgen/texgen/custom_rasterizer python3 setup.py install cd ../../.. cd hy3dgen/texgen/differentiable_renderer python3 setup.py install ``` ### 代码使用方法 我们设计了一个类似于 diffusers 的 API 来使用我们的几何生成模型 — 混元 3D-DiT 和纹理合成模型 — 混元 3D-Paint。 你可以通过以下方式使用 混元 3D-DiT： ```python from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(image='assets/demo.png')[0] ``` 输出的网格是一个 Trimesh 对象，你可以将其保存为 glb/obj（或其他格式）文件。 对于 混元 3D-Paint，请执行以下操作： ```python from hy3dgen.texgen import Hunyuan3DPaintPipeline from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline # let's generate a mesh first pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(image='assets/demo.png')[0] pipeline = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(mesh, image='assets/demo.png') ``` 请访问 [minimal_demo.py](minimal_demo.py) 以了解更多高级用法，例如 文本转 3D 以及 为手工制作的网格生成纹理。 ### Gradio App 使用方法 你也可以通过以下方式在自己的计算机上托管一个Gradio应用程序： ```bash python3 gradio_app.py ``` ### API 服务器 你可以在本地启动一个API服务器，通过它你可以发送将图像/文本转换为3D模型、为现有网格模型添加纹理等的网络请求。 ```bash python api_server.py --host 0.0.0.0 --port 8080 ``` A demo post request for image to 3D without texture. ```bash img_b64_str=$(base64 -i assets/demo.png) curl -X POST "http://localhost:8080/generate" \ -H "Content-Type: application/json" \ -d '{ "image": "'"$img_b64_str"'", }' \ -o test2.glb ``` ### Blender 插件 API服务器启动后，你还可以通过我们的[Blender插件](blender_addon.py)在Blender中直接使用混元3D 2.0。请按照我们的教程进行安装和使用。 https://github.com/user-attachments/assets/8230bfb5-32b1-4e48-91f4-a977c54a4f3e ### 官方网站 如果你不想自己托管，别忘了访问[混元 3D](https://3d.hunyuan.tencent.com)进行快速使用。 ## 📑 开源计划 - [x] 推理代码 - [x] 模型权重 - [x] 技术报告 - [ ] ComfyUI - [ ] TensorRT 量化 ## 🔗 引用 如果你发现我们的工作有帮助，你可以以下面的方式引用我们的报告： ```bibtex @misc{hunyuan3d22025tencent, title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation}, author={Tencent Hunyuan3D Team}, year={2025}, eprint={2501.12202}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{yang2024hunyuan3d, title={Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation}, author={Tencent Hunyuan3D Team}, year={2024}, eprint={2411.02293}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ## 致谢 我们要感谢以下内容的贡献者： [DINOv2](https://github.com/facebookresearch/dinov2), [Stable Diffusion](https://github.com/Stability-AI/stablediffusion), [FLUX](https://github.com/black-forest-labs/flux), [diffusers](https://github.com/huggingface/diffusers), [HuggingFace](https://huggingface.co), [CraftsMan3D](https://github.com/wyysf-98/CraftsMan3D), 和 [Michelangelo](https://github.com/NeuralCarver/Michelangelo/tree/main) 各研究机构，感谢它们开展公开研究与探索。 ## Star 历史 Star History Chart ================================================ FILE: api_server.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. """ A model worker executes the model. """ import argparse import asyncio import base64 import logging import logging.handlers import os import sys import tempfile import threading import traceback import uuid from io import BytesIO import torch import trimesh import uvicorn from PIL import Image from fastapi import FastAPI, Request from fastapi.responses import JSONResponse, FileResponse from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline, FloaterRemover, DegenerateFaceRemover, FaceReducer, \ MeshSimplifier from hy3dgen.texgen import Hunyuan3DPaintPipeline from hy3dgen.text2image import HunyuanDiTPipeline LOGDIR = '.' server_error_msg = "**NETWORK ERROR DUE TO HIGH TRAFFIC. PLEASE REGENERATE OR REFRESH THIS PAGE.**" moderation_msg = "YOUR INPUT VIOLATES OUR CONTENT MODERATION GUIDELINES. PLEASE TRY AGAIN." handler = None def build_logger(logger_name, logger_filename): global handler formatter = logging.Formatter( fmt="%(asctime)s | %(levelname)s | %(name)s | %(message)s", datefmt="%Y-%m-%d %H:%M:%S", ) # Set the format of root handlers if not logging.getLogger().handlers: logging.basicConfig(level=logging.INFO) logging.getLogger().handlers[0].setFormatter(formatter) # Redirect stdout and stderr to loggers stdout_logger = logging.getLogger("stdout") stdout_logger.setLevel(logging.INFO) sl = StreamToLogger(stdout_logger, logging.INFO) sys.stdout = sl stderr_logger = logging.getLogger("stderr") stderr_logger.setLevel(logging.ERROR) sl = StreamToLogger(stderr_logger, logging.ERROR) sys.stderr = sl # Get logger logger = logging.getLogger(logger_name) logger.setLevel(logging.INFO) # Add a file handler for all loggers if handler is None: os.makedirs(LOGDIR, exist_ok=True) filename = os.path.join(LOGDIR, logger_filename) handler = logging.handlers.TimedRotatingFileHandler( filename, when='D', utc=True, encoding='UTF-8') handler.setFormatter(formatter) for name, item in logging.root.manager.loggerDict.items(): if isinstance(item, logging.Logger): item.addHandler(handler) return logger class StreamToLogger(object): """ Fake file-like stream object that redirects writes to a logger instance. """ def __init__(self, logger, log_level=logging.INFO): self.terminal = sys.stdout self.logger = logger self.log_level = log_level self.linebuf = '' def __getattr__(self, attr): return getattr(self.terminal, attr) def write(self, buf): temp_linebuf = self.linebuf + buf self.linebuf = '' for line in temp_linebuf.splitlines(True): # From the io.TextIOWrapper docs: # On output, if newline is None, any '\n' characters written # are translated to the system default line separator. # By default sys.stdout.write() expects '\n' newlines and then # translates them so this is still cross platform. if line[-1] == '\n': self.logger.log(self.log_level, line.rstrip()) else: self.linebuf += line def flush(self): if self.linebuf != '': self.logger.log(self.log_level, self.linebuf.rstrip()) self.linebuf = '' def pretty_print_semaphore(semaphore): if semaphore is None: return "None" return f"Semaphore(value={semaphore._value}, locked={semaphore.locked()})" SAVE_DIR = 'gradio_cache' os.makedirs(SAVE_DIR, exist_ok=True) worker_id = str(uuid.uuid4())[:6] logger = build_logger("controller", f"{SAVE_DIR}/controller.log") def load_image_from_base64(image): return Image.open(BytesIO(base64.b64decode(image))) class ModelWorker: def __init__(self, model_path='tencent/Hunyuan3D-2mini', tex_model_path='tencent/Hunyuan3D-2', subfolder='hunyuan3d-dit-v2-mini-turbo', device='cuda', enable_tex=False): self.model_path = model_path self.worker_id = worker_id self.device = device logger.info(f"Loading the model {model_path} on worker {worker_id} ...") self.rembg = BackgroundRemover() self.pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( model_path, subfolder=subfolder, use_safetensors=True, device=device, ) self.pipeline.enable_flashvdm(mc_algo='mc') # self.pipeline_t2i = HunyuanDiTPipeline( # 'Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled', # device=device # ) if enable_tex: self.pipeline_tex = Hunyuan3DPaintPipeline.from_pretrained(tex_model_path) def get_queue_length(self): if model_semaphore is None: return 0 else: return args.limit_model_concurrency - model_semaphore._value + (len( model_semaphore._waiters) if model_semaphore._waiters is not None else 0) def get_status(self): return { "speed": 1, "queue_length": self.get_queue_length(), } @torch.inference_mode() def generate(self, uid, params): if 'image' in params: image = params["image"] image = load_image_from_base64(image) else: if 'text' in params: text = params["text"] image = self.pipeline_t2i(text) else: raise ValueError("No input image or text provided") image = self.rembg(image) params['image'] = image if 'mesh' in params: mesh = trimesh.load(BytesIO(base64.b64decode(params["mesh"])), file_type='glb') else: seed = params.get("seed", 1234) params['generator'] = torch.Generator(self.device).manual_seed(seed) params['octree_resolution'] = params.get("octree_resolution", 128) params['num_inference_steps'] = params.get("num_inference_steps", 5) params['guidance_scale'] = params.get('guidance_scale', 5.0) params['mc_algo'] = 'mc' import time start_time = time.time() mesh = self.pipeline(**params)[0] logger.info("--- %s seconds ---" % (time.time() - start_time)) if params.get('texture', False): mesh = FloaterRemover()(mesh) mesh = DegenerateFaceRemover()(mesh) mesh = FaceReducer()(mesh, max_facenum=params.get('face_count', 40000)) mesh = self.pipeline_tex(mesh, image) type = params.get('type', 'glb') with tempfile.NamedTemporaryFile(suffix=f'.{type}', delete=False) as temp_file: mesh.export(temp_file.name) mesh = trimesh.load(temp_file.name) save_path = os.path.join(SAVE_DIR, f'{str(uid)}.{type}') mesh.export(save_path) torch.cuda.empty_cache() return save_path, uid app = FastAPI() from fastapi.middleware.cors import CORSMiddleware app.add_middleware( CORSMiddleware, allow_origins=["*"], # 你可以指定允许的来源 allow_credentials=True, allow_methods=["*"], # 允许所有方法 allow_headers=["*"], # 允许所有头部 ) @app.post("/generate") async def generate(request: Request): logger.info("Worker generating...") params = await request.json() uid = uuid.uuid4() try: file_path, uid = worker.generate(uid, params) return FileResponse(file_path) except ValueError as e: traceback.print_exc() print("Caught ValueError:", e) ret = { "text": server_error_msg, "error_code": 1, } return JSONResponse(ret, status_code=404) except torch.cuda.CudaError as e: print("Caught torch.cuda.CudaError:", e) ret = { "text": server_error_msg, "error_code": 1, } return JSONResponse(ret, status_code=404) except Exception as e: print("Caught Unknown Error", e) traceback.print_exc() ret = { "text": server_error_msg, "error_code": 1, } return JSONResponse(ret, status_code=404) @app.post("/send") async def generate(request: Request): logger.info("Worker send...") params = await request.json() uid = uuid.uuid4() threading.Thread(target=worker.generate, args=(uid, params,)).start() ret = {"uid": str(uid)} return JSONResponse(ret, status_code=200) @app.get("/status/{uid}") async def status(uid: str): save_file_path = os.path.join(SAVE_DIR, f'{uid}.glb') print(save_file_path, os.path.exists(save_file_path)) if not os.path.exists(save_file_path): response = {'status': 'processing'} return JSONResponse(response, status_code=200) else: base64_str = base64.b64encode(open(save_file_path, 'rb').read()).decode() response = {'status': 'completed', 'model_base64': base64_str} return JSONResponse(response, status_code=200) if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--host", type=str, default="0.0.0.0") parser.add_argument("--port", type=int, default=8081) parser.add_argument("--model_path", type=str, default='tencent/Hunyuan3D-2mini') parser.add_argument("--tex_model_path", type=str, default='tencent/Hunyuan3D-2') parser.add_argument("--device", type=str, default="cuda") parser.add_argument("--limit-model-concurrency", type=int, default=5) parser.add_argument('--enable_tex', action='store_true') args = parser.parse_args() logger.info(f"args: {args}") model_semaphore = asyncio.Semaphore(args.limit_model_concurrency) worker = ModelWorker(model_path=args.model_path, device=args.device, enable_tex=args.enable_tex, tex_model_path=args.tex_model_path) uvicorn.run(app, host=args.host, port=args.port, log_level="info") ================================================ FILE: assets/example_prompts.txt ================================================ 一片绿色的树叶在白色背景上居中展现，清晰的纹理 一只棕白相间的仓鼠，站在白色背景前。照片采用居中构图方式，卡通风格 一盆绿色植物生长在红色花盆中，居中，写实 a pot of green plants grows in a red flower pot. a lovely rabbit eating carrots ================================================ FILE: assets/modelviewer-template.html ================================================
================================================ FILE: assets/modelviewer-textured-template.html ================================================
Appearance
Geometry
================================================ FILE: blender_addon.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. bl_info = { "name": "Hunyuan3D-2 Generator", "author": "Tencent Hunyuan3D", "version": (1, 0), "blender": (3, 0, 0), "location": "View3D > Sidebar > Hunyuan3D-2 3D Generator", "description": "Generate/Texturing 3D models from text descriptions or images", "category": "3D View", } import base64 import os import tempfile import threading import bpy import requests from bpy.props import StringProperty, BoolProperty, IntProperty, FloatProperty class Hunyuan3DProperties(bpy.types.PropertyGroup): prompt: StringProperty( name="Text Prompt", description="Describe what you want to generate", default="" ) api_url: StringProperty( name="API URL", description="URL of the Text-to-3D API service", default="http://localhost:8080" ) is_processing: BoolProperty( name="Processing", default=False ) job_id: StringProperty( name="Job ID", default="" ) status_message: StringProperty( name="Status Message", default="" ) # 添加图片路径属性 image_path: StringProperty( name="Image", description="Select an image to upload", subtype='FILE_PATH' ) # 修改后的 octree_resolution 属性 octree_resolution: IntProperty( name="Octree Resolution", description="Octree resolution for the 3D generation", default=256, min=128, max=512, ) num_inference_steps: IntProperty( name="Number of Inference Steps", description="Number of inference steps for the 3D generation", default=20, min=20, max=50 ) guidance_scale: FloatProperty( name="Guidance Scale", description="Guidance scale for the 3D generation", default=5.5, min=1.0, max=10.0 ) # 添加 texture 属性 texture: BoolProperty( name="Generate Texture", description="Whether to generate texture for the 3D model", default=False ) class Hunyuan3DOperator(bpy.types.Operator): bl_idname = "object.generate_3d" bl_label = "Generate 3D Model" bl_description = "Generate a 3D model from text description, an image or a selected mesh" job_id = '' prompt = "" api_url = "" image_path = "" octree_resolution = 256 num_inference_steps = 20 guidance_scale = 5.5 texture = False # 新增属性 selected_mesh_base64 = "" selected_mesh = None # 新增属性，用于存储选中的 mesh thread = None task_finished = False def modal(self, context, event): if event.type in {'RIGHTMOUSE', 'ESC'}: return {'CANCELLED'} if self.task_finished: print("Threaded task completed") self.task_finished = False props = context.scene.gen_3d_props props.is_processing = False return {'PASS_THROUGH'} def invoke(self, context, event): # 启动线程 props = context.scene.gen_3d_props self.prompt = props.prompt self.api_url = props.api_url self.image_path = props.image_path self.octree_resolution = props.octree_resolution self.num_inference_steps = props.num_inference_steps self.guidance_scale = props.guidance_scale self.texture = props.texture # 获取 texture 属性的值 if self.prompt == "" and self.image_path == "": self.report({'WARNING'}, "Please enter some text or select an image first.") return {'FINISHED'} # 保存选中的 mesh 对象引用 for obj in context.selected_objects: if obj.type == 'MESH': self.selected_mesh = obj break if self.selected_mesh: temp_glb_file = tempfile.NamedTemporaryFile(delete=False, suffix=".glb") temp_glb_file.close() bpy.ops.export_scene.gltf(filepath=temp_glb_file.name, use_selection=True) with open(temp_glb_file.name, "rb") as file: mesh_data = file.read() mesh_b64_str = base64.b64encode(mesh_data).decode() os.unlink(temp_glb_file.name) self.selected_mesh_base64 = mesh_b64_str props.is_processing = True # 将相对路径转换为相对于 Blender 文件所在目录的绝对路径 blend_file_dir = os.path.dirname(bpy.data.filepath) self.report({'INFO'}, f"blend_file_dir {blend_file_dir}") self.report({'INFO'}, f"image_path {self.image_path}") if self.image_path.startswith('//'): self.image_path = self.image_path[2:] self.image_path = os.path.join(blend_file_dir, self.image_path) if self.selected_mesh and self.texture: props.status_message = "Texturing Selected Mesh...\n" \ "This may take several minutes depending \n on your GPU power." else: mesh_type = 'Textured Mesh' if self.texture else 'White Mesh' prompt_type = 'Text Prompt' if self.prompt else 'Image' props.status_message = f"Generating {mesh_type} with {prompt_type}...\n" \ "This may take several minutes depending \n on your GPU power." self.thread = threading.Thread(target=self.generate_model, args=[context]) self.thread.start() wm = context.window_manager wm.modal_handler_add(self) return {'RUNNING_MODAL'} def generate_model(self, context): self.report({'INFO'}, f"Generation Start") base_url = self.api_url.rstrip('/') try: if self.selected_mesh_base64 and self.texture: # Texturing the selected mesh if self.image_path and os.path.exists(self.image_path): self.report({'INFO'}, f"Post Texturing with Image") # 打开图片文件并以二进制模式读取 with open(self.image_path, "rb") as file: # 读取文件内容 image_data = file.read() # 对图片数据进行 Base64 编码 img_b64_str = base64.b64encode(image_data).decode() response = requests.post( f"{base_url}/generate", json={ "mesh": self.selected_mesh_base64, "image": img_b64_str, "octree_resolution": self.octree_resolution, "num_inference_steps": self.num_inference_steps, "guidance_scale": self.guidance_scale, "texture": self.texture # 传递 texture 参数 }, ) else: self.report({'INFO'}, f"Post Texturing with Text") response = requests.post( f"{base_url}/generate", json={ "mesh": self.selected_mesh_base64, "text": self.prompt, "octree_resolution": self.octree_resolution, "num_inference_steps": self.num_inference_steps, "guidance_scale": self.guidance_scale, "texture": self.texture # 传递 texture 参数 }, ) else: if self.image_path: if not os.path.exists(self.image_path): self.report({'ERROR'}, f"Image path does not exist {self.image_path}") raise Exception(f'Image path does not exist {self.image_path}') self.report({'INFO'}, f"Post Start Image to 3D") # 打开图片文件并以二进制模式读取 with open(self.image_path, "rb") as file: # 读取文件内容 image_data = file.read() # 对图片数据进行 Base64 编码 img_b64_str = base64.b64encode(image_data).decode() response = requests.post( f"{base_url}/generate", json={ "image": img_b64_str, "octree_resolution": self.octree_resolution, "num_inference_steps": self.num_inference_steps, "guidance_scale": self.guidance_scale, "texture": self.texture # 传递 texture 参数 }, ) else: self.report({'INFO'}, f"Post Start Text to 3D") response = requests.post( f"{base_url}/generate", json={ "text": self.prompt, "octree_resolution": self.octree_resolution, "num_inference_steps": self.num_inference_steps, "guidance_scale": self.guidance_scale, "texture": self.texture # 传递 texture 参数 }, ) self.report({'INFO'}, f"Post Done") self.task_finished = True props = context.scene.gen_3d_props props.is_processing = False if response.status_code != 200: self.report({'ERROR'}, f"Generation failed: {response.text}") return # Decode base64 and save to temporary file temp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".glb") temp_file.write(response.content) temp_file.close() # Import the GLB file in the main thread def import_handler(): bpy.ops.import_scene.gltf(filepath=temp_file.name) os.unlink(temp_file.name) # 获取新导入的 mesh new_obj = bpy.context.selected_objects[0] if bpy.context.selected_objects else None if new_obj and self.selected_mesh and self.texture: # 应用选中 mesh 的位置、旋转和缩放 new_obj.location = self.selected_mesh.location new_obj.rotation_euler = self.selected_mesh.rotation_euler new_obj.scale = self.selected_mesh.scale # 隐藏原来的 mesh self.selected_mesh.hide_set(True) self.selected_mesh.hide_render = True return None bpy.app.timers.register(import_handler) except Exception as e: self.report({'ERROR'}, f"Error: {str(e)}") finally: self.task_finished = True props = context.scene.gen_3d_props props.is_processing = False self.selected_mesh_base64 = "" class Hunyuan3DPanel(bpy.types.Panel): bl_space_type = 'VIEW_3D' bl_region_type = 'UI' bl_category = 'Hunyuan3D-2' bl_label = 'Hunyuan3D-2 3D Generator' def draw(self, context): layout = self.layout props = context.scene.gen_3d_props layout.prop(props, "api_url") layout.prop(props, "prompt") # 添加图片选择器 layout.prop(props, "image_path") # 添加新属性的 UI 元素 layout.prop(props, "octree_resolution") layout.prop(props, "num_inference_steps") layout.prop(props, "guidance_scale") # 添加 texture 属性的 UI 元素 layout.prop(props, "texture") row = layout.row() row.enabled = not props.is_processing row.operator("object.generate_3d") if props.is_processing: if props.status_message: for line in props.status_message.split("\n"): layout.label(text=line) else: layout.label("Processing...") classes = ( Hunyuan3DProperties, Hunyuan3DOperator, Hunyuan3DPanel, ) def register(): for cls in classes: bpy.utils.register_class(cls) bpy.types.Scene.gen_3d_props = bpy.props.PointerProperty(type=Hunyuan3DProperties) def unregister(): for cls in reversed(classes): bpy.utils.unregister_class(cls) del bpy.types.Scene.gen_3d_props if __name__ == "__main__": register() ================================================ FILE: docs/Makefile ================================================ # Minimal makefile for Sphinx documentation # # You can set these variables from the command line, and also # from the environment for the first two. SPHINXOPTS ?= SPHINXBUILD ?= sphinx-build SOURCEDIR = source BUILDDIR = build # Put it first so that "make" without argument is like "make help". help: @$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) .PHONY: help Makefile # Catch-all target: route all unknown targets to Sphinx using the new # "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS). %: Makefile @$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O) ================================================ FILE: docs/README.md ================================================ # Documentation Source of the documentation. - Build html. ```bash make html ``` - Preview at `build/html/index.html` ================================================ FILE: docs/make.bat ================================================ @ECHO OFF pushd %~dp0 REM Command file for Sphinx documentation if "%SPHINXBUILD%" == "" ( set SPHINXBUILD=sphinx-build ) set SOURCEDIR=source set BUILDDIR=build if "%1" == "" goto help %SPHINXBUILD% >NUL 2>NUL if errorlevel 9009 ( echo. echo.The 'sphinx-build' command was not found. Make sure you have Sphinx echo.installed, then set the SPHINXBUILD environment variable to point echo.to the full path of the 'sphinx-build' executable. Alternatively you echo.may add the Sphinx directory to PATH. echo. echo.If you don't have Sphinx installed, grab it from echo.https://www.sphinx-doc.org/ exit /b 1 ) %SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% goto end :help %SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% :end popd ================================================ FILE: docs/requirements.txt ================================================ myst-parser sphinx-rtd-theme furo sphinx-copybutton sphinx-inline-tabs nbsphinx nbsphinx_link linkify-it-py linkify ipython torch imageio scikit_image matplotlib munch tfpnp cvxpy torchlights tensorboardX termcolor proximal opencv-python huggingface_hub torchvision ================================================ FILE: docs/source/_static/css/custom.css ================================================ /*.sidebar-logo {*/ /* display: block;*/ /* margin: 0;*/ /* max-width: 50%;*/ /*}*/ .nbsphinx-gallery { display: grid; grid-template-columns: repeat(auto-fill, minmax(200px, 1fr)); gap: 5px; margin-top: 1em; margin-bottom: 1em; } h1 { font-size: 2em } h2 { font-size: 1.3em } h3 { font-size: 1.25em } h4 { font-size: 1.125em } h5 { font-size: 1.07em } h6 { font-size: 1em } ================================================ FILE: docs/source/citation.md ================================================ # Citation The following publications discuss the ideas behind Hunyuan3D-2: > **Unleashing Vecset Diffusion Model for Fast Shape Generation**
> Technical Report, 2025. > **Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation**
> Technical Report, 2024. > **Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation**
> Technical Report, 2024. If you found this repository helpful, please cite our reports: ```bibtex @misc{lai2025flashvdm, title={Unleashing Vecset Diffusion Model for Fast Shape Generation}, author={Zeqiang Lai and Yunfei Zhao and Zibo Zhao and Haolin Liu and Fuyun Wang and Huiwen Shi and Xianghui Yang and Qinxiang Lin and Jinwei Huang and Yuhong Liu and Jie Jiang and Chunchao Guo and Xiangyu Yue}, year={2025}, eprint={2503.16302}, archivePrefix={arXiv}, primaryClass={cs.CV}, url={https://arxiv.org/abs/2503.16302}, } @misc{hunyuan3d22025tencent, title={Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation}, author={Tencent Hunyuan3D Team}, year={2025}, eprint={2501.12202}, archivePrefix={arXiv}, primaryClass={cs.CV} } @misc{yang2024hunyuan3d, title={Hunyuan3D 1.0: A Unified Framework for Text-to-3D and Image-to-3D Generation}, author={Tencent Hunyuan3D Team}, year={2024}, eprint={2411.02293}, archivePrefix={arXiv}, primaryClass={cs.CV} } ``` ================================================ FILE: docs/source/conf.py ================================================ # Configuration file for the Sphinx documentation builder. # # This file only contains a selection of the most common options. For a full # list see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Path setup -------------------------------------------------------------- # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. import os import sys sys.path.insert(0, os.path.abspath(".")) sys.path.insert(0, os.path.abspath("../../")) # -- Project information ----------------------------------------------------- project = 'Hunyuan3D-2' copyright = '2025, Tencent Hunyuan3D' author = 'Hunyuan3D Team' # The full version, including alpha/beta/rc tags release = '0.0.1' # -- General configuration --------------------------------------------------- # Add any Sphinx extension module names here, as strings. They can be # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom # ones. extensions = [ 'myst_parser', 'nbsphinx', 'nbsphinx_link', # "myst_nb", 'sphinx_copybutton', # "sphinx_inline_tabs", # https://sphinx-codeautolink.readthedocs.io/en/latest/examples.html 'sphinx.ext.autodoc', "sphinx.ext.intersphinx", "sphinx.ext.extlinks", 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', ] # -- Options for extlinks ---------------------------------------------------- # extlinks = { "pypi": ("https://pypi.org/project/%s/", "%s"), } # -- Options for intersphinx ------------------------------------------------- # intersphinx_mapping = { "python": ("https://docs.python.org/3", None), "sphinx": ("https://www.sphinx-doc.org/en/master", None), 'torch': ('https://pytorch.org/docs/master/', None) } napoleon_preprocess_types = True myst_enable_extensions = [ "amsmath", "colon_fence", "deflist", "dollarmath", "fieldlist", "html_admonition", "html_image", "linkify", "replacements", "smartquotes", "strikethrough", "substitution", "tasklist", ] # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. # This pattern also affects html_static_path and html_extra_path. exclude_patterns = [] # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # -- Options for HTML output ------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. # # html_theme = 'alabaster' # html_theme = 'sphinx_rtd_theme' html_theme = "furo" html_title = "Hunyuan3D-2" language = "en" # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static'] html_theme_options = { "light_css_variables": { "font-stack": "Arial,Noto Sans,sans-serif", "font-stack--monospace": "IBM Plex Mono,ui-monospace,SFMono-Regular,Menlo,Monaco,Consolas,Liberation Mono,Courier New,monospace", }, "announcement": 'Release 🤗Turbo Series and FlashVDM, Fast Shape Generation within 1 Second Right Now!', } # # -- Options for TODOs ------------------------------------------------------- # todo_include_todos = True # # -- Options for Markdown files ---------------------------------------------- # myst_admonition_enable = True myst_deflist_enable = True myst_heading_anchors = 3 html_favicon = '_static/favicon.ico' pygments_style = "default" pygments_dark_style = "github-dark" html_css_files = [ 'css/custom.css', ] ================================================ FILE: docs/source/index.md ================================================ --- hide-toc: true --- # Welcome to Hunyuan3D ```{toctree} :maxdepth: 3 :hidden: Installation Get Started Model Zoo citation ``` ```{toctree} :caption: Useful Links :hidden: PyPI Page GitHub Repository Paper ```
☯️ Hunyuan3D 2.0 is an advanced large-scale 3D synthesis system for generating high-resolution textured 3D assets. The system includes the following foundation components: 1. [Hunyuan3D-DiT](): a large-scale shape generation model. 2. [Hunyuan3D-Paint](): a large-scale texture synthesis model. 3. [Hunyuan3D-Studio](): a versatile, user-friendly production platform that simplifies the re-creation process of 3D assets. It allows both professional and amateur users to manipulate or even animate their meshes efficiently. 4. [FlashVDM](): a universal acceleration framework. ```{nbgallery} ```
Quicktour

Learn the fundamentals of using ∇-Prox. We recommend starting here if you are using 🎉 ∇-Prox for the first time!

Tutorials

Understand the design of the library and the mathematics behind the code.

API Documentation

Explore the complete reference guide. Useful if you want to develop programs with ∇-Prox.

================================================ FILE: docs/source/installation/index.md ================================================ # Installation ```{toctree} :hidden: ``` ∇-Prox works with PyTorch. To install Pytorch, please follow the [PyTorch installation instructions](https://pytorch.org/get-started/locally/). **Install with pip** ```bash pip install dprox ``` **Install from source** ```bash pip install git+https://github.com/princeton-computational-imaging/Delta-Prox.git ``` **Editable installation** You will need an editable install if you would like to: 1. Use the main version of the source code. 2. Need to test changes in the code. To do so, clone the repository and install 🎉 Delta Prox with the following commands: ``` git clone git+https://github.com/princeton-computational-imaging/Delta-Prox.git cd DeltaProx pip install -e . ``` ```{caution} Note that you must keep the DeltaProx folder for editable installation if you want to keep using the library. ``` ================================================ FILE: docs/source/modelzoo.md ================================================ # Model Zoo It takes 6 GB VRAM for shape generation and 24.5 GB for shape and texture generation in total. ## Hunyuan3D-2mini Series | Model | Description | Date | Size | Huggingface | |-----------------------------|-------------------------------|------------|------|--------------------------------------------------------------------------------------------------| | Hunyuan3D-DiT-v2-mini-Turbo | Step Distillation Version | 2025-03-19 | 0.6B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mini/tree/main/hunyuan3d-dit-v2-mini-turbo) | | Hunyuan3D-DiT-v2-mini-Fast | Guidance Distillation Version | 2025-03-18 | 0.6B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mini/tree/main/hunyuan3d-dit-v2-mini-fast) | | Hunyuan3D-DiT-v2-mini | Mini Image to Shape Model | 2025-03-18 | 0.6B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mini/tree/main/hunyuan3d-dit-v2-mini) | ## Hunyuan3D-2mv Series | Model | Description | Date | Size | Huggingface | |---------------------------|--------------------------------|------------|------|----------------------------------------------------------------------------------------------| | Hunyuan3D-DiT-v2-mv-Turbo | Step Distillation Version | 2025-03-19 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mv/tree/main/hunyuan3d-dit-v2-mv-turbo) | | Hunyuan3D-DiT-v2-mv-Fast | Guidance Distillation Version | 2025-03-18 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mv/tree/main/hunyuan3d-dit-v2-mv-fast) | | Hunyuan3D-DiT-v2-mv | Multiview Image to Shape Model | 2025-03-18 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2mv/tree/main/hunyuan3d-dit-v2-mv) | ## Hunyuan3D-2 Series | Model | Description | Date | Size | Huggingface | |--------------------------|-----------------------------|------------|------|-------------------------------------------------------------------------------------------| | Hunyuan3D-DiT-v2-0-Turbo | Step Distillation Model | 2025-03-19 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-dit-v2-0-turbo) | | Hunyuan3D-DiT-v2-0-Fast | Guidance Distillation Model | 2025-02-03 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-dit-v2-0-fast) | | Hunyuan3D-DiT-v2-0 | Image to Shape Model | 2025-01-21 | 1.1B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-dit-v2-0) | | Hunyuan3D-Paint-v2-0 | Texture Generation Model | 2025-01-21 | 1.3B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-paint-v2-0) | | Hunyuan3D-Delight-v2-0 | Image Delight Model | 2025-01-21 | 1.3B | [Download](https://huggingface.co/tencent/Hunyuan3D-2/tree/main/hunyuan3d-delight-v2-0) | ================================================ FILE: docs/source/started/api.md ================================================ # API You could launch an API server locally, which you could post web request for Image/Text to 3D, Texturing existing mesh, and e.t.c. ```bash python api_server.py --host 0.0.0.0 --port 8080 ``` A demo post request for image to 3D without texture. ```bash img_b64_str=$(base64 -i assets/demo.png) curl -X POST "http://localhost:8080/generate" \ -H "Content-Type: application/json" \ -d '{ "image": "'"$img_b64_str"'", }' \ -o test2.glb ``` ================================================ FILE: docs/source/started/blender.md ================================================ # Blender Addon With an API server launched, you could also directly use Hunyuan3D 2.0 in your blender with our [Blender Addon](blender_addon.py). Please follow our tutorial to install and use. https://github.com/user-attachments/assets/8230bfb5-32b1-4e48-91f4-a977c54a4f3e ================================================ FILE: docs/source/started/code.md ================================================ # Code We designed a diffusers-like API to use our shape generation model - Hunyuan3D-DiT and texture synthesis model - Hunyuan3D-Paint. You could assess **Hunyuan3D-DiT** via: ```python from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(image='assets/demo.png')[0] ``` The output mesh is a [trimesh object](https://trimesh.org/trimesh.html), which you could save to glb/obj (or other format) file. For **Hunyuan3D-Paint**, do the following: ```python from hy3dgen.texgen import Hunyuan3DPaintPipeline from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline # let's generate a mesh first pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(image='assets/demo.png')[0] pipeline = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2') mesh = pipeline(mesh, image='assets/demo.png') ``` Please visit [examples](examples) folder for more advanced usage, such as **multiview image to 3D generation** and * *texture generation for handcrafted mesh**. ================================================ FILE: docs/source/started/comfyui.md ================================================ # Comfyui comfyui ================================================ FILE: docs/source/started/gradio.md ================================================ # Gradio APP You could also host a [Gradio](https://www.gradio.app/) App in your own computer via: Standard Version ```bash # Hunyuan3D-2mini python3 gradio_app.py --model_path tencent/Hunyuan3D-2mini --subfolder hunyuan3d-dit-v2-mini --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode # Hunyuan3D-2mv python3 gradio_app.py --model_path tencent/Hunyuan3D-2mv --subfolder hunyuan3d-dit-v2-mv --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode # Hunyuan3D-2 python3 gradio_app.py --model_path tencent/Hunyuan3D-2 --subfolder hunyuan3d-dit-v2-0 --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode ``` Turbo Version ```bash # Hunyuan3D-2mini python3 gradio_app.py --model_path tencent/Hunyuan3D-2mini --subfolder hunyuan3d-dit-v2-mini-turbo --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode --enable_flashvdm # Hunyuan3D-2mv python3 gradio_app.py --model_path tencent/Hunyuan3D-2mv --subfolder hunyuan3d-dit-v2-mv-turbo --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode --enable_flashvdm # Hunyuan3D-2 python3 gradio_app.py --model_path tencent/Hunyuan3D-2 --subfolder hunyuan3d-dit-v2-0-turbo --texgen_model_path tencent/Hunyuan3D-2 --low_vram_mode --enable_flashvdm ``` ================================================ FILE: docs/source/started/index.md ================================================ # Get Started ```{toctree} :hidden: gradio comfyui api blender code studio ``` ## Installation To get started with ∇-Prox, please follow the [Installation Documentation](install) for detailed instructions on how to install the library. ## Quick Tour - Take a [Quick Tour](quicktour) to get familiar with the features and functionalities of ∇-Prox. - Explore the [API Reference](../api/index) for a complete list of classes and functions. - For advanced topics and best practices, refer to the [tutorials](../tutorials/index). Happy coding with ∇-Prox! 🎉 ================================================ FILE: docs/source/started/studio.md ================================================ # Official Website Don't forget to visit [Hunyuan3D](https://3d.hunyuan.tencent.com) for quick use, if you don't want to host yourself. ================================================ FILE: examples/fast_shape_gen_multiview.py ================================================ import time import torch from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline images = { "front": "assets/example_mv_images/1/front.png", "left": "assets/example_mv_images/1/left.png", "back": "assets/example_mv_images/1/back.png" } for key in images: image = Image.open(images[key]).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) images[key] = image pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( 'tencent/Hunyuan3D-2mv', subfolder='hunyuan3d-dit-v2-mv-turbo', variant='fp16' ) pipeline.enable_flashvdm() start_time = time.time() mesh = pipeline( image=images, num_inference_steps=5, octree_resolution=380, num_chunks=20000, generator=torch.manual_seed(12345), output_type='trimesh' )[0] print("--- %s seconds ---" % (time.time() - start_time)) mesh.export(f'demo_mv3.glb') ================================================ FILE: examples/fast_shape_gen_with_flashvdm.py ================================================ # HY3DGEN_DEBUG=1 USE_SAGEATTN=1 python3 examples/fast_shape_gen_with_flashvdm.py # HY3DGEN_DEBUG=1 USE_SAGEATTN=0 python3 examples/fast_shape_gen_with_flashvdm.py import os import time import torch from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( 'tencent/Hunyuan3D-2', subfolder='hunyuan3d-dit-v2-0-turbo', use_safetensors=True, ) pipeline.enable_flashvdm() # pipeline.compile() image_path = 'assets/demo.png' image = Image.open(image_path).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) def run(): return pipeline( image=image, num_inference_steps=5, octree_resolution=380, num_chunks=200000, generator=torch.manual_seed(12345), output_type='trimesh' )[0] save_dir = 'tmp/results/' os.makedirs(save_dir, exist_ok=True) for it in range(2): start_time = time.time() mesh = run() print("--- %s seconds ---" % (time.time() - start_time)) mesh.export(f'{save_dir}/run_{it}.glb') ================================================ FILE: examples/fast_texture_gen_multiview.py ================================================ import time import torch from PIL import Image import trimesh from hy3dgen.rembg import BackgroundRemover from hy3dgen.texgen import Hunyuan3DPaintPipeline images_path = [ "assets/example_mv_images/1/front.png", "assets/example_mv_images/1/left.png", "assets/example_mv_images/1/back.png" ] images = [] for image_path in images_path: image = Image.open(image_path) if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) images.append(image) pipeline = Hunyuan3DPaintPipeline.from_pretrained( 'tencent/Hunyuan3D-2', subfolder='hunyuan3d-paint-v2-0-turbo' ) mesh = trimesh.load('assets/1.glb') mesh = pipeline(mesh, image=images) mesh.export('demo_textured.glb') ================================================ FILE: examples/faster_shape_gen_with_flashvdm_mini_turbo.py ================================================ # HY3DGEN_DEBUG=1 USE_SAGEATTN=1 python3 examples/faster_shape_gen_with_flashvdm_mini_turbo.py # HY3DGEN_DEBUG=1 USE_SAGEATTN=0 python3 examples/faster_shape_gen_with_flashvdm_mini_turbo.py import os import time import torch from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline device = 'cuda' pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( 'tencent/Hunyuan3D-2mini', subfolder='hunyuan3d-dit-v2-mini-turbo', use_safetensors=False, device=device ) pipeline.enable_flashvdm(topk_mode='merge') # pipeline.compile() image_path = 'assets/demo.png' image = Image.open(image_path).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) def run(): return pipeline( image=image, num_inference_steps=5, octree_resolution=380, num_chunks=20000, generator=torch.manual_seed(12345), output_type='trimesh' )[0] save_dir = 'tmp/results/' os.makedirs(save_dir, exist_ok=True) for it in range(2): start_time = time.time() mesh = run() print("--- %s seconds ---" % (time.time() - start_time)) mesh.export(f'{save_dir}/run_{it}.glb') ================================================ FILE: examples/shape_gen.py ================================================ import time import torch from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline image_path = 'assets/demo.png' image = Image.open(image_path).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( 'tencent/Hunyuan3D-2', subfolder='hunyuan3d-dit-v2-0', variant='fp16' ) start_time = time.time() mesh = pipeline(image=image, num_inference_steps=50, octree_resolution=380, num_chunks=20000, generator=torch.manual_seed(12345), output_type='trimesh' )[0] print("--- %s seconds ---" % (time.time() - start_time)) mesh.export(f'demo.glb') ================================================ FILE: examples/shape_gen_mini.py ================================================ import time import torch from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline image_path = 'assets/demo.png' image = Image.open(image_path).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( 'tencent/Hunyuan3D-2mini', subfolder='hunyuan3d-dit-v2-mini', variant='fp16' ) start_time = time.time() mesh = pipeline( image=image, num_inference_steps=50, octree_resolution=380, num_chunks=20000, generator=torch.manual_seed(12345), output_type='trimesh' )[0] print("--- %s seconds ---" % (time.time() - start_time)) mesh.export(f'demo_mini.glb') ================================================ FILE: examples/shape_gen_multiview.py ================================================ import time import torch from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline images = { "front": "assets/example_mv_images/1/front.png", "left": "assets/example_mv_images/1/left.png", "back": "assets/example_mv_images/1/back.png" } for key in images: image = Image.open(images[key]).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) images[key] = image pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( 'tencent/Hunyuan3D-2mv', subfolder='hunyuan3d-dit-v2-mv', variant='fp16' ) start_time = time.time() mesh = pipeline( image=images, num_inference_steps=50, octree_resolution=380, num_chunks=20000, generator=torch.manual_seed(12345), output_type='trimesh' )[0] print("--- %s seconds ---" % (time.time() - start_time)) mesh.export(f'demo_mv.glb') ================================================ FILE: examples/shape_gen_v2_1.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline model_path = 'tencent/Hunyuan3D-2.1' pipeline_shapegen = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( model_path, subfolder='hunyuan3d-dit-v2-1' ) image_path = 'assets/demo.png' image = Image.open(image_path).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) mesh = pipeline_shapegen(image=image)[0] mesh.export('demo.glb') ================================================ FILE: examples/textured_shape_gen.py ================================================ from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline from hy3dgen.texgen import Hunyuan3DPaintPipeline model_path = 'tencent/Hunyuan3D-2' pipeline_shapegen = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(model_path) pipeline_texgen = Hunyuan3DPaintPipeline.from_pretrained(model_path) image_path = 'assets/demo.png' image = Image.open(image_path).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) mesh = pipeline_shapegen(image=image)[0] mesh = pipeline_texgen(mesh, image=image) mesh.export('demo.glb') ================================================ FILE: examples/textured_shape_gen_mini.py ================================================ import time import torch from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline from hy3dgen.texgen import Hunyuan3DPaintPipeline image_path = 'assets/demo.png' image = Image.open(image_path).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( 'tencent/Hunyuan3D-2mini', subfolder='hunyuan3d-dit-v2-mini', variant='fp16' ) pipeline_texgen = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2') start_time = time.time() mesh = pipeline( image=image, num_inference_steps=50, octree_resolution=380, num_chunks=20000, generator=torch.manual_seed(12345), output_type='trimesh' )[0] print("--- %s seconds ---" % (time.time() - start_time)) mesh.export(f'demo_mini.glb') mesh = pipeline_texgen(mesh, image=image) mesh.export('demo_textured_mini.glb') ================================================ FILE: examples/textured_shape_gen_multiview.py ================================================ import time import torch from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline from hy3dgen.texgen import Hunyuan3DPaintPipeline images = { "front": "assets/example_mv_images/1/front.png", "left": "assets/example_mv_images/1/left.png", "back": "assets/example_mv_images/1/back.png" } for key in images: image = Image.open(images[key]).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) images[key] = image pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( 'tencent/Hunyuan3D-2mv', subfolder='hunyuan3d-dit-v2-mv', variant='fp16' ) pipeline_texgen = Hunyuan3DPaintPipeline.from_pretrained('tencent/Hunyuan3D-2') start_time = time.time() mesh = pipeline( image=images, num_inference_steps=50, octree_resolution=380, num_chunks=20000, generator=torch.manual_seed(12345), output_type='trimesh' )[0] print("--- %s seconds ---" % (time.time() - start_time)) mesh.export(f'demo_white_mesh_mv.glb') mesh = pipeline_texgen(mesh, image=images["front"]) mesh.export('demo_textured_mv.glb') ================================================ FILE: gradio_app.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import os import random import shutil import time from glob import glob from pathlib import Path import gradio as gr import torch import trimesh import uvicorn from fastapi import FastAPI from fastapi.staticfiles import StaticFiles import uuid from hy3dgen.shapegen.utils import logger MAX_SEED = int(1e7) def get_example_img_list(): print('Loading example img list ...') return sorted(glob('./assets/example_images/**/*.png', recursive=True)) def get_example_txt_list(): print('Loading example txt list ...') txt_list = list() for line in open('./assets/example_prompts.txt', encoding='utf-8'): txt_list.append(line.strip()) return txt_list def get_example_mv_list(): print('Loading example mv list ...') mv_list = list() root = './assets/example_mv_images' for mv_dir in os.listdir(root): view_list = [] for view in ['front', 'back', 'left', 'right']: path = os.path.join(root, mv_dir, f'{view}.png') if os.path.exists(path): view_list.append(path) else: view_list.append(None) mv_list.append(view_list) return mv_list def gen_save_folder(max_size=200): os.makedirs(SAVE_DIR, exist_ok=True) # 获取所有文件夹路径 dirs = [f for f in Path(SAVE_DIR).iterdir() if f.is_dir()] # 如果文件夹数量超过 max_size，删除创建时间最久的文件夹 if len(dirs) >= max_size: # 按创建时间排序，最久的排在前面 oldest_dir = min(dirs, key=lambda x: x.stat().st_ctime) shutil.rmtree(oldest_dir) print(f"Removed the oldest folder: {oldest_dir}") # 生成一个新的 uuid 文件夹名称 new_folder = os.path.join(SAVE_DIR, str(uuid.uuid4())) os.makedirs(new_folder, exist_ok=True) print(f"Created new folder: {new_folder}") return new_folder def export_mesh(mesh, save_folder, textured=False, type='glb'): if textured: path = os.path.join(save_folder, f'textured_mesh.{type}') else: path = os.path.join(save_folder, f'white_mesh.{type}') if type not in ['glb', 'obj']: mesh.export(path) else: mesh.export(path, include_normals=textured) return path def randomize_seed_fn(seed: int, randomize_seed: bool) -> int: if randomize_seed: seed = random.randint(0, MAX_SEED) return seed def build_model_viewer_html(save_folder, height=660, width=790, textured=False): # Remove first folder from path to make relative path if textured: related_path = f"./textured_mesh.glb" template_name = './assets/modelviewer-textured-template.html' output_html_path = os.path.join(save_folder, f'textured_mesh.html') else: related_path = f"./white_mesh.glb" template_name = './assets/modelviewer-template.html' output_html_path = os.path.join(save_folder, f'white_mesh.html') offset = 50 if textured else 10 with open(os.path.join(CURRENT_DIR, template_name), 'r', encoding='utf-8') as f: template_html = f.read() with open(output_html_path, 'w', encoding='utf-8') as f: template_html = template_html.replace('#height#', f'{height - offset}') template_html = template_html.replace('#width#', f'{width}') template_html = template_html.replace('#src#', f'{related_path}/') f.write(template_html) rel_path = os.path.relpath(output_html_path, SAVE_DIR) iframe_tag = f'' print( f'Find html file {output_html_path}, {os.path.exists(output_html_path)}, relative HTML path is /static/{rel_path}') return f"""
{iframe_tag}
""" def _gen_shape( caption=None, image=None, mv_image_front=None, mv_image_back=None, mv_image_left=None, mv_image_right=None, steps=50, guidance_scale=7.5, seed=1234, octree_resolution=256, check_box_rembg=False, num_chunks=200000, randomize_seed: bool = False, ): if not MV_MODE and image is None and caption is None: raise gr.Error("Please provide either a caption or an image.") if MV_MODE: if mv_image_front is None and mv_image_back is None and mv_image_left is None and mv_image_right is None: raise gr.Error("Please provide at least one view image.") image = {} if mv_image_front: image['front'] = mv_image_front if mv_image_back: image['back'] = mv_image_back if mv_image_left: image['left'] = mv_image_left if mv_image_right: image['right'] = mv_image_right seed = int(randomize_seed_fn(seed, randomize_seed)) octree_resolution = int(octree_resolution) if caption: print('prompt is', caption) save_folder = gen_save_folder() stats = { 'model': { 'shapegen': f'{args.model_path}/{args.subfolder}', 'texgen': f'{args.texgen_model_path}', }, 'params': { 'caption': caption, 'steps': steps, 'guidance_scale': guidance_scale, 'seed': seed, 'octree_resolution': octree_resolution, 'check_box_rembg': check_box_rembg, 'num_chunks': num_chunks, } } time_meta = {} if image is None: start_time = time.time() try: image = t2i_worker(caption) except Exception as e: raise gr.Error(f"Text to 3D is disable. Please enable it by `python gradio_app.py --enable_t23d`.") time_meta['text2image'] = time.time() - start_time # remove disk io to make responding faster, uncomment at your will. # image.save(os.path.join(save_folder, 'input.png')) if MV_MODE: start_time = time.time() for k, v in image.items(): if check_box_rembg or v.mode == "RGB": img = rmbg_worker(v.convert('RGB')) image[k] = img time_meta['remove background'] = time.time() - start_time else: if check_box_rembg or image.mode == "RGB": start_time = time.time() image = rmbg_worker(image.convert('RGB')) time_meta['remove background'] = time.time() - start_time # remove disk io to make responding faster, uncomment at your will. # image.save(os.path.join(save_folder, 'rembg.png')) # image to white model start_time = time.time() generator = torch.Generator() generator = generator.manual_seed(int(seed)) outputs = i23d_worker( image=image, num_inference_steps=steps, guidance_scale=guidance_scale, generator=generator, octree_resolution=octree_resolution, num_chunks=num_chunks, output_type='mesh' ) time_meta['shape generation'] = time.time() - start_time logger.info("---Shape generation takes %s seconds ---" % (time.time() - start_time)) tmp_start = time.time() mesh = export_to_trimesh(outputs)[0] time_meta['export to trimesh'] = time.time() - tmp_start stats['number_of_faces'] = mesh.faces.shape[0] stats['number_of_vertices'] = mesh.vertices.shape[0] stats['time'] = time_meta main_image = image if not MV_MODE else image['front'] return mesh, main_image, save_folder, stats, seed def generation_all( caption=None, image=None, mv_image_front=None, mv_image_back=None, mv_image_left=None, mv_image_right=None, steps=50, guidance_scale=7.5, seed=1234, octree_resolution=256, check_box_rembg=False, num_chunks=200000, randomize_seed: bool = False, ): start_time_0 = time.time() mesh, image, save_folder, stats, seed = _gen_shape( caption, image, mv_image_front=mv_image_front, mv_image_back=mv_image_back, mv_image_left=mv_image_left, mv_image_right=mv_image_right, steps=steps, guidance_scale=guidance_scale, seed=seed, octree_resolution=octree_resolution, check_box_rembg=check_box_rembg, num_chunks=num_chunks, randomize_seed=randomize_seed, ) path = export_mesh(mesh, save_folder, textured=False) # tmp_time = time.time() # mesh = floater_remove_worker(mesh) # mesh = degenerate_face_remove_worker(mesh) # logger.info("---Postprocessing takes %s seconds ---" % (time.time() - tmp_time)) # stats['time']['postprocessing'] = time.time() - tmp_time tmp_time = time.time() mesh = face_reduce_worker(mesh) logger.info("---Face Reduction takes %s seconds ---" % (time.time() - tmp_time)) stats['time']['face reduction'] = time.time() - tmp_time tmp_time = time.time() textured_mesh = texgen_worker(mesh, image) logger.info("---Texture Generation takes %s seconds ---" % (time.time() - tmp_time)) stats['time']['texture generation'] = time.time() - tmp_time stats['time']['total'] = time.time() - start_time_0 textured_mesh.metadata['extras'] = stats path_textured = export_mesh(textured_mesh, save_folder, textured=True) model_viewer_html_textured = build_model_viewer_html(save_folder, height=HTML_HEIGHT, width=HTML_WIDTH, textured=True) if args.low_vram_mode: torch.cuda.empty_cache() return ( gr.update(value=path), gr.update(value=path_textured), model_viewer_html_textured, stats, seed, ) def shape_generation( caption=None, image=None, mv_image_front=None, mv_image_back=None, mv_image_left=None, mv_image_right=None, steps=50, guidance_scale=7.5, seed=1234, octree_resolution=256, check_box_rembg=False, num_chunks=200000, randomize_seed: bool = False, ): start_time_0 = time.time() mesh, image, save_folder, stats, seed = _gen_shape( caption, image, mv_image_front=mv_image_front, mv_image_back=mv_image_back, mv_image_left=mv_image_left, mv_image_right=mv_image_right, steps=steps, guidance_scale=guidance_scale, seed=seed, octree_resolution=octree_resolution, check_box_rembg=check_box_rembg, num_chunks=num_chunks, randomize_seed=randomize_seed, ) stats['time']['total'] = time.time() - start_time_0 mesh.metadata['extras'] = stats path = export_mesh(mesh, save_folder, textured=False) model_viewer_html = build_model_viewer_html(save_folder, height=HTML_HEIGHT, width=HTML_WIDTH) if args.low_vram_mode: torch.cuda.empty_cache() return ( gr.update(value=path), model_viewer_html, stats, seed, ) def build_app(): title = 'Hunyuan3D-2: High Resolution Textured 3D Assets Generation' if MV_MODE: title = 'Hunyuan3D-2mv: Image to 3D Generation with 1-4 Views' if 'mini' in args.subfolder: title = 'Hunyuan3D-2mini: Strong 0.6B Image to Shape Generator' if TURBO_MODE: title = title.replace(':', '-Turbo: Fast ') title_html = f"""
{title}
Tencent Hunyuan3D Team
""" custom_css = """ .app.svelte-wpkpf6.svelte-wpkpf6:not(.fill_width) { max-width: 1480px; } .mv-image button .wrap { font-size: 10px; } .mv-image .icon-wrap { width: 20px; } """ with gr.Blocks(theme=gr.themes.Base(), title='Hunyuan-3D-2.0', analytics_enabled=False, css=custom_css) as demo: gr.HTML(title_html) with gr.Row(): with gr.Column(scale=3): with gr.Tabs(selected='tab_img_prompt') as tabs_prompt: with gr.Tab('Image Prompt', id='tab_img_prompt', visible=not MV_MODE) as tab_ip: image = gr.Image(label='Image', type='pil', image_mode='RGBA', height=290) with gr.Tab('Text Prompt', id='tab_txt_prompt', visible=HAS_T2I and not MV_MODE) as tab_tp: caption = gr.Textbox(label='Text Prompt', placeholder='HunyuanDiT will be used to generate image.', info='Example: A 3D model of a cute cat, white background') with gr.Tab('MultiView Prompt', visible=MV_MODE) as tab_mv: # gr.Label('Please upload at least one front image.') with gr.Row(): mv_image_front = gr.Image(label='Front', type='pil', image_mode='RGBA', height=140, min_width=100, elem_classes='mv-image') mv_image_back = gr.Image(label='Back', type='pil', image_mode='RGBA', height=140, min_width=100, elem_classes='mv-image') with gr.Row(): mv_image_left = gr.Image(label='Left', type='pil', image_mode='RGBA', height=140, min_width=100, elem_classes='mv-image') mv_image_right = gr.Image(label='Right', type='pil', image_mode='RGBA', height=140, min_width=100, elem_classes='mv-image') with gr.Row(): btn = gr.Button(value='Gen Shape', variant='primary', min_width=100) btn_all = gr.Button(value='Gen Textured Shape', variant='primary', visible=HAS_TEXTUREGEN, min_width=100) with gr.Group(): file_out = gr.File(label="File", visible=False) file_out2 = gr.File(label="File", visible=False) with gr.Tabs(selected='tab_options' if TURBO_MODE else 'tab_export'): with gr.Tab("Options", id='tab_options', visible=TURBO_MODE): gen_mode = gr.Radio(label='Generation Mode', info='Recommendation: Turbo for most cases, Fast for very complex cases, Standard seldom use.', choices=['Turbo', 'Fast', 'Standard'], value='Turbo') decode_mode = gr.Radio(label='Decoding Mode', info='The resolution for exporting mesh from generated vectset', choices=['Low', 'Standard', 'High'], value='Standard') with gr.Tab('Advanced Options', id='tab_advanced_options'): with gr.Row(): check_box_rembg = gr.Checkbox(value=True, label='Remove Background', min_width=100) randomize_seed = gr.Checkbox(label="Randomize seed", value=True, min_width=100) seed = gr.Slider( label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=1234, min_width=100, ) with gr.Row(): num_steps = gr.Slider(maximum=100, minimum=1, value=5 if 'turbo' in args.subfolder else 30, step=1, label='Inference Steps') octree_resolution = gr.Slider(maximum=512, minimum=16, value=256, label='Octree Resolution') with gr.Row(): cfg_scale = gr.Number(value=5.0, label='Guidance Scale', min_width=100) num_chunks = gr.Slider(maximum=5000000, minimum=1000, value=8000, label='Number of Chunks', min_width=100) with gr.Tab("Export", id='tab_export'): with gr.Row(): file_type = gr.Dropdown(label='File Type', choices=SUPPORTED_FORMATS, value='glb', min_width=100) reduce_face = gr.Checkbox(label='Simplify Mesh', value=False, min_width=100) export_texture = gr.Checkbox(label='Include Texture', value=False, visible=False, min_width=100) target_face_num = gr.Slider(maximum=1000000, minimum=100, value=10000, label='Target Face Number') with gr.Row(): confirm_export = gr.Button(value="Transform", min_width=100) file_export = gr.DownloadButton(label="Download", variant='primary', interactive=False, min_width=100) with gr.Column(scale=6): with gr.Tabs(selected='gen_mesh_panel') as tabs_output: with gr.Tab('Generated Mesh', id='gen_mesh_panel'): html_gen_mesh = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output') with gr.Tab('Exporting Mesh', id='export_mesh_panel'): html_export_mesh = gr.HTML(HTML_OUTPUT_PLACEHOLDER, label='Output') with gr.Tab('Mesh Statistic', id='stats_panel'): stats = gr.Json({}, label='Mesh Stats') with gr.Column(scale=3 if MV_MODE else 2): with gr.Tabs(selected='tab_img_gallery') as gallery: with gr.Tab('Image to 3D Gallery', id='tab_img_gallery', visible=not MV_MODE) as tab_gi: with gr.Row(): gr.Examples(examples=example_is, inputs=[image], label=None, examples_per_page=18) with gr.Tab('Text to 3D Gallery', id='tab_txt_gallery', visible=HAS_T2I and not MV_MODE) as tab_gt: with gr.Row(): gr.Examples(examples=example_ts, inputs=[caption], label=None, examples_per_page=18) with gr.Tab('MultiView to 3D Gallery', id='tab_mv_gallery', visible=MV_MODE) as tab_mv: with gr.Row(): gr.Examples(examples=example_mvs, inputs=[mv_image_front, mv_image_back, mv_image_left, mv_image_right], label=None, examples_per_page=6) gr.HTML(f"""
Activated Model - Shape Generation ({args.model_path}/{args.subfolder}) ; Texture Generation ({'Hunyuan3D-2' if HAS_TEXTUREGEN else 'Unavailable'})
""") if not HAS_TEXTUREGEN: gr.HTML("""
Warning: Texture synthesis is disable due to missing requirements, please install requirements following README.mdto activate it.
""") if not args.enable_t23d: gr.HTML("""
Warning: Text to 3D is disable. To activate it, please run `python gradio_app.py --enable_t23d`.
""") tab_ip.select(fn=lambda: gr.update(selected='tab_img_gallery'), outputs=gallery) if HAS_T2I: tab_tp.select(fn=lambda: gr.update(selected='tab_txt_gallery'), outputs=gallery) btn.click( shape_generation, inputs=[ caption, image, mv_image_front, mv_image_back, mv_image_left, mv_image_right, num_steps, cfg_scale, seed, octree_resolution, check_box_rembg, num_chunks, randomize_seed, ], outputs=[file_out, html_gen_mesh, stats, seed] ).then( lambda: (gr.update(visible=False, value=False), gr.update(interactive=True), gr.update(interactive=True), gr.update(interactive=False)), outputs=[export_texture, reduce_face, confirm_export, file_export], ).then( lambda: gr.update(selected='gen_mesh_panel'), outputs=[tabs_output], ) btn_all.click( generation_all, inputs=[ caption, image, mv_image_front, mv_image_back, mv_image_left, mv_image_right, num_steps, cfg_scale, seed, octree_resolution, check_box_rembg, num_chunks, randomize_seed, ], outputs=[file_out, file_out2, html_gen_mesh, stats, seed] ).then( lambda: (gr.update(visible=True, value=True), gr.update(interactive=False), gr.update(interactive=True), gr.update(interactive=False)), outputs=[export_texture, reduce_face, confirm_export, file_export], ).then( lambda: gr.update(selected='gen_mesh_panel'), outputs=[tabs_output], ) def on_gen_mode_change(value): if value == 'Turbo': return gr.update(value=5) elif value == 'Fast': return gr.update(value=10) else: return gr.update(value=30) gen_mode.change(on_gen_mode_change, inputs=[gen_mode], outputs=[num_steps]) def on_decode_mode_change(value): if value == 'Low': return gr.update(value=196) elif value == 'Standard': return gr.update(value=256) else: return gr.update(value=384) decode_mode.change(on_decode_mode_change, inputs=[decode_mode], outputs=[octree_resolution]) def on_export_click(file_out, file_out2, file_type, reduce_face, export_texture, target_face_num): if file_out is None: raise gr.Error('Please generate a mesh first.') print(f'exporting {file_out}') print(f'reduce face to {target_face_num}') if export_texture: mesh = trimesh.load(file_out2) save_folder = gen_save_folder() path = export_mesh(mesh, save_folder, textured=True, type=file_type) # for preview save_folder = gen_save_folder() _ = export_mesh(mesh, save_folder, textured=True) model_viewer_html = build_model_viewer_html(save_folder, height=HTML_HEIGHT, width=HTML_WIDTH, textured=True) else: mesh = trimesh.load(file_out) mesh = floater_remove_worker(mesh) mesh = degenerate_face_remove_worker(mesh) if reduce_face: mesh = face_reduce_worker(mesh, target_face_num) save_folder = gen_save_folder() path = export_mesh(mesh, save_folder, textured=False, type=file_type) # for preview save_folder = gen_save_folder() _ = export_mesh(mesh, save_folder, textured=False) model_viewer_html = build_model_viewer_html(save_folder, height=HTML_HEIGHT, width=HTML_WIDTH, textured=False) print(f'export to {path}') return model_viewer_html, gr.update(value=path, interactive=True) confirm_export.click( lambda: gr.update(selected='export_mesh_panel'), outputs=[tabs_output], ).then( on_export_click, inputs=[file_out, file_out2, file_type, reduce_face, export_texture, target_face_num], outputs=[html_export_mesh, file_export] ) return demo if __name__ == '__main__': import argparse parser = argparse.ArgumentParser() parser.add_argument("--model_path", type=str, default='tencent/Hunyuan3D-2mini') parser.add_argument("--subfolder", type=str, default='hunyuan3d-dit-v2-mini-turbo') parser.add_argument("--texgen_model_path", type=str, default='tencent/Hunyuan3D-2') parser.add_argument('--port', type=int, default=8080) parser.add_argument('--host', type=str, default='0.0.0.0') parser.add_argument('--device', type=str, default='cuda') parser.add_argument('--mc_algo', type=str, default='mc') parser.add_argument('--cache-path', type=str, default='gradio_cache') parser.add_argument('--enable_t23d', action='store_true') parser.add_argument('--disable_tex', action='store_true') parser.add_argument('--enable_flashvdm', action='store_true') parser.add_argument('--compile', action='store_true') parser.add_argument('--low_vram_mode', action='store_true') args = parser.parse_args() SAVE_DIR = args.cache_path os.makedirs(SAVE_DIR, exist_ok=True) CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) MV_MODE = 'mv' in args.model_path TURBO_MODE = 'turbo' in args.subfolder HTML_HEIGHT = 690 if MV_MODE else 650 HTML_WIDTH = 500 HTML_OUTPUT_PLACEHOLDER = f"""

Welcome to Hunyuan3D!

No mesh here.

""" INPUT_MESH_HTML = """
""" example_is = get_example_img_list() example_ts = get_example_txt_list() example_mvs = get_example_mv_list() SUPPORTED_FORMATS = ['glb', 'obj', 'ply', 'stl'] HAS_TEXTUREGEN = False if not args.disable_tex: try: from hy3dgen.texgen import Hunyuan3DPaintPipeline texgen_worker = Hunyuan3DPaintPipeline.from_pretrained(args.texgen_model_path) if args.low_vram_mode: texgen_worker.enable_model_cpu_offload() # Not help much, ignore for now. # if args.compile: # texgen_worker.models['delight_model'].pipeline.unet.compile() # texgen_worker.models['delight_model'].pipeline.vae.compile() # texgen_worker.models['multiview_model'].pipeline.unet.compile() # texgen_worker.models['multiview_model'].pipeline.vae.compile() HAS_TEXTUREGEN = True except Exception as e: print(e) print("Failed to load texture generator.") print('Please try to install requirements by following README.md') HAS_TEXTUREGEN = False HAS_T2I = True if args.enable_t23d: from hy3dgen.text2image import HunyuanDiTPipeline t2i_worker = HunyuanDiTPipeline('Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled', device=args.device) HAS_T2I = True from hy3dgen.shapegen import FaceReducer, FloaterRemover, DegenerateFaceRemover, MeshSimplifier, \ Hunyuan3DDiTFlowMatchingPipeline from hy3dgen.shapegen.pipelines import export_to_trimesh from hy3dgen.rembg import BackgroundRemover rmbg_worker = BackgroundRemover() i23d_worker = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained( args.model_path, subfolder=args.subfolder, use_safetensors=True, device=args.device, ) if args.enable_flashvdm: mc_algo = 'mc' if args.device in ['cpu', 'mps'] else args.mc_algo i23d_worker.enable_flashvdm(mc_algo=mc_algo) if args.compile: i23d_worker.compile() floater_remove_worker = FloaterRemover() degenerate_face_remove_worker = DegenerateFaceRemover() face_reduce_worker = FaceReducer() # https://discuss.huggingface.co/t/how-to-serve-an-html-file/33921/2 # create a FastAPI app app = FastAPI() # create a static directory to store the static files static_dir = Path(SAVE_DIR).absolute() static_dir.mkdir(parents=True, exist_ok=True) app.mount("/static", StaticFiles(directory=static_dir, html=True), name="static") shutil.copytree('./assets/env_maps', os.path.join(static_dir, 'env_maps'), dirs_exist_ok=True) if args.low_vram_mode: torch.cuda.empty_cache() demo = build_app() app = gr.mount_gradio_app(app, demo, path="/") uvicorn.run(app, host=args.host, port=args.port, workers=1) ================================================ FILE: hy3dgen/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. ================================================ FILE: hy3dgen/rembg.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from PIL import Image from rembg import remove, new_session class BackgroundRemover(): def __init__(self): self.session = new_session() def __call__(self, image: Image.Image): output = remove(image, session=self.session, bgcolor=[255, 255, 255, 0]) return output ================================================ FILE: hy3dgen/shapegen/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from .pipelines import Hunyuan3DDiTPipeline, Hunyuan3DDiTFlowMatchingPipeline from .postprocessors import FaceReducer, FloaterRemover, DegenerateFaceRemover, MeshSimplifier from .preprocessors import ImageProcessorV2, IMAGE_PROCESSORS, DEFAULT_IMAGEPROCESSOR ================================================ FILE: hy3dgen/shapegen/models/__init__.py ================================================ # Open Source Model Licensed under the Apache License Version 2.0 # and Other Licenses of the Third-Party Components therein: # The below Model in this distribution may have been modified by THL A29 Limited # ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. # Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. # The below software and/or models in this distribution may have been # modified by THL A29 Limited ("Tencent Modifications"). # All Tencent Modifications are Copyright (C) THL A29 Limited. # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from .autoencoders import ShapeVAE from .conditioner import DualImageEncoder, SingleImageEncoder, DinoImageEncoder, CLIPImageEncoder from .denoisers import Hunyuan3DDiT ================================================ FILE: hy3dgen/shapegen/models/autoencoders/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from .attention_blocks import CrossAttentionDecoder from .attention_processors import FlashVDMCrossAttentionProcessor, CrossAttentionProcessor, \ FlashVDMTopMCrossAttentionProcessor from .model import ShapeVAE, VectsetVAE from .surface_extractors import SurfaceExtractors, MCSurfaceExtractor, DMCSurfaceExtractor, Latent2MeshOutput from .volume_decoders import HierarchicalVolumeDecoding, FlashVDMVolumeDecoding, VanillaVolumeDecoder ================================================ FILE: hy3dgen/shapegen/models/autoencoders/attention_blocks.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import os from typing import Optional, Union, List import torch import torch.nn as nn from einops import rearrange from torch import Tensor from .attention_processors import CrossAttentionProcessor from ...utils import logger scaled_dot_product_attention = nn.functional.scaled_dot_product_attention if os.environ.get('USE_SAGEATTN', '0') == '1': try: from sageattention import sageattn except ImportError: raise ImportError('Please install the package "sageattention" to use this USE_SAGEATTN.') scaled_dot_product_attention = sageattn class FourierEmbedder(nn.Module): """The sin/cosine positional embedding. Given an input tensor `x` of shape [n_batch, ..., c_dim], it converts each feature dimension of `x[..., i]` into: [ sin(x[..., i]), sin(f_1*x[..., i]), sin(f_2*x[..., i]), ... sin(f_N * x[..., i]), cos(x[..., i]), cos(f_1*x[..., i]), cos(f_2*x[..., i]), ... cos(f_N * x[..., i]), x[..., i] # only present if include_input is True. ], here f_i is the frequency. Denote the space is [0 / num_freqs, 1 / num_freqs, 2 / num_freqs, 3 / num_freqs, ..., (num_freqs - 1) / num_freqs]. If logspace is True, then the frequency f_i is [2^(0 / num_freqs), ..., 2^(i / num_freqs), ...]; Otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]. Args: num_freqs (int): the number of frequencies, default is 6; logspace (bool): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1)]; input_dim (int): the input dimension, default is 3; include_input (bool): include the input tensor or not, default is True. Attributes: frequencies (torch.Tensor): If logspace is True, then the frequency f_i is [..., 2^(i / num_freqs), ...], otherwise, the frequencies are linearly spaced between [1.0, 2^(num_freqs - 1); out_dim (int): the embedding size, if include_input is True, it is input_dim * (num_freqs * 2 + 1), otherwise, it is input_dim * num_freqs * 2. """ def __init__(self, num_freqs: int = 6, logspace: bool = True, input_dim: int = 3, include_input: bool = True, include_pi: bool = True) -> None: """The initialization""" super().__init__() if logspace: frequencies = 2.0 ** torch.arange( num_freqs, dtype=torch.float32 ) else: frequencies = torch.linspace( 1.0, 2.0 ** (num_freqs - 1), num_freqs, dtype=torch.float32 ) if include_pi: frequencies *= torch.pi self.register_buffer("frequencies", frequencies, persistent=False) self.include_input = include_input self.num_freqs = num_freqs self.out_dim = self.get_dims(input_dim) def get_dims(self, input_dim): temp = 1 if self.include_input or self.num_freqs == 0 else 0 out_dim = input_dim * (self.num_freqs * 2 + temp) return out_dim def forward(self, x: torch.Tensor) -> torch.Tensor: """ Forward process. Args: x: tensor of shape [..., dim] Returns: embedding: an embedding of `x` of shape [..., dim * (num_freqs * 2 + temp)] where temp is 1 if include_input is True and 0 otherwise. """ if self.num_freqs > 0: embed = (x[..., None].contiguous() * self.frequencies).view(*x.shape[:-1], -1) if self.include_input: return torch.cat((x, embed.sin(), embed.cos()), dim=-1) else: return torch.cat((embed.sin(), embed.cos()), dim=-1) else: return x class DropPath(nn.Module): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). """ def __init__(self, drop_prob: float = 0., scale_by_keep: bool = True): super(DropPath, self).__init__() self.drop_prob = drop_prob self.scale_by_keep = scale_by_keep def forward(self, x): """Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks). This is the same as the DropConnect impl I created for EfficientNet, etc networks, however, the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper... See discussion: https://github.com/tensorflow/tpu/issues/494#issuecomment-532968956 ... I've opted for changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use 'survival rate' as the argument. """ if self.drop_prob == 0. or not self.training: return x keep_prob = 1 - self.drop_prob shape = (x.shape[0],) + (1,) * (x.ndim - 1) # work with diff dim tensors, not just 2D ConvNets random_tensor = x.new_empty(shape).bernoulli_(keep_prob) if keep_prob > 0.0 and self.scale_by_keep: random_tensor.div_(keep_prob) return x * random_tensor def extra_repr(self): return f'drop_prob={round(self.drop_prob, 3):0.3f}' class MLP(nn.Module): def __init__( self, *, width: int, expand_ratio: int = 4, output_width: int = None, drop_path_rate: float = 0.0 ): super().__init__() self.width = width self.c_fc = nn.Linear(width, width * expand_ratio) self.c_proj = nn.Linear(width * expand_ratio, output_width if output_width is not None else width) self.gelu = nn.GELU() self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() def forward(self, x): return self.drop_path(self.c_proj(self.gelu(self.c_fc(x)))) class QKVMultiheadCrossAttention(nn.Module): def __init__( self, *, heads: int, n_data: Optional[int] = None, width=None, qk_norm=False, norm_layer=nn.LayerNorm ): super().__init__() self.heads = heads self.n_data = n_data self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.attn_processor = CrossAttentionProcessor() def forward(self, q, kv): _, n_ctx, _ = q.shape bs, n_data, width = kv.shape attn_ch = width // self.heads // 2 q = q.view(bs, n_ctx, self.heads, -1) kv = kv.view(bs, n_data, self.heads, -1) k, v = torch.split(kv, attn_ch, dim=-1) q = self.q_norm(q) k = self.k_norm(k) q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v)) out = self.attn_processor(self, q, k, v) out = out.transpose(1, 2).reshape(bs, n_ctx, -1) return out class MultiheadCrossAttention(nn.Module): def __init__( self, *, width: int, heads: int, qkv_bias: bool = True, n_data: Optional[int] = None, data_width: Optional[int] = None, norm_layer=nn.LayerNorm, qk_norm: bool = False, kv_cache: bool = False, ): super().__init__() self.n_data = n_data self.width = width self.heads = heads self.data_width = width if data_width is None else data_width self.c_q = nn.Linear(width, width, bias=qkv_bias) self.c_kv = nn.Linear(self.data_width, width * 2, bias=qkv_bias) self.c_proj = nn.Linear(width, width) self.attention = QKVMultiheadCrossAttention( heads=heads, n_data=n_data, width=width, norm_layer=norm_layer, qk_norm=qk_norm ) self.kv_cache = kv_cache self.data = None def forward(self, x, data): x = self.c_q(x) if self.kv_cache: if self.data is None: self.data = self.c_kv(data) logger.info('Save kv cache,this should be called only once for one mesh') data = self.data else: data = self.c_kv(data) x = self.attention(x, data) x = self.c_proj(x) return x class ResidualCrossAttentionBlock(nn.Module): def __init__( self, *, n_data: Optional[int] = None, width: int, heads: int, mlp_expand_ratio: int = 4, data_width: Optional[int] = None, qkv_bias: bool = True, norm_layer=nn.LayerNorm, qk_norm: bool = False ): super().__init__() if data_width is None: data_width = width self.attn = MultiheadCrossAttention( n_data=n_data, width=width, heads=heads, data_width=data_width, qkv_bias=qkv_bias, norm_layer=norm_layer, qk_norm=qk_norm ) self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) self.ln_2 = norm_layer(data_width, elementwise_affine=True, eps=1e-6) self.ln_3 = norm_layer(width, elementwise_affine=True, eps=1e-6) self.mlp = MLP(width=width, expand_ratio=mlp_expand_ratio) def forward(self, x: torch.Tensor, data: torch.Tensor): x = x + self.attn(self.ln_1(x), self.ln_2(data)) x = x + self.mlp(self.ln_3(x)) return x class QKVMultiheadAttention(nn.Module): def __init__( self, *, heads: int, n_ctx: int, width=None, qk_norm=False, norm_layer=nn.LayerNorm ): super().__init__() self.heads = heads self.n_ctx = n_ctx self.q_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.k_norm = norm_layer(width // heads, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() def forward(self, qkv): bs, n_ctx, width = qkv.shape attn_ch = width // self.heads // 3 qkv = qkv.view(bs, n_ctx, self.heads, -1) q, k, v = torch.split(qkv, attn_ch, dim=-1) q = self.q_norm(q) k = self.k_norm(k) q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.heads), (q, k, v)) out = scaled_dot_product_attention(q, k, v).transpose(1, 2).reshape(bs, n_ctx, -1) return out class MultiheadAttention(nn.Module): def __init__( self, *, n_ctx: int, width: int, heads: int, qkv_bias: bool, norm_layer=nn.LayerNorm, qk_norm: bool = False, drop_path_rate: float = 0.0 ): super().__init__() self.n_ctx = n_ctx self.width = width self.heads = heads self.c_qkv = nn.Linear(width, width * 3, bias=qkv_bias) self.c_proj = nn.Linear(width, width) self.attention = QKVMultiheadAttention( heads=heads, n_ctx=n_ctx, width=width, norm_layer=norm_layer, qk_norm=qk_norm ) self.drop_path = DropPath(drop_path_rate) if drop_path_rate > 0. else nn.Identity() def forward(self, x): x = self.c_qkv(x) x = self.attention(x) x = self.drop_path(self.c_proj(x)) return x class ResidualAttentionBlock(nn.Module): def __init__( self, *, n_ctx: int, width: int, heads: int, qkv_bias: bool = True, norm_layer=nn.LayerNorm, qk_norm: bool = False, drop_path_rate: float = 0.0, ): super().__init__() self.attn = MultiheadAttention( n_ctx=n_ctx, width=width, heads=heads, qkv_bias=qkv_bias, norm_layer=norm_layer, qk_norm=qk_norm, drop_path_rate=drop_path_rate ) self.ln_1 = norm_layer(width, elementwise_affine=True, eps=1e-6) self.mlp = MLP(width=width, drop_path_rate=drop_path_rate) self.ln_2 = norm_layer(width, elementwise_affine=True, eps=1e-6) def forward(self, x: torch.Tensor): x = x + self.attn(self.ln_1(x)) x = x + self.mlp(self.ln_2(x)) return x class Transformer(nn.Module): def __init__( self, *, n_ctx: int, width: int, layers: int, heads: int, qkv_bias: bool = True, norm_layer=nn.LayerNorm, qk_norm: bool = False, drop_path_rate: float = 0.0 ): super().__init__() self.n_ctx = n_ctx self.width = width self.layers = layers self.resblocks = nn.ModuleList( [ ResidualAttentionBlock( n_ctx=n_ctx, width=width, heads=heads, qkv_bias=qkv_bias, norm_layer=norm_layer, qk_norm=qk_norm, drop_path_rate=drop_path_rate ) for _ in range(layers) ] ) def forward(self, x: torch.Tensor): for block in self.resblocks: x = block(x) return x class CrossAttentionDecoder(nn.Module): def __init__( self, *, num_latents: int, out_channels: int, fourier_embedder: FourierEmbedder, width: int, heads: int, mlp_expand_ratio: int = 4, downsample_ratio: int = 1, enable_ln_post: bool = True, qkv_bias: bool = True, qk_norm: bool = False, label_type: str = "binary" ): super().__init__() self.enable_ln_post = enable_ln_post self.fourier_embedder = fourier_embedder self.downsample_ratio = downsample_ratio self.query_proj = nn.Linear(self.fourier_embedder.out_dim, width) if self.downsample_ratio != 1: self.latents_proj = nn.Linear(width * downsample_ratio, width) if self.enable_ln_post == False: qk_norm = False self.cross_attn_decoder = ResidualCrossAttentionBlock( n_data=num_latents, width=width, mlp_expand_ratio=mlp_expand_ratio, heads=heads, qkv_bias=qkv_bias, qk_norm=qk_norm ) if self.enable_ln_post: self.ln_post = nn.LayerNorm(width) self.output_proj = nn.Linear(width, out_channels) self.label_type = label_type self.count = 0 def set_cross_attention_processor(self, processor): self.cross_attn_decoder.attn.attention.attn_processor = processor def set_default_cross_attention_processor(self): self.cross_attn_decoder.attn.attention.attn_processor = CrossAttentionProcessor def forward(self, queries=None, query_embeddings=None, latents=None): if query_embeddings is None: query_embeddings = self.query_proj(self.fourier_embedder(queries).to(latents.dtype)) self.count += query_embeddings.shape[1] if self.downsample_ratio != 1: latents = self.latents_proj(latents) x = self.cross_attn_decoder(query_embeddings, latents) if self.enable_ln_post: x = self.ln_post(x) occ = self.output_proj(x) return occ def fps( src: torch.Tensor, batch: Optional[Tensor] = None, ratio: Optional[Union[Tensor, float]] = None, random_start: bool = True, batch_size: Optional[int] = None, ptr: Optional[Union[Tensor, List[int]]] = None, ): src = src.float() from torch_cluster import fps as fps_fn output = fps_fn(src, batch, ratio, random_start, batch_size, ptr) return output class PointCrossAttentionEncoder(nn.Module): def __init__( self, *, num_latents: int, downsample_ratio: float, pc_size: int, pc_sharpedge_size: int, fourier_embedder: FourierEmbedder, point_feats: int, width: int, heads: int, layers: int, normal_pe: bool = False, qkv_bias: bool = True, use_ln_post: bool = False, use_checkpoint: bool = False, qk_norm: bool = False ): super().__init__() self.use_checkpoint = use_checkpoint self.num_latents = num_latents self.downsample_ratio = downsample_ratio self.point_feats = point_feats self.normal_pe = normal_pe if pc_sharpedge_size == 0: print( f'PointCrossAttentionEncoder INFO: pc_sharpedge_size is not given, using pc_size as pc_sharpedge_size') else: print( f'PointCrossAttentionEncoder INFO: pc_sharpedge_size is given, using pc_size={pc_size}, pc_sharpedge_size={pc_sharpedge_size}') self.pc_size = pc_size self.pc_sharpedge_size = pc_sharpedge_size self.fourier_embedder = fourier_embedder self.input_proj = nn.Linear(self.fourier_embedder.out_dim + point_feats, width) self.cross_attn = ResidualCrossAttentionBlock( width=width, heads=heads, qkv_bias=qkv_bias, qk_norm=qk_norm ) self.self_attn = None if layers > 0: self.self_attn = Transformer( n_ctx=num_latents, width=width, layers=layers, heads=heads, qkv_bias=qkv_bias, qk_norm=qk_norm ) if use_ln_post: self.ln_post = nn.LayerNorm(width) else: self.ln_post = None def sample_points_and_latents(self, pc: torch.FloatTensor, feats: Optional[torch.FloatTensor] = None): B, N, D = pc.shape num_pts = self.num_latents * self.downsample_ratio # Compute number of latents num_latents = int(num_pts / self.downsample_ratio) # Compute the number of random and sharpedge latents num_random_query = self.pc_size / (self.pc_size + self.pc_sharpedge_size) * num_latents num_sharpedge_query = num_latents - num_random_query # Split random and sharpedge surface points random_pc, sharpedge_pc = torch.split(pc, [self.pc_size, self.pc_sharpedge_size], dim=1) assert random_pc.shape[1] <= self.pc_size, "Random surface points size must be less than or equal to pc_size" assert sharpedge_pc.shape[ 1] <= self.pc_sharpedge_size, "Sharpedge surface points size must be less than or equal to pc_sharpedge_size" # Randomly select random surface points and random query points input_random_pc_size = int(num_random_query * self.downsample_ratio) random_query_ratio = num_random_query / input_random_pc_size idx_random_pc = torch.randperm(random_pc.shape[1], device=random_pc.device)[:input_random_pc_size] input_random_pc = random_pc[:, idx_random_pc, :] flatten_input_random_pc = input_random_pc.view(B * input_random_pc_size, D) N_down = int(flatten_input_random_pc.shape[0] / B) batch_down = torch.arange(B).to(pc.device) batch_down = torch.repeat_interleave(batch_down, N_down) idx_query_random = fps(flatten_input_random_pc, batch_down, ratio=random_query_ratio) query_random_pc = flatten_input_random_pc[idx_query_random].view(B, -1, D) # Randomly select sharpedge surface points and sharpedge query points input_sharpedge_pc_size = int(num_sharpedge_query * self.downsample_ratio) if input_sharpedge_pc_size == 0: input_sharpedge_pc = torch.zeros(B, 0, D, dtype=input_random_pc.dtype).to(pc.device) query_sharpedge_pc = torch.zeros(B, 0, D, dtype=query_random_pc.dtype).to(pc.device) else: sharpedge_query_ratio = num_sharpedge_query / input_sharpedge_pc_size idx_sharpedge_pc = torch.randperm(sharpedge_pc.shape[1], device=sharpedge_pc.device)[ :input_sharpedge_pc_size] input_sharpedge_pc = sharpedge_pc[:, idx_sharpedge_pc, :] flatten_input_sharpedge_surface_points = input_sharpedge_pc.view(B * input_sharpedge_pc_size, D) N_down = int(flatten_input_sharpedge_surface_points.shape[0] / B) batch_down = torch.arange(B).to(pc.device) batch_down = torch.repeat_interleave(batch_down, N_down) idx_query_sharpedge = fps(flatten_input_sharpedge_surface_points, batch_down, ratio=sharpedge_query_ratio) query_sharpedge_pc = flatten_input_sharpedge_surface_points[idx_query_sharpedge].view(B, -1, D) # Concatenate random and sharpedge surface points and query points query_pc = torch.cat([query_random_pc, query_sharpedge_pc], dim=1) input_pc = torch.cat([input_random_pc, input_sharpedge_pc], dim=1) # PE query = self.fourier_embedder(query_pc) data = self.fourier_embedder(input_pc) # Concat normal if given if self.point_feats != 0: random_surface_feats, sharpedge_surface_feats = torch.split(feats, [self.pc_size, self.pc_sharpedge_size], dim=1) input_random_surface_feats = random_surface_feats[:, idx_random_pc, :] flatten_input_random_surface_feats = input_random_surface_feats.view(B * input_random_pc_size, -1) query_random_feats = flatten_input_random_surface_feats[idx_query_random].view(B, -1, flatten_input_random_surface_feats.shape[ -1]) if input_sharpedge_pc_size == 0: input_sharpedge_surface_feats = torch.zeros(B, 0, self.point_feats, dtype=input_random_surface_feats.dtype).to(pc.device) query_sharpedge_feats = torch.zeros(B, 0, self.point_feats, dtype=query_random_feats.dtype).to( pc.device) else: input_sharpedge_surface_feats = sharpedge_surface_feats[:, idx_sharpedge_pc, :] flatten_input_sharpedge_surface_feats = input_sharpedge_surface_feats.view(B * input_sharpedge_pc_size, -1) query_sharpedge_feats = flatten_input_sharpedge_surface_feats[idx_query_sharpedge].view(B, -1, flatten_input_sharpedge_surface_feats.shape[ -1]) query_feats = torch.cat([query_random_feats, query_sharpedge_feats], dim=1) input_feats = torch.cat([input_random_surface_feats, input_sharpedge_surface_feats], dim=1) if self.normal_pe: query_normal_pe = self.fourier_embedder(query_feats[..., :3]) input_normal_pe = self.fourier_embedder(input_feats[..., :3]) query_feats = torch.cat([query_normal_pe, query_feats[..., 3:]], dim=-1) input_feats = torch.cat([input_normal_pe, input_feats[..., 3:]], dim=-1) query = torch.cat([query, query_feats], dim=-1) data = torch.cat([data, input_feats], dim=-1) if input_sharpedge_pc_size == 0: query_sharpedge_pc = torch.zeros(B, 1, D).to(pc.device) input_sharpedge_pc = torch.zeros(B, 1, D).to(pc.device) return query.view(B, -1, query.shape[-1]), data.view(B, -1, data.shape[-1]), [query_pc, input_pc, query_random_pc, input_random_pc, query_sharpedge_pc, input_sharpedge_pc] def forward(self, pc, feats): """ Args: pc (torch.FloatTensor): [B, N, 3] feats (torch.FloatTensor or None): [B, N, C] Returns: """ query, data, pc_infos = self.sample_points_and_latents(pc, feats) query = self.input_proj(query) query = query data = self.input_proj(data) data = data latents = self.cross_attn(query, data) if self.self_attn is not None: latents = self.self_attn(latents) if self.ln_post is not None: latents = self.ln_post(latents) return latents, pc_infos ================================================ FILE: hy3dgen/shapegen/models/autoencoders/attention_processors.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import os import torch import torch.nn.functional as F scaled_dot_product_attention = F.scaled_dot_product_attention if os.environ.get('CA_USE_SAGEATTN', '0') == '1': try: from sageattention import sageattn except ImportError: raise ImportError('Please install the package "sageattention" to use this USE_SAGEATTN.') scaled_dot_product_attention = sageattn class CrossAttentionProcessor: def __call__(self, attn, q, k, v): out = scaled_dot_product_attention(q, k, v) return out class FlashVDMCrossAttentionProcessor: def __init__(self, topk=None): self.topk = topk def __call__(self, attn, q, k, v): if k.shape[-2] == 3072: topk = 1024 elif k.shape[-2] == 512: topk = 256 else: topk = k.shape[-2] // 3 if self.topk is True: q1 = q[:, :, ::100, :] sim = q1 @ k.transpose(-1, -2) sim = torch.mean(sim, -2) topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1) topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1]) v0 = torch.gather(v, dim=-2, index=topk_ind) k0 = torch.gather(k, dim=-2, index=topk_ind) out = scaled_dot_product_attention(q, k0, v0) elif self.topk is False: out = scaled_dot_product_attention(q, k, v) else: idx, counts = self.topk start = 0 outs = [] for grid_coord, count in zip(idx, counts): end = start + count q_chunk = q[:, :, start:end, :] k0, v0 = self.select_topkv(q_chunk, k, v, topk) out = scaled_dot_product_attention(q_chunk, k0, v0) outs.append(out) start += count out = torch.cat(outs, dim=-2) self.topk = False return out def select_topkv(self, q_chunk, k, v, topk): q1 = q_chunk[:, :, ::50, :] sim = q1 @ k.transpose(-1, -2) sim = torch.mean(sim, -2) topk_ind = torch.topk(sim, dim=-1, k=topk).indices.squeeze(-2).unsqueeze(-1) topk_ind = topk_ind.expand(-1, -1, -1, v.shape[-1]) v0 = torch.gather(v, dim=-2, index=topk_ind) k0 = torch.gather(k, dim=-2, index=topk_ind) return k0, v0 class FlashVDMTopMCrossAttentionProcessor(FlashVDMCrossAttentionProcessor): def select_topkv(self, q_chunk, k, v, topk): q1 = q_chunk[:, :, ::30, :] sim = q1 @ k.transpose(-1, -2) # sim = sim.to(torch.float32) sim = sim.softmax(-1) sim = torch.mean(sim, 1) activated_token = torch.where(sim > 1e-6)[2] index = torch.unique(activated_token, return_counts=True)[0].unsqueeze(0).unsqueeze(0).unsqueeze(-1) index = index.expand(-1, v.shape[1], -1, v.shape[-1]) v0 = torch.gather(v, dim=-2, index=index) k0 = torch.gather(k, dim=-2, index=index) return k0, v0 ================================================ FILE: hy3dgen/shapegen/models/autoencoders/model.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import os from typing import Union, List import numpy as np import torch import torch.nn as nn import yaml from .attention_blocks import FourierEmbedder, Transformer, CrossAttentionDecoder, PointCrossAttentionEncoder from .surface_extractors import MCSurfaceExtractor, SurfaceExtractors from .volume_decoders import VanillaVolumeDecoder, FlashVDMVolumeDecoding, HierarchicalVolumeDecoding from ...utils import logger, synchronize_timer, smart_load_model class DiagonalGaussianDistribution(object): def __init__(self, parameters: Union[torch.Tensor, List[torch.Tensor]], deterministic=False, feat_dim=1): self.feat_dim = feat_dim self.parameters = parameters if isinstance(parameters, list): self.mean = parameters[0] self.logvar = parameters[1] else: self.mean, self.logvar = torch.chunk(parameters, 2, dim=feat_dim) self.logvar = torch.clamp(self.logvar, -30.0, 20.0) self.deterministic = deterministic self.std = torch.exp(0.5 * self.logvar) self.var = torch.exp(self.logvar) if self.deterministic: self.var = self.std = torch.zeros_like(self.mean) def sample(self): x = self.mean + self.std * torch.randn_like(self.mean) return x def kl(self, other=None, dims=(1, 2, 3)): if self.deterministic: return torch.Tensor([0.]) else: if other is None: return 0.5 * torch.mean(torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar, dim=dims) else: return 0.5 * torch.mean( torch.pow(self.mean - other.mean, 2) / other.var + self.var / other.var - 1.0 - self.logvar + other.logvar, dim=dims) def nll(self, sample, dims=(1, 2, 3)): if self.deterministic: return torch.Tensor([0.]) logtwopi = np.log(2.0 * np.pi) return 0.5 * torch.sum( logtwopi + self.logvar + torch.pow(sample - self.mean, 2) / self.var, dim=dims) def mode(self): return self.mean class VectsetVAE(nn.Module): @classmethod @synchronize_timer('VectsetVAE Model Loading') def from_single_file( cls, ckpt_path, config_path, device='cuda', dtype=torch.float16, use_safetensors=None, **kwargs, ): # load config with open(config_path, 'r') as f: config = yaml.safe_load(f) # load ckpt if use_safetensors: ckpt_path = ckpt_path.replace('.ckpt', '.safetensors') if not os.path.exists(ckpt_path): raise FileNotFoundError(f"Model file {ckpt_path} not found") logger.info(f"Loading model from {ckpt_path}") if use_safetensors: import safetensors.torch ckpt = safetensors.torch.load_file(ckpt_path, device='cpu') else: ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=True) model_kwargs = config['params'] model_kwargs.update(kwargs) model = cls(**model_kwargs) model.load_state_dict(ckpt, strict=False) model.to(device=device, dtype=dtype) return model @classmethod def from_pretrained( cls, model_path, device='cuda', dtype=torch.float16, use_safetensors=True, variant='fp16', subfolder='hunyuan3d-vae-v2-0', **kwargs, ): config_path, ckpt_path = smart_load_model( model_path, subfolder=subfolder, use_safetensors=use_safetensors, variant=variant ) return cls.from_single_file( ckpt_path, config_path, device=device, dtype=dtype, use_safetensors=use_safetensors, **kwargs ) def init_from_ckpt(self, path, ignore_keys=()): state_dict = torch.load(path, map_location="cpu") state_dict = state_dict.get("state_dict", state_dict) keys = list(state_dict.keys()) for k in keys: for ik in ignore_keys: if k.startswith(ik): print("Deleting key {} from state_dict.".format(k)) del state_dict[k] missing, unexpected = self.load_state_dict(state_dict, strict=False) print(f"Restored from {path} with {len(missing)} missing and {len(unexpected)} unexpected keys") if len(missing) > 0: print(f"Missing Keys: {missing}") print(f"Unexpected Keys: {unexpected}") def __init__( self, volume_decoder=None, surface_extractor=None ): super().__init__() if volume_decoder is None: volume_decoder = VanillaVolumeDecoder() if surface_extractor is None: surface_extractor = MCSurfaceExtractor() self.volume_decoder = volume_decoder self.surface_extractor = surface_extractor def latents2mesh(self, latents: torch.FloatTensor, **kwargs): with synchronize_timer('Volume decoding'): grid_logits = self.volume_decoder(latents, self.geo_decoder, **kwargs) with synchronize_timer('Surface extraction'): outputs = self.surface_extractor(grid_logits, **kwargs) return outputs def enable_flashvdm_decoder( self, enabled: bool = True, adaptive_kv_selection=True, topk_mode='mean', mc_algo='dmc', ): if enabled: if adaptive_kv_selection: self.volume_decoder = FlashVDMVolumeDecoding(topk_mode) else: self.volume_decoder = HierarchicalVolumeDecoding() if mc_algo not in SurfaceExtractors.keys(): raise ValueError(f'Unsupported mc_algo {mc_algo}, available: {list(SurfaceExtractors.keys())}') self.surface_extractor = SurfaceExtractors[mc_algo]() else: self.volume_decoder = VanillaVolumeDecoder() self.surface_extractor = MCSurfaceExtractor() class ShapeVAE(VectsetVAE): def __init__( self, *, num_latents: int, embed_dim: int, width: int, heads: int, num_decoder_layers: int, num_encoder_layers: int = 8, pc_size: int = 5120, pc_sharpedge_size: int = 5120, point_feats: int = 3, downsample_ratio: int = 20, geo_decoder_downsample_ratio: int = 1, geo_decoder_mlp_expand_ratio: int = 4, geo_decoder_ln_post: bool = True, num_freqs: int = 8, include_pi: bool = True, qkv_bias: bool = True, qk_norm: bool = False, label_type: str = "binary", drop_path_rate: float = 0.0, scale_factor: float = 1.0, use_ln_post: bool = True, ckpt_path=None ): super().__init__() self.geo_decoder_ln_post = geo_decoder_ln_post self.downsample_ratio = downsample_ratio self.fourier_embedder = FourierEmbedder(num_freqs=num_freqs, include_pi=include_pi) self.encoder = PointCrossAttentionEncoder( fourier_embedder=self.fourier_embedder, num_latents=num_latents, downsample_ratio=self.downsample_ratio, pc_size=pc_size, pc_sharpedge_size=pc_sharpedge_size, point_feats=point_feats, width=width, heads=heads, layers=num_encoder_layers, qkv_bias=qkv_bias, use_ln_post=use_ln_post, qk_norm=qk_norm ) self.pre_kl = nn.Linear(width, embed_dim * 2) self.post_kl = nn.Linear(embed_dim, width) self.transformer = Transformer( n_ctx=num_latents, width=width, layers=num_decoder_layers, heads=heads, qkv_bias=qkv_bias, qk_norm=qk_norm, drop_path_rate=drop_path_rate ) self.geo_decoder = CrossAttentionDecoder( fourier_embedder=self.fourier_embedder, out_channels=1, num_latents=num_latents, mlp_expand_ratio=geo_decoder_mlp_expand_ratio, downsample_ratio=geo_decoder_downsample_ratio, enable_ln_post=self.geo_decoder_ln_post, width=width // geo_decoder_downsample_ratio, heads=heads // geo_decoder_downsample_ratio, qkv_bias=qkv_bias, qk_norm=qk_norm, label_type=label_type, ) self.scale_factor = scale_factor self.latent_shape = (num_latents, embed_dim) if ckpt_path is not None: self.init_from_ckpt(ckpt_path) def forward(self, latents): latents = self.post_kl(latents) latents = self.transformer(latents) return latents def encode(self, surface, sample_posterior=True): pc, feats = surface[:, :, :3], surface[:, :, 3:] latents, _ = self.encoder(pc, feats) moments = self.pre_kl(latents) posterior = DiagonalGaussianDistribution(moments, feat_dim=-1) if sample_posterior: latents = posterior.sample() else: latents = posterior.mode() return latents def decode(self, latents): latents = self.post_kl(latents) latents = self.transformer(latents) return latents ================================================ FILE: hy3dgen/shapegen/models/autoencoders/surface_extractors.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from typing import Union, Tuple, List import numpy as np import torch from skimage import measure class Latent2MeshOutput: def __init__(self, mesh_v=None, mesh_f=None): self.mesh_v = mesh_v self.mesh_f = mesh_f def center_vertices(vertices): """Translate the vertices so that bounding box is centered at zero.""" vert_min = vertices.min(dim=0)[0] vert_max = vertices.max(dim=0)[0] vert_center = 0.5 * (vert_min + vert_max) return vertices - vert_center class SurfaceExtractor: def _compute_box_stat(self, bounds: Union[Tuple[float], List[float], float], octree_resolution: int): if isinstance(bounds, float): bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6]) bbox_size = bbox_max - bbox_min grid_size = [int(octree_resolution) + 1, int(octree_resolution) + 1, int(octree_resolution) + 1] return grid_size, bbox_min, bbox_size def run(self, *args, **kwargs): return NotImplementedError def __call__(self, grid_logits, **kwargs): outputs = [] for i in range(grid_logits.shape[0]): try: vertices, faces = self.run(grid_logits[i], **kwargs) vertices = vertices.astype(np.float32) faces = np.ascontiguousarray(faces) outputs.append(Latent2MeshOutput(mesh_v=vertices, mesh_f=faces)) except Exception: import traceback traceback.print_exc() outputs.append(None) return outputs class MCSurfaceExtractor(SurfaceExtractor): def run(self, grid_logit, *, mc_level, bounds, octree_resolution, **kwargs): vertices, faces, normals, _ = measure.marching_cubes( grid_logit.cpu().numpy(), mc_level, method="lewiner" ) grid_size, bbox_min, bbox_size = self._compute_box_stat(bounds, octree_resolution) vertices = vertices / grid_size * bbox_size + bbox_min return vertices, faces class DMCSurfaceExtractor(SurfaceExtractor): def run(self, grid_logit, *, octree_resolution, **kwargs): device = grid_logit.device if not hasattr(self, 'dmc'): try: from diso import DiffDMC except: raise ImportError("Please install diso via `pip install diso`, or set mc_algo to 'mc'") self.dmc = DiffDMC(dtype=torch.float32).to(device) sdf = -grid_logit / octree_resolution sdf = sdf.to(torch.float32).contiguous() verts, faces = self.dmc(sdf, deform=None, return_quads=False, normalize=True) verts = center_vertices(verts) vertices = verts.detach().cpu().numpy() faces = faces.detach().cpu().numpy()[:, ::-1] return vertices, faces SurfaceExtractors = { 'mc': MCSurfaceExtractor, 'dmc': DMCSurfaceExtractor, } ================================================ FILE: hy3dgen/shapegen/models/autoencoders/volume_decoders.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from typing import Union, Tuple, List, Callable import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from einops import repeat from tqdm import tqdm from .attention_blocks import CrossAttentionDecoder from .attention_processors import FlashVDMCrossAttentionProcessor, FlashVDMTopMCrossAttentionProcessor from ...utils import logger def extract_near_surface_volume_fn(input_tensor: torch.Tensor, alpha: float): device = input_tensor.device D = input_tensor.shape[0] signed_val = 0.0 # 添加偏移并处理无效值 val = input_tensor + alpha valid_mask = val > -9000 # 假设-9000是无效值 # 改进的邻居获取函数（保持维度一致） def get_neighbor(t, shift, axis): """根据指定轴进行位移并保持维度一致""" if shift == 0: return t.clone() # 确定填充轴（输入为[D, D, D]对应z,y,x轴） pad_dims = [0, 0, 0, 0, 0, 0] # 格式：[x前，x后，y前，y后，z前，z后] # 根据轴类型设置填充 if axis == 0: # x轴（最后一个维度） pad_idx = 0 if shift > 0 else 1 pad_dims[pad_idx] = abs(shift) elif axis == 1: # y轴（中间维度） pad_idx = 2 if shift > 0 else 3 pad_dims[pad_idx] = abs(shift) elif axis == 2: # z轴（第一个维度） pad_idx = 4 if shift > 0 else 5 pad_dims[pad_idx] = abs(shift) # 执行填充（添加batch和channel维度适配F.pad） padded = F.pad(t.unsqueeze(0).unsqueeze(0), pad_dims[::-1], mode='replicate') # 反转顺序适配F.pad # 构建动态切片索引 slice_dims = [slice(None)] * 3 # 初始化为全切片 if axis == 0: # x轴（dim=2） if shift > 0: slice_dims[0] = slice(shift, None) else: slice_dims[0] = slice(None, shift) elif axis == 1: # y轴（dim=1） if shift > 0: slice_dims[1] = slice(shift, None) else: slice_dims[1] = slice(None, shift) elif axis == 2: # z轴（dim=0） if shift > 0: slice_dims[2] = slice(shift, None) else: slice_dims[2] = slice(None, shift) # 应用切片并恢复维度 padded = padded.squeeze(0).squeeze(0) sliced = padded[slice_dims] return sliced # 获取各方向邻居（确保维度一致） left = get_neighbor(val, 1, axis=0) # x方向 right = get_neighbor(val, -1, axis=0) back = get_neighbor(val, 1, axis=1) # y方向 front = get_neighbor(val, -1, axis=1) down = get_neighbor(val, 1, axis=2) # z方向 up = get_neighbor(val, -1, axis=2) # 处理边界无效值（使用where保持维度一致） def safe_where(neighbor): return torch.where(neighbor > -9000, neighbor, val) left = safe_where(left) right = safe_where(right) back = safe_where(back) front = safe_where(front) down = safe_where(down) up = safe_where(up) # 计算符号一致性（转换为float32确保精度） sign = torch.sign(val.to(torch.float32)) neighbors_sign = torch.stack([ torch.sign(left.to(torch.float32)), torch.sign(right.to(torch.float32)), torch.sign(back.to(torch.float32)), torch.sign(front.to(torch.float32)), torch.sign(down.to(torch.float32)), torch.sign(up.to(torch.float32)) ], dim=0) # 检查所有符号是否一致 same_sign = torch.all(neighbors_sign == sign, dim=0) # 生成最终掩码 mask = (~same_sign).to(torch.int32) return mask * valid_mask.to(torch.int32) def generate_dense_grid_points( bbox_min: np.ndarray, bbox_max: np.ndarray, octree_resolution: int, indexing: str = "ij", ): length = bbox_max - bbox_min num_cells = octree_resolution x = np.linspace(bbox_min[0], bbox_max[0], int(num_cells) + 1, dtype=np.float32) y = np.linspace(bbox_min[1], bbox_max[1], int(num_cells) + 1, dtype=np.float32) z = np.linspace(bbox_min[2], bbox_max[2], int(num_cells) + 1, dtype=np.float32) [xs, ys, zs] = np.meshgrid(x, y, z, indexing=indexing) xyz = np.stack((xs, ys, zs), axis=-1) grid_size = [int(num_cells) + 1, int(num_cells) + 1, int(num_cells) + 1] return xyz, grid_size, length class VanillaVolumeDecoder: @torch.no_grad() def __call__( self, latents: torch.FloatTensor, geo_decoder: Callable, bounds: Union[Tuple[float], List[float], float] = 1.01, num_chunks: int = 10000, octree_resolution: int = None, enable_pbar: bool = True, **kwargs, ): device = latents.device dtype = latents.dtype batch_size = latents.shape[0] # 1. generate query points if isinstance(bounds, float): bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] bbox_min, bbox_max = np.array(bounds[0:3]), np.array(bounds[3:6]) xyz_samples, grid_size, length = generate_dense_grid_points( bbox_min=bbox_min, bbox_max=bbox_max, octree_resolution=octree_resolution, indexing="ij" ) xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype).contiguous().reshape(-1, 3) # 2. latents to 3d volume batch_logits = [] for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), desc=f"Volume Decoding", disable=not enable_pbar): chunk_queries = xyz_samples[start: start + num_chunks, :] chunk_queries = repeat(chunk_queries, "p c -> b p c", b=batch_size) logits = geo_decoder(queries=chunk_queries, latents=latents) batch_logits.append(logits) grid_logits = torch.cat(batch_logits, dim=1) grid_logits = grid_logits.view((batch_size, *grid_size)).float() return grid_logits class HierarchicalVolumeDecoding: @torch.no_grad() def __call__( self, latents: torch.FloatTensor, geo_decoder: Callable, bounds: Union[Tuple[float], List[float], float] = 1.01, num_chunks: int = 10000, mc_level: float = 0.0, octree_resolution: int = None, min_resolution: int = 63, enable_pbar: bool = True, **kwargs, ): device = latents.device dtype = latents.dtype resolutions = [] if octree_resolution < min_resolution: resolutions.append(octree_resolution) while octree_resolution >= min_resolution: resolutions.append(octree_resolution) octree_resolution = octree_resolution // 2 resolutions.reverse() # 1. generate query points if isinstance(bounds, float): bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] bbox_min = np.array(bounds[0:3]) bbox_max = np.array(bounds[3:6]) bbox_size = bbox_max - bbox_min xyz_samples, grid_size, length = generate_dense_grid_points( bbox_min=bbox_min, bbox_max=bbox_max, octree_resolution=resolutions[0], indexing="ij" ) dilate = nn.Conv3d(1, 1, 3, padding=1, bias=False, device=device, dtype=dtype) dilate.weight = torch.nn.Parameter(torch.ones(dilate.weight.shape, dtype=dtype, device=device)) grid_size = np.array(grid_size) xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype).contiguous().reshape(-1, 3) # 2. latents to 3d volume batch_logits = [] batch_size = latents.shape[0] for start in tqdm(range(0, xyz_samples.shape[0], num_chunks), desc=f"Hierarchical Volume Decoding [r{resolutions[0] + 1}]"): queries = xyz_samples[start: start + num_chunks, :] batch_queries = repeat(queries, "p c -> b p c", b=batch_size) logits = geo_decoder(queries=batch_queries, latents=latents) batch_logits.append(logits) grid_logits = torch.cat(batch_logits, dim=1).view((batch_size, grid_size[0], grid_size[1], grid_size[2])) for octree_depth_now in resolutions[1:]: grid_size = np.array([octree_depth_now + 1] * 3) resolution = bbox_size / octree_depth_now next_index = torch.zeros(tuple(grid_size), dtype=dtype, device=device) next_logits = torch.full(next_index.shape, -10000., dtype=dtype, device=device) curr_points = extract_near_surface_volume_fn(grid_logits.squeeze(0), mc_level) curr_points += grid_logits.squeeze(0).abs() < 0.95 if octree_depth_now == resolutions[-1]: expand_num = 0 else: expand_num = 1 for i in range(expand_num): curr_points = dilate(curr_points.unsqueeze(0).to(dtype)).squeeze(0) (cidx_x, cidx_y, cidx_z) = torch.where(curr_points > 0) next_index[cidx_x * 2, cidx_y * 2, cidx_z * 2] = 1 for i in range(2 - expand_num): next_index = dilate(next_index.unsqueeze(0)).squeeze(0) nidx = torch.where(next_index > 0) next_points = torch.stack(nidx, dim=1) next_points = (next_points * torch.tensor(resolution, dtype=next_points.dtype, device=device) + torch.tensor(bbox_min, dtype=next_points.dtype, device=device)) batch_logits = [] for start in tqdm(range(0, next_points.shape[0], num_chunks), desc=f"Hierarchical Volume Decoding [r{octree_depth_now + 1}]"): queries = next_points[start: start + num_chunks, :] batch_queries = repeat(queries, "p c -> b p c", b=batch_size) logits = geo_decoder(queries=batch_queries.to(latents.dtype), latents=latents) batch_logits.append(logits) grid_logits = torch.cat(batch_logits, dim=1) next_logits[nidx] = grid_logits[0, ..., 0] grid_logits = next_logits.unsqueeze(0) grid_logits[grid_logits == -10000.] = float('nan') return grid_logits class FlashVDMVolumeDecoding: def __init__(self, topk_mode='mean'): if topk_mode not in ['mean', 'merge']: raise ValueError(f'Unsupported topk_mode {topk_mode}, available: {["mean", "merge"]}') if topk_mode == 'mean': self.processor = FlashVDMCrossAttentionProcessor() else: self.processor = FlashVDMTopMCrossAttentionProcessor() @torch.no_grad() def __call__( self, latents: torch.FloatTensor, geo_decoder: CrossAttentionDecoder, bounds: Union[Tuple[float], List[float], float] = 1.01, num_chunks: int = 10000, mc_level: float = 0.0, octree_resolution: int = None, min_resolution: int = 63, mini_grid_num: int = 4, enable_pbar: bool = True, **kwargs, ): processor = self.processor geo_decoder.set_cross_attention_processor(processor) device = latents.device dtype = latents.dtype resolutions = [] if octree_resolution < min_resolution: resolutions.append(octree_resolution) while octree_resolution >= min_resolution: resolutions.append(octree_resolution) octree_resolution = octree_resolution // 2 resolutions.reverse() resolutions[0] = round(resolutions[0] / mini_grid_num) * mini_grid_num - 1 for i, resolution in enumerate(resolutions[1:]): resolutions[i + 1] = resolutions[0] * 2 ** (i + 1) logger.info(f"FlashVDMVolumeDecoding Resolution: {resolutions}") # 1. generate query points if isinstance(bounds, float): bounds = [-bounds, -bounds, -bounds, bounds, bounds, bounds] bbox_min = np.array(bounds[0:3]) bbox_max = np.array(bounds[3:6]) bbox_size = bbox_max - bbox_min xyz_samples, grid_size, length = generate_dense_grid_points( bbox_min=bbox_min, bbox_max=bbox_max, octree_resolution=resolutions[0], indexing="ij" ) dilate = nn.Conv3d(1, 1, 3, padding=1, bias=False, device=device, dtype=dtype) dilate.weight = torch.nn.Parameter(torch.ones(dilate.weight.shape, dtype=dtype, device=device)) grid_size = np.array(grid_size) # 2. latents to 3d volume xyz_samples = torch.from_numpy(xyz_samples).to(device, dtype=dtype) batch_size = latents.shape[0] mini_grid_size = xyz_samples.shape[0] // mini_grid_num xyz_samples = xyz_samples.view( mini_grid_num, mini_grid_size, mini_grid_num, mini_grid_size, mini_grid_num, mini_grid_size, 3 ).permute( 0, 2, 4, 1, 3, 5, 6 ).reshape( -1, mini_grid_size * mini_grid_size * mini_grid_size, 3 ) batch_logits = [] num_batchs = max(num_chunks // xyz_samples.shape[1], 1) for start in tqdm(range(0, xyz_samples.shape[0], num_batchs), desc=f"FlashVDM Volume Decoding", disable=not enable_pbar): queries = xyz_samples[start: start + num_batchs, :] batch = queries.shape[0] batch_latents = repeat(latents.squeeze(0), "p c -> b p c", b=batch) processor.topk = True logits = geo_decoder(queries=queries, latents=batch_latents) batch_logits.append(logits) grid_logits = torch.cat(batch_logits, dim=0).reshape( mini_grid_num, mini_grid_num, mini_grid_num, mini_grid_size, mini_grid_size, mini_grid_size ).permute(0, 3, 1, 4, 2, 5).contiguous().view( (batch_size, grid_size[0], grid_size[1], grid_size[2]) ) for octree_depth_now in resolutions[1:]: grid_size = np.array([octree_depth_now + 1] * 3) resolution = bbox_size / octree_depth_now next_index = torch.zeros(tuple(grid_size), dtype=dtype, device=device) next_logits = torch.full(next_index.shape, -10000., dtype=dtype, device=device) curr_points = extract_near_surface_volume_fn(grid_logits.squeeze(0), mc_level) curr_points += grid_logits.squeeze(0).abs() < 0.95 if octree_depth_now == resolutions[-1]: expand_num = 0 else: expand_num = 1 for i in range(expand_num): curr_points = dilate(curr_points.unsqueeze(0).to(dtype)).squeeze(0) (cidx_x, cidx_y, cidx_z) = torch.where(curr_points > 0) next_index[cidx_x * 2, cidx_y * 2, cidx_z * 2] = 1 for i in range(2 - expand_num): next_index = dilate(next_index.unsqueeze(0)).squeeze(0) nidx = torch.where(next_index > 0) next_points = torch.stack(nidx, dim=1) next_points = (next_points * torch.tensor(resolution, dtype=torch.float32, device=device) + torch.tensor(bbox_min, dtype=torch.float32, device=device)) query_grid_num = 6 min_val = next_points.min(axis=0).values max_val = next_points.max(axis=0).values vol_queries_index = (next_points - min_val) / (max_val - min_val) * (query_grid_num - 0.001) index = torch.floor(vol_queries_index).long() index = index[..., 0] * (query_grid_num ** 2) + index[..., 1] * query_grid_num + index[..., 2] index = index.sort() next_points = next_points[index.indices].unsqueeze(0).contiguous() unique_values = torch.unique(index.values, return_counts=True) grid_logits = torch.zeros((next_points.shape[1]), dtype=latents.dtype, device=latents.device) input_grid = [[], []] logits_grid_list = [] start_num = 0 sum_num = 0 for grid_index, count in zip(unique_values[0].cpu().tolist(), unique_values[1].cpu().tolist()): if sum_num + count < num_chunks or sum_num == 0: sum_num += count input_grid[0].append(grid_index) input_grid[1].append(count) else: processor.topk = input_grid logits_grid = geo_decoder(queries=next_points[:, start_num:start_num + sum_num], latents=latents) start_num = start_num + sum_num logits_grid_list.append(logits_grid) input_grid = [[grid_index], [count]] sum_num = count if sum_num > 0: processor.topk = input_grid logits_grid = geo_decoder(queries=next_points[:, start_num:start_num + sum_num], latents=latents) logits_grid_list.append(logits_grid) logits_grid = torch.cat(logits_grid_list, dim=1) grid_logits[index.indices] = logits_grid.squeeze(0).squeeze(-1) next_logits[nidx] = grid_logits grid_logits = next_logits.unsqueeze(0) grid_logits[grid_logits == -10000.] = float('nan') return grid_logits ================================================ FILE: hy3dgen/shapegen/models/conditioner.py ================================================ # Open Source Model Licensed under the Apache License Version 2.0 # and Other Licenses of the Third-Party Components therein: # The below Model in this distribution may have been modified by THL A29 Limited # ("Tencent Modifications"). All Tencent Modifications are Copyright (C) 2024 THL A29 Limited. # Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved. # The below software and/or models in this distribution may have been # modified by THL A29 Limited ("Tencent Modifications"). # All Tencent Modifications are Copyright (C) THL A29 Limited. # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import numpy as np import torch import torch.nn as nn from torchvision import transforms from transformers import ( CLIPVisionModelWithProjection, CLIPVisionConfig, Dinov2Model, Dinov2Config, ) def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) """ assert embed_dim % 2 == 0 omega = np.arange(embed_dim // 2, dtype=np.float64) omega /= embed_dim / 2. omega = 1. / 10000 ** omega # (D/2,) pos = pos.reshape(-1) # (M,) out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product emb_sin = np.sin(out) # (M, D/2) emb_cos = np.cos(out) # (M, D/2) return np.concatenate([emb_sin, emb_cos], axis=1) class ImageEncoder(nn.Module): def __init__( self, version=None, config=None, use_cls_token=True, image_size=224, **kwargs, ): super().__init__() if config is None: self.model = self.MODEL_CLASS.from_pretrained(version) else: self.model = self.MODEL_CLASS(self.MODEL_CONFIG_CLASS.from_dict(config)) self.model.eval() self.model.requires_grad_(False) self.use_cls_token = use_cls_token self.size = image_size // 14 self.num_patches = (image_size // 14) ** 2 if self.use_cls_token: self.num_patches += 1 self.transform = transforms.Compose( [ transforms.Resize(image_size, transforms.InterpolationMode.BILINEAR, antialias=True), transforms.CenterCrop(image_size), transforms.Normalize( mean=self.mean, std=self.std, ), ] ) def forward(self, image, mask=None, value_range=(-1, 1), **kwargs): if value_range is not None: low, high = value_range image = (image - low) / (high - low) image = image.to(self.model.device, dtype=self.model.dtype) inputs = self.transform(image) outputs = self.model(inputs) last_hidden_state = outputs.last_hidden_state if not self.use_cls_token: last_hidden_state = last_hidden_state[:, 1:, :] return last_hidden_state def unconditional_embedding(self, batch_size, **kwargs): device = next(self.model.parameters()).device dtype = next(self.model.parameters()).dtype zero = torch.zeros( batch_size, self.num_patches, self.model.config.hidden_size, device=device, dtype=dtype, ) return zero class CLIPImageEncoder(ImageEncoder): MODEL_CLASS = CLIPVisionModelWithProjection MODEL_CONFIG_CLASS = CLIPVisionConfig mean = [0.48145466, 0.4578275, 0.40821073] std = [0.26862954, 0.26130258, 0.27577711] class DinoImageEncoder(ImageEncoder): MODEL_CLASS = Dinov2Model MODEL_CONFIG_CLASS = Dinov2Config mean = [0.485, 0.456, 0.406] std = [0.229, 0.224, 0.225] class DinoImageEncoderMV(DinoImageEncoder): def __init__( self, version=None, config=None, use_cls_token=True, image_size=224, view_num=4, **kwargs, ): super().__init__(version, config, use_cls_token, image_size, **kwargs) self.view_num = view_num self.num_patches = self.num_patches pos = np.arange(self.view_num, dtype=np.float32) view_embedding = torch.from_numpy( get_1d_sincos_pos_embed_from_grid(self.model.config.hidden_size, pos)).float() view_embedding = view_embedding.unsqueeze(1).repeat(1, self.num_patches, 1) self.view_embed = view_embedding.unsqueeze(0) def forward(self, image, mask=None, value_range=(-1, 1), view_idxs=None): if value_range is not None: low, high = value_range image = (image - low) / (high - low) image = image.to(self.model.device, dtype=self.model.dtype) bs, num_views, c, h, w = image.shape image = image.view(bs * num_views, c, h, w) inputs = self.transform(image) outputs = self.model(inputs) last_hidden_state = outputs.last_hidden_state last_hidden_state = last_hidden_state.view( bs, num_views, last_hidden_state.shape[-2], last_hidden_state.shape[-1] ) view_embedding = self.view_embed.to(last_hidden_state.dtype).to(last_hidden_state.device) if view_idxs is not None: assert len(view_idxs) == bs view_embeddings = [] for i in range(bs): view_idx = view_idxs[i] assert num_views == len(view_idx) view_embeddings.append(self.view_embed[:, view_idx, ...]) view_embedding = torch.cat(view_embeddings, 0).to(last_hidden_state.dtype).to(last_hidden_state.device) if num_views != self.view_num: view_embedding = view_embedding[:, :num_views, ...] last_hidden_state = last_hidden_state + view_embedding last_hidden_state = last_hidden_state.view(bs, num_views * last_hidden_state.shape[-2], last_hidden_state.shape[-1]) return last_hidden_state def unconditional_embedding(self, batch_size, view_idxs=None, **kwargs): device = next(self.model.parameters()).device dtype = next(self.model.parameters()).dtype zero = torch.zeros( batch_size, self.num_patches * len(view_idxs[0]), self.model.config.hidden_size, device=device, dtype=dtype, ) return zero def build_image_encoder(config): if config['type'] == 'CLIPImageEncoder': return CLIPImageEncoder(**config['kwargs']) elif config['type'] == 'DinoImageEncoder': return DinoImageEncoder(**config['kwargs']) elif config['type'] == 'DinoImageEncoderMV': return DinoImageEncoderMV(**config['kwargs']) else: raise ValueError(f'Unknown image encoder type: {config["type"]}') class DualImageEncoder(nn.Module): def __init__( self, main_image_encoder, additional_image_encoder, ): super().__init__() self.main_image_encoder = build_image_encoder(main_image_encoder) self.additional_image_encoder = build_image_encoder(additional_image_encoder) def forward(self, image, mask=None, **kwargs): outputs = { 'main': self.main_image_encoder(image, mask=mask, **kwargs), 'additional': self.additional_image_encoder(image, mask=mask, **kwargs), } return outputs def unconditional_embedding(self, batch_size, **kwargs): outputs = { 'main': self.main_image_encoder.unconditional_embedding(batch_size, **kwargs), 'additional': self.additional_image_encoder.unconditional_embedding(batch_size, **kwargs), } return outputs class SingleImageEncoder(nn.Module): def __init__( self, main_image_encoder, ): super().__init__() self.main_image_encoder = build_image_encoder(main_image_encoder) def forward(self, image, mask=None, **kwargs): outputs = { 'main': self.main_image_encoder(image, mask=mask, **kwargs), } return outputs def unconditional_embedding(self, batch_size, **kwargs): outputs = { 'main': self.main_image_encoder.unconditional_embedding(batch_size, **kwargs), } return outputs ================================================ FILE: hy3dgen/shapegen/models/denoisers/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from .hunyuan3ddit import Hunyuan3DDiT ================================================ FILE: hy3dgen/shapegen/models/denoisers/hunyuan3ddit.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import math import os from dataclasses import dataclass from typing import List, Tuple, Optional import torch from einops import rearrange from torch import Tensor, nn scaled_dot_product_attention = nn.functional.scaled_dot_product_attention if os.environ.get('USE_SAGEATTN', '0') == '1': try: from sageattention import sageattn except ImportError: raise ImportError('Please install the package "sageattention" to use this USE_SAGEATTN.') scaled_dot_product_attention = sageattn def attention(q: Tensor, k: Tensor, v: Tensor, **kwargs) -> Tensor: x = scaled_dot_product_attention(q, k, v) x = rearrange(x, "B H L D -> B L (H D)") return x def timestep_embedding(t: Tensor, dim, max_period=10000, time_factor: float = 1000.0): """ Create sinusoidal timestep embeddings. :param t: a 1-D Tensor of N indices, one per batch element. These may be fractional. :param dim: the dimension of the output. :param max_period: controls the minimum frequency of the embeddings. :return: an (N, D) Tensor of positional embeddings. """ t = time_factor * t half = dim // 2 freqs = torch.exp(-math.log(max_period) * torch.arange(start=0, end=half, dtype=torch.float32) / half).to( t.device ) args = t[:, None].float() * freqs[None] embedding = torch.cat([torch.cos(args), torch.sin(args)], dim=-1) if dim % 2: embedding = torch.cat([embedding, torch.zeros_like(embedding[:, :1])], dim=-1) if torch.is_floating_point(t): embedding = embedding.to(t) return embedding class GELU(nn.Module): def __init__(self, approximate='tanh'): super().__init__() self.approximate = approximate def forward(self, x: Tensor) -> Tensor: return nn.functional.gelu(x, approximate=self.approximate) class MLPEmbedder(nn.Module): def __init__(self, in_dim: int, hidden_dim: int): super().__init__() self.in_layer = nn.Linear(in_dim, hidden_dim, bias=True) self.silu = nn.SiLU() self.out_layer = nn.Linear(hidden_dim, hidden_dim, bias=True) def forward(self, x: Tensor) -> Tensor: return self.out_layer(self.silu(self.in_layer(x))) class RMSNorm(torch.nn.Module): def __init__(self, dim: int): super().__init__() self.scale = nn.Parameter(torch.ones(dim)) def forward(self, x: Tensor): x_dtype = x.dtype x = x.float() rrms = torch.rsqrt(torch.mean(x ** 2, dim=-1, keepdim=True) + 1e-6) return (x * rrms).to(dtype=x_dtype) * self.scale class QKNorm(torch.nn.Module): def __init__(self, dim: int): super().__init__() self.query_norm = RMSNorm(dim) self.key_norm = RMSNorm(dim) def forward(self, q: Tensor, k: Tensor, v: Tensor) -> Tuple[Tensor, Tensor]: q = self.query_norm(q) k = self.key_norm(k) return q.to(v), k.to(v) class SelfAttention(nn.Module): def __init__( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, ): super().__init__() self.num_heads = num_heads head_dim = dim // num_heads self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias) self.norm = QKNorm(head_dim) self.proj = nn.Linear(dim, dim) def forward(self, x: Tensor, pe: Tensor) -> Tensor: qkv = self.qkv(x) q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) q, k = self.norm(q, k, v) x = attention(q, k, v, pe=pe) x = self.proj(x) return x @dataclass class ModulationOut: shift: Tensor scale: Tensor gate: Tensor class Modulation(nn.Module): def __init__(self, dim: int, double: bool): super().__init__() self.is_double = double self.multiplier = 6 if double else 3 self.lin = nn.Linear(dim, self.multiplier * dim, bias=True) def forward(self, vec: Tensor) -> Tuple[ModulationOut, Optional[ModulationOut]]: out = self.lin(nn.functional.silu(vec))[:, None, :] out = out.chunk(self.multiplier, dim=-1) return ( ModulationOut(*out[:3]), ModulationOut(*out[3:]) if self.is_double else None, ) class DoubleStreamBlock(nn.Module): def __init__( self, hidden_size: int, num_heads: int, mlp_ratio: float, qkv_bias: bool = False, ): super().__init__() mlp_hidden_dim = int(hidden_size * mlp_ratio) self.num_heads = num_heads self.hidden_size = hidden_size self.img_mod = Modulation(hidden_size, double=True) self.img_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.img_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) self.img_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.img_mlp = nn.Sequential( nn.Linear(hidden_size, mlp_hidden_dim, bias=True), GELU(approximate="tanh"), nn.Linear(mlp_hidden_dim, hidden_size, bias=True), ) self.txt_mod = Modulation(hidden_size, double=True) self.txt_norm1 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.txt_attn = SelfAttention(dim=hidden_size, num_heads=num_heads, qkv_bias=qkv_bias) self.txt_norm2 = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.txt_mlp = nn.Sequential( nn.Linear(hidden_size, mlp_hidden_dim, bias=True), GELU(approximate="tanh"), nn.Linear(mlp_hidden_dim, hidden_size, bias=True), ) def forward(self, img: Tensor, txt: Tensor, vec: Tensor, pe: Tensor) -> Tuple[Tensor, Tensor]: img_mod1, img_mod2 = self.img_mod(vec) txt_mod1, txt_mod2 = self.txt_mod(vec) img_modulated = self.img_norm1(img) img_modulated = (1 + img_mod1.scale) * img_modulated + img_mod1.shift img_qkv = self.img_attn.qkv(img_modulated) img_q, img_k, img_v = rearrange(img_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) img_q, img_k = self.img_attn.norm(img_q, img_k, img_v) txt_modulated = self.txt_norm1(txt) txt_modulated = (1 + txt_mod1.scale) * txt_modulated + txt_mod1.shift txt_qkv = self.txt_attn.qkv(txt_modulated) txt_q, txt_k, txt_v = rearrange(txt_qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) txt_q, txt_k = self.txt_attn.norm(txt_q, txt_k, txt_v) q = torch.cat((txt_q, img_q), dim=2) k = torch.cat((txt_k, img_k), dim=2) v = torch.cat((txt_v, img_v), dim=2) attn = attention(q, k, v, pe=pe) txt_attn, img_attn = attn[:, : txt.shape[1]], attn[:, txt.shape[1]:] img = img + img_mod1.gate * self.img_attn.proj(img_attn) img = img + img_mod2.gate * self.img_mlp((1 + img_mod2.scale) * self.img_norm2(img) + img_mod2.shift) txt = txt + txt_mod1.gate * self.txt_attn.proj(txt_attn) txt = txt + txt_mod2.gate * self.txt_mlp((1 + txt_mod2.scale) * self.txt_norm2(txt) + txt_mod2.shift) return img, txt class SingleStreamBlock(nn.Module): """ A DiT block with parallel linear layers as described in https://arxiv.org/abs/2302.05442 and adapted modulation interface. """ def __init__( self, hidden_size: int, num_heads: int, mlp_ratio: float = 4.0, qk_scale: Optional[float] = None, ): super().__init__() self.hidden_dim = hidden_size self.num_heads = num_heads head_dim = hidden_size // num_heads self.scale = qk_scale or head_dim ** -0.5 self.mlp_hidden_dim = int(hidden_size * mlp_ratio) # qkv and mlp_in self.linear1 = nn.Linear(hidden_size, hidden_size * 3 + self.mlp_hidden_dim) # proj and mlp_out self.linear2 = nn.Linear(hidden_size + self.mlp_hidden_dim, hidden_size) self.norm = QKNorm(head_dim) self.hidden_size = hidden_size self.pre_norm = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.mlp_act = GELU(approximate="tanh") self.modulation = Modulation(hidden_size, double=False) def forward(self, x: Tensor, vec: Tensor, pe: Tensor) -> Tensor: mod, _ = self.modulation(vec) x_mod = (1 + mod.scale) * self.pre_norm(x) + mod.shift qkv, mlp = torch.split(self.linear1(x_mod), [3 * self.hidden_size, self.mlp_hidden_dim], dim=-1) q, k, v = rearrange(qkv, "B L (K H D) -> K B H L D", K=3, H=self.num_heads) q, k = self.norm(q, k, v) # compute attention attn = attention(q, k, v, pe=pe) # compute activation in mlp stream, cat again and run second linear layer output = self.linear2(torch.cat((attn, self.mlp_act(mlp)), 2)) return x + mod.gate * output class LastLayer(nn.Module): def __init__(self, hidden_size: int, patch_size: int, out_channels: int): super().__init__() self.norm_final = nn.LayerNorm(hidden_size, elementwise_affine=False, eps=1e-6) self.linear = nn.Linear(hidden_size, patch_size * patch_size * out_channels, bias=True) self.adaLN_modulation = nn.Sequential(nn.SiLU(), nn.Linear(hidden_size, 2 * hidden_size, bias=True)) def forward(self, x: Tensor, vec: Tensor) -> Tensor: shift, scale = self.adaLN_modulation(vec).chunk(2, dim=1) x = (1 + scale[:, None, :]) * self.norm_final(x) + shift[:, None, :] x = self.linear(x) return x class Hunyuan3DDiT(nn.Module): def __init__( self, in_channels: int = 64, context_in_dim: int = 1536, hidden_size: int = 1024, mlp_ratio: float = 4.0, num_heads: int = 16, depth: int = 16, depth_single_blocks: int = 32, axes_dim: List[int] = [64], theta: int = 10_000, qkv_bias: bool = True, time_factor: float = 1000, guidance_embed: bool = False, ckpt_path: Optional[str] = None, **kwargs, ): super().__init__() self.in_channels = in_channels self.context_in_dim = context_in_dim self.hidden_size = hidden_size self.mlp_ratio = mlp_ratio self.num_heads = num_heads self.depth = depth self.depth_single_blocks = depth_single_blocks self.axes_dim = axes_dim self.theta = theta self.qkv_bias = qkv_bias self.time_factor = time_factor self.out_channels = self.in_channels self.guidance_embed = guidance_embed if hidden_size % num_heads != 0: raise ValueError( f"Hidden size {hidden_size} must be divisible by num_heads {num_heads}" ) pe_dim = hidden_size // num_heads if sum(axes_dim) != pe_dim: raise ValueError(f"Got {axes_dim} but expected positional dim {pe_dim}") self.hidden_size = hidden_size self.num_heads = num_heads self.latent_in = nn.Linear(self.in_channels, self.hidden_size, bias=True) self.time_in = MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) self.cond_in = nn.Linear(context_in_dim, self.hidden_size) self.guidance_in = ( MLPEmbedder(in_dim=256, hidden_dim=self.hidden_size) if guidance_embed else nn.Identity() ) self.double_blocks = nn.ModuleList( [ DoubleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, ) for _ in range(depth) ] ) self.single_blocks = nn.ModuleList( [ SingleStreamBlock( self.hidden_size, self.num_heads, mlp_ratio=mlp_ratio, ) for _ in range(depth_single_blocks) ] ) self.final_layer = LastLayer(self.hidden_size, 1, self.out_channels) if ckpt_path is not None: print('restored denoiser ckpt', ckpt_path) ckpt = torch.load(ckpt_path, map_location="cpu") if 'state_dict' not in ckpt: # deepspeed ckpt state_dict = {} for k in ckpt.keys(): new_k = k.replace('_forward_module.', '') state_dict[new_k] = ckpt[k] else: state_dict = ckpt["state_dict"] final_state_dict = {} for k, v in state_dict.items(): if k.startswith('model.'): final_state_dict[k.replace('model.', '')] = v else: final_state_dict[k] = v missing, unexpected = self.load_state_dict(final_state_dict, strict=False) print('unexpected keys:', unexpected) print('missing keys:', missing) def forward( self, x, t, contexts, **kwargs, ) -> Tensor: cond = contexts['main'] latent = self.latent_in(x) vec = self.time_in(timestep_embedding(t, 256, self.time_factor).to(dtype=latent.dtype)) if self.guidance_embed: guidance = kwargs.get('guidance', None) if guidance is None: raise ValueError("Didn't get guidance strength for guidance distilled model.") vec = vec + self.guidance_in(timestep_embedding(guidance, 256, self.time_factor)) cond = self.cond_in(cond) pe = None for block in self.double_blocks: latent, cond = block(img=latent, txt=cond, vec=vec, pe=pe) latent = torch.cat((cond, latent), 1) for block in self.single_blocks: latent = block(latent, vec=vec, pe=pe) latent = latent[:, cond.shape[1]:, ...] latent = self.final_layer(latent, vec) return latent ================================================ FILE: hy3dgen/shapegen/models/denoisers/hunyuandit.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import math import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from einops import rearrange from .moe_layers import MoEBlock def modulate(x, shift, scale): return x * (1 + scale.unsqueeze(1)) + shift.unsqueeze(1) def get_1d_sincos_pos_embed_from_grid(embed_dim, pos): """ embed_dim: output dimension for each position pos: a list of positions to be encoded: size (M,) out: (M, D) """ assert embed_dim % 2 == 0 omega = np.arange(embed_dim // 2, dtype=np.float64) omega /= embed_dim / 2. omega = 1. / 10000 ** omega # (D/2,) pos = pos.reshape(-1) # (M,) out = np.einsum('m,d->md', pos, omega) # (M, D/2), outer product emb_sin = np.sin(out) # (M, D/2) emb_cos = np.cos(out) # (M, D/2) return np.concatenate([emb_sin, emb_cos], axis=1) class Timesteps(nn.Module): def __init__(self, num_channels: int, downscale_freq_shift: float = 0.0, scale: int = 1, max_period: int = 10000 ): super().__init__() self.num_channels = num_channels self.downscale_freq_shift = downscale_freq_shift self.scale = scale self.max_period = max_period def forward(self, timesteps): assert len(timesteps.shape) == 1, "Timesteps should be a 1d-array" embedding_dim = self.num_channels half_dim = embedding_dim // 2 exponent = -math.log(self.max_period) * torch.arange( start=0, end=half_dim, dtype=torch.float32, device=timesteps.device) exponent = exponent / (half_dim - self.downscale_freq_shift) emb = torch.exp(exponent) emb = timesteps[:, None].float() * emb[None, :] emb = self.scale * emb emb = torch.cat([torch.sin(emb), torch.cos(emb)], dim=-1) if embedding_dim % 2 == 1: emb = torch.nn.functional.pad(emb, (0, 1, 0, 0)) return emb class TimestepEmbedder(nn.Module): """ Embeds scalar timesteps into vector representations. """ def __init__(self, hidden_size, frequency_embedding_size=256, cond_proj_dim=None, out_size=None): super().__init__() if out_size is None: out_size = hidden_size self.mlp = nn.Sequential( nn.Linear(hidden_size, frequency_embedding_size, bias=True), nn.GELU(), nn.Linear(frequency_embedding_size, out_size, bias=True), ) self.frequency_embedding_size = frequency_embedding_size if cond_proj_dim is not None: self.cond_proj = nn.Linear(cond_proj_dim, frequency_embedding_size, bias=False) self.time_embed = Timesteps(hidden_size) def forward(self, t, condition): t_freq = self.time_embed(t).type(self.mlp[0].weight.dtype) # t_freq = timestep_embedding(t, self.frequency_embedding_size).type(self.mlp[0].weight.dtype) if condition is not None: t_freq = t_freq + self.cond_proj(condition) t = self.mlp(t_freq) t = t.unsqueeze(dim=1) return t class MLP(nn.Module): def __init__(self, *, width: int): super().__init__() self.width = width self.fc1 = nn.Linear(width, width * 4) self.fc2 = nn.Linear(width * 4, width) self.gelu = nn.GELU() def forward(self, x): return self.fc2(self.gelu(self.fc1(x))) class CrossAttention(nn.Module): def __init__( self, qdim, kdim, num_heads, qkv_bias=True, qk_norm=False, norm_layer=nn.LayerNorm, with_decoupled_ca=False, decoupled_ca_dim=16, decoupled_ca_weight=1.0, **kwargs, ): super().__init__() self.qdim = qdim self.kdim = kdim self.num_heads = num_heads assert self.qdim % num_heads == 0, "self.qdim must be divisible by num_heads" self.head_dim = self.qdim // num_heads assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" self.scale = self.head_dim ** -0.5 self.to_q = nn.Linear(qdim, qdim, bias=qkv_bias) self.to_k = nn.Linear(kdim, qdim, bias=qkv_bias) self.to_v = nn.Linear(kdim, qdim, bias=qkv_bias) # TODO: eps should be 1 / 65530 if using fp16 self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.out_proj = nn.Linear(qdim, qdim, bias=True) self.with_dca = with_decoupled_ca if self.with_dca: self.kv_proj_dca = nn.Linear(kdim, 2 * qdim, bias=qkv_bias) self.k_norm_dca = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.dca_dim = decoupled_ca_dim self.dca_weight = decoupled_ca_weight def forward(self, x, y): """ Parameters ---------- x: torch.Tensor (batch, seqlen1, hidden_dim) (where hidden_dim = num heads * head dim) y: torch.Tensor (batch, seqlen2, hidden_dim2) freqs_cis_img: torch.Tensor (batch, hidden_dim // 2), RoPE for image """ b, s1, c = x.shape # [b, s1, D] if self.with_dca: token_len = y.shape[1] context_dca = y[:, -self.dca_dim:, :] kv_dca = self.kv_proj_dca(context_dca).view(b, self.dca_dim, 2, self.num_heads, self.head_dim) k_dca, v_dca = kv_dca.unbind(dim=2) # [b, s, h, d] k_dca = self.k_norm_dca(k_dca) y = y[:, :(token_len - self.dca_dim), :] _, s2, c = y.shape # [b, s2, 1024] q = self.to_q(x) k = self.to_k(y) v = self.to_v(y) kv = torch.cat((k, v), dim=-1) split_size = kv.shape[-1] // self.num_heads // 2 kv = kv.view(1, -1, self.num_heads, split_size * 2) k, v = torch.split(kv, split_size, dim=-1) q = q.view(b, s1, self.num_heads, self.head_dim) # [b, s1, h, d] k = k.view(b, s2, self.num_heads, self.head_dim) # [b, s2, h, d] v = v.view(b, s2, self.num_heads, self.head_dim) # [b, s2, h, d] q = self.q_norm(q) k = self.k_norm(k) with torch.backends.cuda.sdp_kernel( enable_flash=True, enable_math=False, enable_mem_efficient=True ): q, k, v = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.num_heads), (q, k, v)) context = F.scaled_dot_product_attention( q, k, v ).transpose(1, 2).reshape(b, s1, -1) if self.with_dca: with torch.backends.cuda.sdp_kernel( enable_flash=True, enable_math=False, enable_mem_efficient=True ): k_dca, v_dca = map(lambda t: rearrange(t, 'b n h d -> b h n d', h=self.num_heads), (k_dca, v_dca)) context_dca = F.scaled_dot_product_attention( q, k_dca, v_dca).transpose(1, 2).reshape(b, s1, -1) context = context + self.dca_weight * context_dca out = self.out_proj(context) # context.reshape - B, L1, -1 return out class Attention(nn.Module): """ We rename some layer names to align with flash attention """ def __init__( self, dim, num_heads, qkv_bias=True, qk_norm=False, norm_layer=nn.LayerNorm, ): super().__init__() self.dim = dim self.num_heads = num_heads assert self.dim % num_heads == 0, 'dim should be divisible by num_heads' self.head_dim = self.dim // num_heads # This assertion is aligned with flash attention assert self.head_dim % 8 == 0 and self.head_dim <= 128, "Only support head_dim <= 128 and divisible by 8" self.scale = self.head_dim ** -0.5 self.to_q = nn.Linear(dim, dim, bias=qkv_bias) self.to_k = nn.Linear(dim, dim, bias=qkv_bias) self.to_v = nn.Linear(dim, dim, bias=qkv_bias) # TODO: eps should be 1 / 65530 if using fp16 self.q_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.k_norm = norm_layer(self.head_dim, elementwise_affine=True, eps=1e-6) if qk_norm else nn.Identity() self.out_proj = nn.Linear(dim, dim) def forward(self, x): B, N, C = x.shape q = self.to_q(x) k = self.to_k(x) v = self.to_v(x) qkv = torch.cat((q, k, v), dim=-1) split_size = qkv.shape[-1] // self.num_heads // 3 qkv = qkv.view(1, -1, self.num_heads, split_size * 3) q, k, v = torch.split(qkv, split_size, dim=-1) q = q.reshape(B, N, self.num_heads, self.head_dim).transpose(1, 2) # [b, h, s, d] k = k.reshape(B, N, self.num_heads, self.head_dim).transpose(1, 2) # [b, h, s, d] v = v.reshape(B, N, self.num_heads, self.head_dim).transpose(1, 2) q = self.q_norm(q) # [b, h, s, d] k = self.k_norm(k) # [b, h, s, d] with torch.backends.cuda.sdp_kernel( enable_flash=True, enable_math=False, enable_mem_efficient=True ): x = F.scaled_dot_product_attention(q, k, v) x = x.transpose(1, 2).reshape(B, N, -1) x = self.out_proj(x) return x class HunYuanDiTBlock(nn.Module): def __init__( self, hidden_size, c_emb_size, num_heads, text_states_dim=1024, use_flash_attn=False, qk_norm=False, norm_layer=nn.LayerNorm, qk_norm_layer=nn.RMSNorm, with_decoupled_ca=False, decoupled_ca_dim=16, decoupled_ca_weight=1.0, init_scale=1.0, qkv_bias=True, skip_connection=True, timested_modulate=False, use_moe: bool = False, num_experts: int = 8, moe_top_k: int = 2, **kwargs, ): super().__init__() self.use_flash_attn = use_flash_attn use_ele_affine = True # ========================= Self-Attention ========================= self.norm1 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6) self.attn1 = Attention(hidden_size, num_heads=num_heads, qkv_bias=qkv_bias, qk_norm=qk_norm, norm_layer=qk_norm_layer) # ========================= FFN ========================= self.norm2 = norm_layer(hidden_size, elementwise_affine=use_ele_affine, eps=1e-6) # ========================= Add ========================= # Simply use add like SDXL. self.timested_modulate = timested_modulate if self.timested_modulate: self.default_modulation = nn.Sequential( nn.SiLU(), nn.Linear(c_emb_size, hidden_size, bias=True) ) # ========================= Cross-Attention ========================= self.attn2 = CrossAttention(hidden_size, text_states_dim, num_heads=num_heads, qkv_bias=qkv_bias, qk_norm=qk_norm, norm_layer=qk_norm_layer, with_decoupled_ca=with_decoupled_ca, decoupled_ca_dim=decoupled_ca_dim, decoupled_ca_weight=decoupled_ca_weight, init_scale=init_scale, ) self.norm3 = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6) if skip_connection: self.skip_norm = norm_layer(hidden_size, elementwise_affine=True, eps=1e-6) self.skip_linear = nn.Linear(2 * hidden_size, hidden_size) else: self.skip_linear = None self.use_moe = use_moe if self.use_moe: print("using moe") self.moe = MoEBlock( hidden_size, num_experts=num_experts, moe_top_k=moe_top_k, dropout=0.0, activation_fn="gelu", final_dropout=False, ff_inner_dim=int(hidden_size * 4.0), ff_bias=True, ) else: self.mlp = MLP(width=hidden_size) def forward(self, x, c=None, text_states=None, skip_value=None): if self.skip_linear is not None: cat = torch.cat([skip_value, x], dim=-1) x = self.skip_linear(cat) x = self.skip_norm(x) # Self-Attention if self.timested_modulate: shift_msa = self.default_modulation(c).unsqueeze(dim=1) x = x + shift_msa attn_out = self.attn1(self.norm1(x)) x = x + attn_out # Cross-Attention x = x + self.attn2(self.norm2(x), text_states) # FFN Layer mlp_inputs = self.norm3(x) if self.use_moe: x = x + self.moe(mlp_inputs) else: x = x + self.mlp(mlp_inputs) return x class AttentionPool(nn.Module): def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None): super().__init__() self.positional_embedding = nn.Parameter(torch.randn(spacial_dim + 1, embed_dim) / embed_dim ** 0.5) self.k_proj = nn.Linear(embed_dim, embed_dim) self.q_proj = nn.Linear(embed_dim, embed_dim) self.v_proj = nn.Linear(embed_dim, embed_dim) self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim) self.num_heads = num_heads def forward(self, x, attention_mask=None): x = x.permute(1, 0, 2) # NLC -> LNC if attention_mask is not None: attention_mask = attention_mask.unsqueeze(-1).permute(1, 0, 2) global_emb = (x * attention_mask).sum(dim=0) / attention_mask.sum(dim=0) x = torch.cat([global_emb[None,], x], dim=0) else: x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0) # (L+1)NC x = x + self.positional_embedding[:, None, :].to(x.dtype) # (L+1)NC x, _ = F.multi_head_attention_forward( query=x[:1], key=x, value=x, embed_dim_to_check=x.shape[-1], num_heads=self.num_heads, q_proj_weight=self.q_proj.weight, k_proj_weight=self.k_proj.weight, v_proj_weight=self.v_proj.weight, in_proj_weight=None, in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]), bias_k=None, bias_v=None, add_zero_attn=False, dropout_p=0, out_proj_weight=self.c_proj.weight, out_proj_bias=self.c_proj.bias, use_separate_proj_weight=True, training=self.training, need_weights=False ) return x.squeeze(0) class FinalLayer(nn.Module): """ The final layer of HunYuanDiT. """ def __init__(self, final_hidden_size, out_channels): super().__init__() self.final_hidden_size = final_hidden_size self.norm_final = nn.LayerNorm(final_hidden_size, elementwise_affine=True, eps=1e-6) self.linear = nn.Linear(final_hidden_size, out_channels, bias=True) def forward(self, x): x = self.norm_final(x) x = x[:, 1:] x = self.linear(x) return x class HunYuanDiTPlain(nn.Module): def __init__( self, input_size=1024, in_channels=4, hidden_size=1024, context_dim=1024, depth=24, num_heads=16, mlp_ratio=4.0, norm_type='layer', qk_norm_type='rms', qk_norm=False, text_len=257, with_decoupled_ca=False, additional_cond_hidden_state=768, decoupled_ca_dim=16, decoupled_ca_weight=1.0, use_pos_emb=False, use_attention_pooling=True, guidance_cond_proj_dim=None, qkv_bias=True, num_moe_layers: int = 6, num_experts: int = 8, moe_top_k: int = 2, ): super().__init__() self.input_size = input_size self.depth = depth self.in_channels = in_channels self.out_channels = in_channels self.num_heads = num_heads self.hidden_size = hidden_size self.norm = nn.LayerNorm if norm_type == 'layer' else nn.RMSNorm self.qk_norm = nn.RMSNorm if qk_norm_type == 'rms' else nn.LayerNorm self.context_dim = context_dim self.with_decoupled_ca = with_decoupled_ca self.decoupled_ca_dim = decoupled_ca_dim self.decoupled_ca_weight = decoupled_ca_weight self.use_pos_emb = use_pos_emb self.use_attention_pooling = use_attention_pooling self.guidance_cond_proj_dim = guidance_cond_proj_dim self.text_len = text_len self.x_embedder = nn.Linear(in_channels, hidden_size, bias=True) self.t_embedder = TimestepEmbedder(hidden_size, hidden_size * 4, cond_proj_dim=guidance_cond_proj_dim) # Will use fixed sin-cos embedding: if self.use_pos_emb: self.register_buffer("pos_embed", torch.zeros(1, input_size, hidden_size)) pos = np.arange(self.input_size, dtype=np.float32) pos_embed = get_1d_sincos_pos_embed_from_grid(self.pos_embed.shape[-1], pos) self.pos_embed.data.copy_(torch.from_numpy(pos_embed).float().unsqueeze(0)) self.use_attention_pooling = use_attention_pooling if use_attention_pooling: self.pooler = AttentionPool(self.text_len, context_dim, num_heads=8, output_dim=1024) self.extra_embedder = nn.Sequential( nn.Linear(1024, hidden_size * 4), nn.SiLU(), nn.Linear(hidden_size * 4, hidden_size, bias=True), ) if with_decoupled_ca: self.additional_cond_hidden_state = additional_cond_hidden_state self.additional_cond_proj = nn.Sequential( nn.Linear(additional_cond_hidden_state, hidden_size * 4), nn.SiLU(), nn.Linear(hidden_size * 4, 1024, bias=True), ) # HUnYuanDiT Blocks self.blocks = nn.ModuleList([ HunYuanDiTBlock(hidden_size=hidden_size, c_emb_size=hidden_size, num_heads=num_heads, mlp_ratio=mlp_ratio, text_states_dim=context_dim, qk_norm=qk_norm, norm_layer=self.norm, qk_norm_layer=self.qk_norm, skip_connection=layer > depth // 2, with_decoupled_ca=with_decoupled_ca, decoupled_ca_dim=decoupled_ca_dim, decoupled_ca_weight=decoupled_ca_weight, qkv_bias=qkv_bias, use_moe=True if depth - layer <= num_moe_layers else False, num_experts=num_experts, moe_top_k=moe_top_k ) for layer in range(depth) ]) self.depth = depth self.final_layer = FinalLayer(hidden_size, self.out_channels) def forward(self, x, t, contexts, **kwargs): cond = contexts['main'] t = self.t_embedder(t, condition=kwargs.get('guidance_cond')) x = self.x_embedder(x) if self.use_pos_emb: pos_embed = self.pos_embed.to(x.dtype) x = x + pos_embed if self.use_attention_pooling: extra_vec = self.pooler(cond, None) c = t + self.extra_embedder(extra_vec) # [B, D] else: c = t if self.with_decoupled_ca: additional_cond = self.additional_cond_proj(contexts['additional']) cond = torch.cat([cond, additional_cond], dim=1) x = torch.cat([c, x], dim=1) skip_value_list = [] for layer, block in enumerate(self.blocks): skip_value = None if layer <= self.depth // 2 else skip_value_list.pop() x = block(x, c, cond, skip_value=skip_value) if layer < self.depth // 2: skip_value_list.append(x) x = self.final_layer(x) return x ================================================ FILE: hy3dgen/shapegen/models/denoisers/moe_layers.py ================================================ import math import torch import torch.nn as nn import torch.nn.functional as F from diffusers.models.attention import FeedForward class AddAuxiliaryLoss(torch.autograd.Function): """ The trick function of adding auxiliary (aux) loss, which includes the gradient of the aux loss during backpropagation. """ @staticmethod def forward(ctx, x, loss): assert loss.numel() == 1 ctx.dtype = loss.dtype ctx.required_aux_loss = loss.requires_grad return x @staticmethod def backward(ctx, grad_output): grad_loss = None if ctx.required_aux_loss: grad_loss = torch.ones(1, dtype=ctx.dtype, device=grad_output.device) return grad_output, grad_loss class MoEGate(nn.Module): def __init__(self, embed_dim, num_experts=16, num_experts_per_tok=2, aux_loss_alpha=0.01): super().__init__() self.top_k = num_experts_per_tok self.n_routed_experts = num_experts self.scoring_func = 'softmax' self.alpha = aux_loss_alpha self.seq_aux = False # topk selection algorithm self.norm_topk_prob = False self.gating_dim = embed_dim self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) self.reset_parameters() def reset_parameters(self) -> None: import torch.nn.init as init init.kaiming_uniform_(self.weight, a=math.sqrt(5)) def forward(self, hidden_states): bsz, seq_len, h = hidden_states.shape # print(bsz, seq_len, h) ### compute gating score hidden_states = hidden_states.view(-1, h) logits = F.linear(hidden_states, self.weight, None) if self.scoring_func == 'softmax': scores = logits.softmax(dim=-1) else: raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}') ### select top-k experts topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) ### norm gate to sum 1 if self.top_k > 1 and self.norm_topk_prob: denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 topk_weight = topk_weight / denominator ### expert-level computation auxiliary loss if self.training and self.alpha > 0.0: scores_for_aux = scores aux_topk = self.top_k # always compute aux loss based on the naive greedy topk method topk_idx_for_aux_loss = topk_idx.view(bsz, -1) if self.seq_aux: scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1) ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device) ce.scatter_add_(1, topk_idx_for_aux_loss, torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_( seq_len * aux_topk / self.n_routed_experts) aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha else: mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts) ce = mask_ce.float().mean(0) Pi = scores_for_aux.mean(0) fi = ce * self.n_routed_experts aux_loss = (Pi * fi).sum() * self.alpha else: aux_loss = None return topk_idx, topk_weight, aux_loss class MoEBlock(nn.Module): def __init__(self, dim, num_experts=8, moe_top_k=2, activation_fn="gelu", dropout=0.0, final_dropout=False, ff_inner_dim=None, ff_bias=True): super().__init__() self.moe_top_k = moe_top_k self.experts = nn.ModuleList([ FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout, inner_dim=ff_inner_dim, bias=ff_bias) for i in range(num_experts)]) self.gate = MoEGate(embed_dim=dim, num_experts=num_experts, num_experts_per_tok=moe_top_k) self.shared_experts = FeedForward(dim, dropout=dropout, activation_fn=activation_fn, final_dropout=final_dropout, inner_dim=ff_inner_dim, bias=ff_bias) def initialize_weight(self): pass def forward(self, hidden_states): identity = hidden_states orig_shape = hidden_states.shape topk_idx, topk_weight, aux_loss = self.gate(hidden_states) hidden_states = hidden_states.view(-1, hidden_states.shape[-1]) flat_topk_idx = topk_idx.view(-1) if self.training: hidden_states = hidden_states.repeat_interleave(self.moe_top_k, dim=0) y = torch.empty_like(hidden_states, dtype=hidden_states.dtype) for i, expert in enumerate(self.experts): y[flat_topk_idx == i] = expert(hidden_states[flat_topk_idx == i]) y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1) y = y.view(*orig_shape) y = AddAuxiliaryLoss.apply(y, aux_loss) else: y = self.moe_infer(hidden_states, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape) y = y + self.shared_experts(identity) return y @torch.no_grad() def moe_infer(self, x, flat_expert_indices, flat_expert_weights): expert_cache = torch.zeros_like(x) idxs = flat_expert_indices.argsort() tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0) token_idxs = idxs // self.moe_top_k for i, end_idx in enumerate(tokens_per_expert): start_idx = 0 if i == 0 else tokens_per_expert[i - 1] if start_idx == end_idx: continue expert = self.experts[i] exp_token_idx = token_idxs[start_idx:end_idx] expert_tokens = x[exp_token_idx] expert_out = expert(expert_tokens) expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]]) # for fp16 and other dtype expert_cache = expert_cache.to(expert_out.dtype) expert_cache.scatter_reduce_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out, reduce='sum') return expert_cache ================================================ FILE: hy3dgen/shapegen/pipelines.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import copy import importlib import inspect import os from typing import List, Optional, Union import numpy as np import torch import trimesh import yaml from PIL import Image from diffusers.utils.torch_utils import randn_tensor from diffusers.utils.import_utils import is_accelerate_version, is_accelerate_available from tqdm import tqdm from .models.autoencoders import ShapeVAE from .models.autoencoders import SurfaceExtractors from .utils import logger, synchronize_timer, smart_load_model def retrieve_timesteps( scheduler, num_inference_steps: Optional[int] = None, device: Optional[Union[str, torch.device]] = None, timesteps: Optional[List[int]] = None, sigmas: Optional[List[float]] = 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 override the timestep spacing strategy of the scheduler. If `timesteps` is passed, `num_inference_steps` and `sigmas` must be `None`. sigmas (`List[float]`, *optional*): Custom sigmas used to override the timestep spacing strategy of the scheduler. If `sigmas` is passed, `num_inference_steps` and `timesteps` 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 and sigmas is not None: raise ValueError("Only one of `timesteps` or `sigmas` can be passed. Please choose one to set custom values") 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) elif sigmas is not None: accept_sigmas = "sigmas" in set(inspect.signature(scheduler.set_timesteps).parameters.keys()) if not accept_sigmas: raise ValueError( f"The current scheduler class {scheduler.__class__}'s `set_timesteps` does not support custom" f" sigmas schedules. Please check whether you are using the correct scheduler." ) scheduler.set_timesteps(sigmas=sigmas, 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 @synchronize_timer('Export to trimesh') def export_to_trimesh(mesh_output): if isinstance(mesh_output, list): outputs = [] for mesh in mesh_output: if mesh is None: outputs.append(None) else: mesh.mesh_f = mesh.mesh_f[:, ::-1] mesh_output = trimesh.Trimesh(mesh.mesh_v, mesh.mesh_f) outputs.append(mesh_output) return outputs else: mesh_output.mesh_f = mesh_output.mesh_f[:, ::-1] mesh_output = trimesh.Trimesh(mesh_output.mesh_v, mesh_output.mesh_f) return mesh_output def get_obj_from_str(string, reload=False): module, cls = string.rsplit(".", 1) if reload: module_imp = importlib.import_module(module) importlib.reload(module_imp) return getattr(importlib.import_module(module, package=None), cls) def instantiate_from_config(config, **kwargs): if "target" not in config: raise KeyError("Expected key `target` to instantiate.") try: target = config['target'] cls = get_obj_from_str(target) except Exception as e: target = config['target'].replace("hy3dshape", "hy3dgen.shapegen") cls = get_obj_from_str(target) params = config.get("params", dict()) kwargs.update(params) instance = cls(**kwargs) return instance class Hunyuan3DDiTPipeline: model_cpu_offload_seq = "conditioner->model->vae" _exclude_from_cpu_offload = [] @classmethod @synchronize_timer('Hunyuan3DDiTPipeline Model Loading') def from_single_file( cls, ckpt_path, config_path, device='cuda', dtype=torch.float16, use_safetensors=None, **kwargs, ): # load config with open(config_path, 'r') as f: config = yaml.safe_load(f) # load ckpt if use_safetensors: ckpt_path = ckpt_path.replace('.ckpt', '.safetensors') if not os.path.exists(ckpt_path): raise FileNotFoundError(f"Model file {ckpt_path} not found") logger.info(f"Loading model from {ckpt_path}") if use_safetensors: # parse safetensors import safetensors.torch safetensors_ckpt = safetensors.torch.load_file(ckpt_path, device='cpu') ckpt = {} for key, value in safetensors_ckpt.items(): model_name = key.split('.')[0] new_key = key[len(model_name) + 1:] if model_name not in ckpt: ckpt[model_name] = {} ckpt[model_name][new_key] = value else: ckpt = torch.load(ckpt_path, map_location='cpu', weights_only=True) # load model model = instantiate_from_config(config['model']) model.load_state_dict(ckpt['model']) vae = instantiate_from_config(config['vae']) vae.load_state_dict(ckpt['vae'], strict=False) conditioner = instantiate_from_config(config['conditioner']) if 'conditioner' in ckpt: conditioner.load_state_dict(ckpt['conditioner']) image_processor = instantiate_from_config(config['image_processor']) scheduler = instantiate_from_config(config['scheduler']) model_kwargs = dict( vae=vae, model=model, scheduler=scheduler, conditioner=conditioner, image_processor=image_processor, device=device, dtype=dtype, ) model_kwargs.update(kwargs) return cls( **model_kwargs ) @classmethod def from_pretrained( cls, model_path, device='cuda', dtype=torch.float16, use_safetensors=True, variant='fp16', subfolder='hunyuan3d-dit-v2-0', **kwargs, ): kwargs['from_pretrained_kwargs'] = dict( model_path=model_path, subfolder=subfolder, use_safetensors=use_safetensors, variant=variant, dtype=dtype, device=device, ) config_path, ckpt_path = smart_load_model( model_path, subfolder=subfolder, use_safetensors=use_safetensors, variant=variant ) return cls.from_single_file( ckpt_path, config_path, device=device, dtype=dtype, use_safetensors=use_safetensors, **kwargs ) def __init__( self, vae, model, scheduler, conditioner, image_processor, device='cuda', dtype=torch.float16, **kwargs ): self.vae = vae self.model = model self.scheduler = scheduler self.conditioner = conditioner self.image_processor = image_processor self.kwargs = kwargs self.to(device, dtype) def compile(self): self.vae = torch.compile(self.vae) self.model = torch.compile(self.model) self.conditioner = torch.compile(self.conditioner) def enable_flashvdm( self, enabled: bool = True, adaptive_kv_selection=True, topk_mode='mean', mc_algo='mc', replace_vae=True, ): if enabled: model_path = self.kwargs['from_pretrained_kwargs']['model_path'] turbo_vae_mapping = { 'Hunyuan3D-2': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0-turbo'), 'Hunyuan3D-2mv': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0-turbo'), 'Hunyuan3D-2mini': ('tencent/Hunyuan3D-2mini', 'hunyuan3d-vae-v2-mini-turbo'), } model_name = model_path.split('/')[-1] if replace_vae and model_name in turbo_vae_mapping: model_path, subfolder = turbo_vae_mapping[model_name] self.vae = ShapeVAE.from_pretrained( model_path, subfolder=subfolder, use_safetensors=self.kwargs['from_pretrained_kwargs']['use_safetensors'], device=self.device, ) self.vae.enable_flashvdm_decoder( enabled=enabled, adaptive_kv_selection=adaptive_kv_selection, topk_mode=topk_mode, mc_algo=mc_algo ) else: model_path = self.kwargs['from_pretrained_kwargs']['model_path'] vae_mapping = { 'Hunyuan3D-2': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0'), 'Hunyuan3D-2mv': ('tencent/Hunyuan3D-2', 'hunyuan3d-vae-v2-0'), 'Hunyuan3D-2mini': ('tencent/Hunyuan3D-2mini', 'hunyuan3d-vae-v2-mini'), } model_name = model_path.split('/')[-1] if model_name in vae_mapping: model_path, subfolder = vae_mapping[model_name] self.vae = ShapeVAE.from_pretrained(model_path, subfolder=subfolder) self.vae.enable_flashvdm_decoder(enabled=False) def to(self, device=None, dtype=None): if dtype is not None: self.dtype = dtype self.vae.to(dtype=dtype) self.model.to(dtype=dtype) self.conditioner.to(dtype=dtype) if device is not None: self.device = torch.device(device) self.vae.to(device) self.model.to(device) self.conditioner.to(device) @property def _execution_device(self): r""" Returns the device on which the pipeline's models will be executed. After calling [`~DiffusionPipeline.enable_sequential_cpu_offload`] the execution device can only be inferred from Accelerate's module hooks. """ for name, model in self.components.items(): if not isinstance(model, torch.nn.Module) or name in self._exclude_from_cpu_offload: continue if not hasattr(model, "_hf_hook"): return self.device for module in model.modules(): if ( hasattr(module, "_hf_hook") and hasattr(module._hf_hook, "execution_device") and module._hf_hook.execution_device is not None ): return torch.device(module._hf_hook.execution_device) return self.device def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"): r""" Offloads all models to CPU using accelerate, reducing memory usage with a low impact on performance. Compared to `enable_sequential_cpu_offload`, this method moves one whole model at a time to the GPU when its `forward` method is called, and the model remains in GPU until the next model runs. Memory savings are lower than with `enable_sequential_cpu_offload`, but performance is much better due to the iterative execution of the `unet`. Arguments: gpu_id (`int`, *optional*): The ID of the accelerator that shall be used in inference. If not specified, it will default to 0. device (`torch.Device` or `str`, *optional*, defaults to "cuda"): The PyTorch device type of the accelerator that shall be used in inference. If not specified, it will default to "cuda". """ if self.model_cpu_offload_seq is None: raise ValueError( "Model CPU offload cannot be enabled because no `model_cpu_offload_seq` class attribute is set." ) if is_accelerate_available() and is_accelerate_version(">=", "0.17.0.dev0"): from accelerate import cpu_offload_with_hook else: raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.") torch_device = torch.device(device) device_index = torch_device.index if gpu_id is not None and device_index is not None: raise ValueError( f"You have passed both `gpu_id`={gpu_id} and an index as part of the passed device `device`={device}" f"Cannot pass both. Please make sure to either not define `gpu_id` or not pass the index as part of the device: `device`={torch_device.type}" ) # _offload_gpu_id should be set to passed gpu_id (or id in passed `device`) or default to previously set id or default to 0 self._offload_gpu_id = gpu_id or torch_device.index or getattr(self, "_offload_gpu_id", 0) device_type = torch_device.type device = torch.device(f"{device_type}:{self._offload_gpu_id}") if self.device.type != "cpu": self.to("cpu") device_mod = getattr(torch, self.device.type, None) if hasattr(device_mod, "empty_cache") and device_mod.is_available(): device_mod.empty_cache() # otherwise we don't see the memory savings (but they probably exist) all_model_components = {k: v for k, v in self.components.items() if isinstance(v, torch.nn.Module)} self._all_hooks = [] hook = None for model_str in self.model_cpu_offload_seq.split("->"): model = all_model_components.pop(model_str, None) if not isinstance(model, torch.nn.Module): continue _, hook = cpu_offload_with_hook(model, device, prev_module_hook=hook) self._all_hooks.append(hook) # CPU offload models that are not in the seq chain unless they are explicitly excluded # these models will stay on CPU until maybe_free_model_hooks is called # some models cannot be in the seq chain because they are iteratively called, such as controlnet for name, model in all_model_components.items(): if not isinstance(model, torch.nn.Module): continue if name in self._exclude_from_cpu_offload: model.to(device) else: _, hook = cpu_offload_with_hook(model, device) self._all_hooks.append(hook) def maybe_free_model_hooks(self): r""" Function that offloads all components, removes all model hooks that were added when using `enable_model_cpu_offload` and then applies them again. In case the model has not been offloaded this function is a no-op. Make sure to add this function to the end of the `__call__` function of your pipeline so that it functions correctly when applying enable_model_cpu_offload. """ if not hasattr(self, "_all_hooks") or len(self._all_hooks) == 0: # `enable_model_cpu_offload` has not be called, so silently do nothing return for hook in self._all_hooks: # offload model and remove hook from model hook.offload() hook.remove() # make sure the model is in the same state as before calling it self.enable_model_cpu_offload() @synchronize_timer('Encode cond') def encode_cond(self, image, additional_cond_inputs, do_classifier_free_guidance, dual_guidance): bsz = image.shape[0] cond = self.conditioner(image=image, **additional_cond_inputs) if do_classifier_free_guidance: un_cond = self.conditioner.unconditional_embedding(bsz, **additional_cond_inputs) if dual_guidance: un_cond_drop_main = copy.deepcopy(un_cond) un_cond_drop_main['additional'] = cond['additional'] def cat_recursive(a, b, c): if isinstance(a, torch.Tensor): return torch.cat([a, b, c], dim=0).to(self.dtype) out = {} for k in a.keys(): out[k] = cat_recursive(a[k], b[k], c[k]) return out cond = cat_recursive(cond, un_cond_drop_main, un_cond) else: def cat_recursive(a, b): if isinstance(a, torch.Tensor): return torch.cat([a, b], dim=0).to(self.dtype) out = {} for k in a.keys(): out[k] = cat_recursive(a[k], b[k]) return out cond = cat_recursive(cond, un_cond) return cond 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 prepare_latents(self, batch_size, dtype, device, generator, latents=None): shape = (batch_size, *self.vae.latent_shape) 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: latents = randn_tensor(shape, generator=generator, device=device, dtype=dtype) else: latents = latents.to(device) # scale the initial noise by the standard deviation required by the scheduler latents = latents * getattr(self.scheduler, 'init_noise_sigma', 1.0) return latents def prepare_image(self, image) -> dict: if isinstance(image, str) and not os.path.exists(image): raise FileNotFoundError(f"Couldn't find image at path {image}") if not isinstance(image, list): image = [image] outputs = [] for img in image: output = self.image_processor(img) outputs.append(output) cond_input = {k: [] for k in outputs[0].keys()} for output in outputs: for key, value in output.items(): cond_input[key].append(value) for key, value in cond_input.items(): if isinstance(value[0], torch.Tensor): cond_input[key] = torch.cat(value, dim=0) return cond_input 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 def set_surface_extractor(self, mc_algo): if mc_algo is None: return logger.info('The parameters `mc_algo` is deprecated, and will be removed in future versions.\n' 'Please use: \n' 'from hy3dgen.shapegen.models.autoencoders import SurfaceExtractors\n' 'pipeline.vae.surface_extractor = SurfaceExtractors[mc_algo]() instead\n') if mc_algo not in SurfaceExtractors.keys(): raise ValueError(f"Unknown mc_algo {mc_algo}") self.vae.surface_extractor = SurfaceExtractors[mc_algo]() @torch.no_grad() def __call__( self, image: Union[str, List[str], Image.Image] = None, num_inference_steps: int = 50, timesteps: List[int] = None, sigmas: List[float] = None, eta: float = 0.0, guidance_scale: float = 7.5, dual_guidance_scale: float = 10.5, dual_guidance: bool = True, generator=None, box_v=1.01, octree_resolution=384, mc_level=-1 / 512, num_chunks=8000, mc_algo=None, output_type: Optional[str] = "trimesh", enable_pbar=True, **kwargs, ) -> List[List[trimesh.Trimesh]]: callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) self.set_surface_extractor(mc_algo) device = self.device dtype = self.dtype do_classifier_free_guidance = guidance_scale >= 0 and \ getattr(self.model, 'guidance_cond_proj_dim', None) is None dual_guidance = dual_guidance_scale >= 0 and dual_guidance cond_inputs = self.prepare_image(image) image = cond_inputs.pop('image') cond = self.encode_cond( image=image, additional_cond_inputs=cond_inputs, do_classifier_free_guidance=do_classifier_free_guidance, dual_guidance=False, ) batch_size = image.shape[0] t_dtype = torch.long timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, timesteps, sigmas) latents = self.prepare_latents(batch_size, dtype, device, generator) extra_step_kwargs = self.prepare_extra_step_kwargs(generator, eta) guidance_cond = None if getattr(self.model, 'guidance_cond_proj_dim', None) is not None: logger.info('Using lcm guidance scale') guidance_scale_tensor = torch.tensor(guidance_scale - 1).repeat(batch_size) guidance_cond = self.get_guidance_scale_embedding( guidance_scale_tensor, embedding_dim=self.model.guidance_cond_proj_dim ).to(device=device, dtype=latents.dtype) with synchronize_timer('Diffusion Sampling'): for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:", leave=False)): # expand the latents if we are doing classifier free guidance if do_classifier_free_guidance: latent_model_input = torch.cat([latents] * (3 if dual_guidance else 2)) else: latent_model_input = latents latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) # predict the noise residual timestep_tensor = torch.tensor([t], dtype=t_dtype, device=device) timestep_tensor = timestep_tensor.expand(latent_model_input.shape[0]) noise_pred = self.model(latent_model_input, timestep_tensor, cond, guidance_cond=guidance_cond) # no drop, drop clip, all drop if do_classifier_free_guidance: if dual_guidance: noise_pred_clip, noise_pred_dino, noise_pred_uncond = noise_pred.chunk(3) noise_pred = ( noise_pred_uncond + guidance_scale * (noise_pred_clip - noise_pred_dino) + dual_guidance_scale * (noise_pred_dino - noise_pred_uncond) ) else: noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 outputs = self.scheduler.step(noise_pred, t, latents, **extra_step_kwargs) latents = outputs.prev_sample if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, outputs) return self._export( latents, output_type, box_v, mc_level, num_chunks, octree_resolution, mc_algo, ) def _export( self, latents, output_type='trimesh', box_v=1.01, mc_level=0.0, num_chunks=20000, octree_resolution=256, mc_algo='mc', enable_pbar=True ): if not output_type == "latent": latents = 1. / self.vae.scale_factor * latents latents = self.vae(latents) outputs = self.vae.latents2mesh( latents, bounds=box_v, mc_level=mc_level, num_chunks=num_chunks, octree_resolution=octree_resolution, mc_algo=mc_algo, enable_pbar=enable_pbar, ) else: outputs = latents if output_type == 'trimesh': outputs = export_to_trimesh(outputs) return outputs class Hunyuan3DDiTFlowMatchingPipeline(Hunyuan3DDiTPipeline): @torch.inference_mode() def __call__( self, image: Union[str, List[str], Image.Image, dict, List[dict]] = None, num_inference_steps: int = 50, timesteps: List[int] = None, sigmas: List[float] = None, eta: float = 0.0, guidance_scale: float = 5.0, generator=None, box_v=1.01, octree_resolution=384, mc_level=0.0, mc_algo=None, num_chunks=8000, output_type: Optional[str] = "trimesh", enable_pbar=True, **kwargs, ) -> List[List[trimesh.Trimesh]]: callback = kwargs.pop("callback", None) callback_steps = kwargs.pop("callback_steps", None) self.set_surface_extractor(mc_algo) device = self.device dtype = self.dtype do_classifier_free_guidance = guidance_scale >= 0 and not ( hasattr(self.model, 'guidance_embed') and self.model.guidance_embed is True ) cond_inputs = self.prepare_image(image) image = cond_inputs.pop('image') cond = self.encode_cond( image=image, additional_cond_inputs=cond_inputs, do_classifier_free_guidance=do_classifier_free_guidance, dual_guidance=False, ) batch_size = image.shape[0] # 5. Prepare timesteps # NOTE: this is slightly different from common usage, we start from 0. sigmas = np.linspace(0, 1, num_inference_steps) if sigmas is None else sigmas timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, sigmas=sigmas, ) latents = self.prepare_latents(batch_size, dtype, device, generator) guidance = None if hasattr(self.model, 'guidance_embed') and \ self.model.guidance_embed is True: guidance = torch.tensor([guidance_scale] * batch_size, device=device, dtype=dtype) # logger.info(f'Using guidance embed with scale {guidance_scale}') with synchronize_timer('Diffusion Sampling'): for i, t in enumerate(tqdm(timesteps, disable=not enable_pbar, desc="Diffusion Sampling:")): # expand the latents if we are doing classifier free guidance if do_classifier_free_guidance: latent_model_input = torch.cat([latents] * 2) else: latent_model_input = latents # NOTE: we assume model get timesteps ranged from 0 to 1 timestep = t.expand(latent_model_input.shape[0]).to( latents.dtype) / self.scheduler.config.num_train_timesteps noise_pred = self.model(latent_model_input, timestep, cond, guidance=guidance) if do_classifier_free_guidance: noise_pred_cond, noise_pred_uncond = noise_pred.chunk(2) noise_pred = noise_pred_uncond + guidance_scale * (noise_pred_cond - noise_pred_uncond) # compute the previous noisy sample x_t -> x_t-1 outputs = self.scheduler.step(noise_pred, t, latents) latents = outputs.prev_sample if callback is not None and i % callback_steps == 0: step_idx = i // getattr(self.scheduler, "order", 1) callback(step_idx, t, outputs) return self._export( latents, output_type, box_v, mc_level, num_chunks, octree_resolution, mc_algo, enable_pbar=enable_pbar, ) ================================================ FILE: hy3dgen/shapegen/postprocessors.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import os import tempfile from typing import Union import numpy as np import pymeshlab import torch import trimesh from .models.autoencoders import Latent2MeshOutput from .utils import synchronize_timer def load_mesh(path): if path.endswith(".glb"): mesh = trimesh.load(path) else: mesh = pymeshlab.MeshSet() mesh.load_new_mesh(path) return mesh def reduce_face(mesh: pymeshlab.MeshSet, max_facenum: int = 200000): if max_facenum > mesh.current_mesh().face_number(): return mesh mesh.apply_filter( "meshing_decimation_quadric_edge_collapse", targetfacenum=max_facenum, qualitythr=1.0, preserveboundary=True, boundaryweight=3, preservenormal=True, preservetopology=True, autoclean=True ) return mesh def remove_floater(mesh: pymeshlab.MeshSet): mesh.apply_filter("compute_selection_by_small_disconnected_components_per_face", nbfaceratio=0.005) mesh.apply_filter("compute_selection_transfer_face_to_vertex", inclusive=False) mesh.apply_filter("meshing_remove_selected_vertices_and_faces") return mesh def pymeshlab2trimesh(mesh: pymeshlab.MeshSet): with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file: mesh.save_current_mesh(temp_file.name) mesh = trimesh.load(temp_file.name) # 检查加载的对象类型 if isinstance(mesh, trimesh.Scene): combined_mesh = trimesh.Trimesh() # 如果是Scene，遍历所有的geometry并合并 for geom in mesh.geometry.values(): combined_mesh = trimesh.util.concatenate([combined_mesh, geom]) mesh = combined_mesh return mesh def trimesh2pymeshlab(mesh: trimesh.Trimesh): with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file: if isinstance(mesh, trimesh.scene.Scene): for idx, obj in enumerate(mesh.geometry.values()): if idx == 0: temp_mesh = obj else: temp_mesh = temp_mesh + obj mesh = temp_mesh mesh.export(temp_file.name) mesh = pymeshlab.MeshSet() mesh.load_new_mesh(temp_file.name) return mesh def export_mesh(input, output): if isinstance(input, pymeshlab.MeshSet): mesh = output elif isinstance(input, Latent2MeshOutput): output = Latent2MeshOutput() output.mesh_v = output.current_mesh().vertex_matrix() output.mesh_f = output.current_mesh().face_matrix() mesh = output else: mesh = pymeshlab2trimesh(output) return mesh def import_mesh(mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str]) -> pymeshlab.MeshSet: if isinstance(mesh, str): mesh = load_mesh(mesh) elif isinstance(mesh, Latent2MeshOutput): mesh = pymeshlab.MeshSet() mesh_pymeshlab = pymeshlab.Mesh(vertex_matrix=mesh.mesh_v, face_matrix=mesh.mesh_f) mesh.add_mesh(mesh_pymeshlab, "converted_mesh") if isinstance(mesh, (trimesh.Trimesh, trimesh.scene.Scene)): mesh = trimesh2pymeshlab(mesh) return mesh class FaceReducer: @synchronize_timer('FaceReducer') def __call__( self, mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str], max_facenum: int = 40000 ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh]: ms = import_mesh(mesh) ms = reduce_face(ms, max_facenum=max_facenum) mesh = export_mesh(mesh, ms) return mesh class FloaterRemover: @synchronize_timer('FloaterRemover') def __call__( self, mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str], ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput]: ms = import_mesh(mesh) ms = remove_floater(ms) mesh = export_mesh(mesh, ms) return mesh class DegenerateFaceRemover: @synchronize_timer('DegenerateFaceRemover') def __call__( self, mesh: Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput, str], ) -> Union[pymeshlab.MeshSet, trimesh.Trimesh, Latent2MeshOutput]: ms = import_mesh(mesh) with tempfile.NamedTemporaryFile(suffix='.ply', delete=False) as temp_file: ms.save_current_mesh(temp_file.name) ms = pymeshlab.MeshSet() ms.load_new_mesh(temp_file.name) mesh = export_mesh(mesh, ms) return mesh def mesh_normalize(mesh): """ Normalize mesh vertices to sphere """ scale_factor = 1.2 vtx_pos = np.asarray(mesh.vertices) max_bb = (vtx_pos - 0).max(0)[0] min_bb = (vtx_pos - 0).min(0)[0] center = (max_bb + min_bb) / 2 scale = torch.norm(torch.tensor(vtx_pos - center, dtype=torch.float32), dim=1).max() * 2.0 vtx_pos = (vtx_pos - center) * (scale_factor / float(scale)) mesh.vertices = vtx_pos return mesh class MeshSimplifier: def __init__(self, executable: str = None): if executable is None: CURRENT_DIR = os.path.dirname(os.path.abspath(__file__)) executable = os.path.join(CURRENT_DIR, "mesh_simplifier.bin") self.executable = executable @synchronize_timer('MeshSimplifier') def __call__( self, mesh: Union[trimesh.Trimesh], ) -> Union[trimesh.Trimesh]: with tempfile.NamedTemporaryFile(suffix='.obj', delete=False) as temp_input: with tempfile.NamedTemporaryFile(suffix='.obj', delete=False) as temp_output: mesh.export(temp_input.name) os.system(f'{self.executable} {temp_input.name} {temp_output.name}') ms = trimesh.load(temp_output.name, process=False) if isinstance(ms, trimesh.Scene): combined_mesh = trimesh.Trimesh() for geom in ms.geometry.values(): combined_mesh = trimesh.util.concatenate([combined_mesh, geom]) ms = combined_mesh ms = mesh_normalize(ms) return ms ================================================ FILE: hy3dgen/shapegen/preprocessors.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import cv2 import numpy as np import torch from PIL import Image from einops import repeat, rearrange def array_to_tensor(np_array): image_pt = torch.tensor(np_array).float() image_pt = image_pt / 255 * 2 - 1 image_pt = rearrange(image_pt, "h w c -> c h w") image_pts = repeat(image_pt, "c h w -> b c h w", b=1) return image_pts class ImageProcessorV2: def __init__(self, size=512, border_ratio=None): self.size = size self.border_ratio = border_ratio @staticmethod def recenter(image, border_ratio: float = 0.2): """ recenter an image to leave some empty space at the image border. Args: image (ndarray): input image, float/uint8 [H, W, 3/4] mask (ndarray): alpha mask, bool [H, W] border_ratio (float, optional): border ratio, image will be resized to (1 - border_ratio). Defaults to 0.2. Returns: ndarray: output image, float/uint8 [H, W, 3/4] """ if image.shape[-1] == 4: mask = image[..., 3] else: mask = np.ones_like(image[..., 0:1]) * 255 image = np.concatenate([image, mask], axis=-1) mask = mask[..., 0] H, W, C = image.shape size = max(H, W) result = np.zeros((size, size, C), dtype=np.uint8) coords = np.nonzero(mask) x_min, x_max = coords[0].min(), coords[0].max() y_min, y_max = coords[1].min(), coords[1].max() h = x_max - x_min w = y_max - y_min if h == 0 or w == 0: raise ValueError('input image is empty') desired_size = int(size * (1 - border_ratio)) scale = desired_size / max(h, w) h2 = int(h * scale) w2 = int(w * scale) x2_min = (size - h2) // 2 x2_max = x2_min + h2 y2_min = (size - w2) // 2 y2_max = y2_min + w2 result[x2_min:x2_max, y2_min:y2_max] = cv2.resize(image[x_min:x_max, y_min:y_max], (w2, h2), interpolation=cv2.INTER_AREA) bg = np.ones((result.shape[0], result.shape[1], 3), dtype=np.uint8) * 255 mask = result[..., 3:].astype(np.float32) / 255 result = result[..., :3] * mask + bg * (1 - mask) mask = mask * 255 result = result.clip(0, 255).astype(np.uint8) mask = mask.clip(0, 255).astype(np.uint8) return result, mask def load_image(self, image, border_ratio=0.15, to_tensor=True): if isinstance(image, str): image = cv2.imread(image, cv2.IMREAD_UNCHANGED) image, mask = self.recenter(image, border_ratio=border_ratio) image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) elif isinstance(image, Image.Image): image = image.convert("RGBA") image = np.asarray(image) image, mask = self.recenter(image, border_ratio=border_ratio) image = cv2.resize(image, (self.size, self.size), interpolation=cv2.INTER_CUBIC) mask = cv2.resize(mask, (self.size, self.size), interpolation=cv2.INTER_NEAREST) mask = mask[..., np.newaxis] if to_tensor: image = array_to_tensor(image) mask = array_to_tensor(mask) return image, mask def __call__(self, image, border_ratio=0.15, to_tensor=True, **kwargs): if self.border_ratio is not None: border_ratio = self.border_ratio image, mask = self.load_image(image, border_ratio=border_ratio, to_tensor=to_tensor) outputs = { 'image': image, 'mask': mask } return outputs class MVImageProcessorV2(ImageProcessorV2): """ view order: front, front clockwise 90, back, front clockwise 270 """ return_view_idx = True def __init__(self, size=512, border_ratio=None): super().__init__(size, border_ratio) self.view2idx = { 'front': 0, 'left': 1, 'back': 2, 'right': 3 } def __call__(self, image_dict, border_ratio=0.15, to_tensor=True, **kwargs): if self.border_ratio is not None: border_ratio = self.border_ratio images = [] masks = [] view_idxs = [] for idx, (view_tag, image) in enumerate(image_dict.items()): view_idxs.append(self.view2idx[view_tag]) image, mask = self.load_image(image, border_ratio=border_ratio, to_tensor=to_tensor) images.append(image) masks.append(mask) zipped_lists = zip(view_idxs, images, masks) sorted_zipped_lists = sorted(zipped_lists) view_idxs, images, masks = zip(*sorted_zipped_lists) image = torch.cat(images, 0).unsqueeze(0) mask = torch.cat(masks, 0).unsqueeze(0) outputs = { 'image': image, 'mask': mask, 'view_idxs': view_idxs } return outputs IMAGE_PROCESSORS = { "v2": ImageProcessorV2, 'mv_v2': MVImageProcessorV2, } DEFAULT_IMAGEPROCESSOR = 'v2' ================================================ FILE: hy3dgen/shapegen/schedulers.py ================================================ # Copyright 2024 Stability AI, Katherine Crowson and 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. # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import math from dataclasses import dataclass from typing import List, Optional, Tuple, Union import numpy as np import torch from diffusers.configuration_utils import ConfigMixin, register_to_config from diffusers.schedulers.scheduling_utils import SchedulerMixin from diffusers.utils import BaseOutput, logging logger = logging.get_logger(__name__) # pylint: disable=invalid-name @dataclass class FlowMatchEulerDiscreteSchedulerOutput(BaseOutput): """ Output class for the scheduler's `step` function output. Args: prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images): Computed sample `(x_{t-1})` of previous timestep. `prev_sample` should be used as next model input in the denoising loop. """ prev_sample: torch.FloatTensor class FlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin): """ NOTE: this is very similar to diffusers.FlowMatchEulerDiscreteScheduler. Except our timesteps are reversed Euler scheduler. This model inherits from [`SchedulerMixin`] and [`ConfigMixin`]. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving. Args: num_train_timesteps (`int`, defaults to 1000): The number of diffusion steps to train the model. timestep_spacing (`str`, defaults to `"linspace"`): The way the timesteps should be scaled. Refer to Table 2 of the [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://huggingface.co/papers/2305.08891) for more information. shift (`float`, defaults to 1.0): The shift value for the timestep schedule. """ _compatibles = [] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, shift: float = 1.0, use_dynamic_shifting=False, ): timesteps = np.linspace(1, num_train_timesteps, num_train_timesteps, dtype=np.float32).copy() timesteps = torch.from_numpy(timesteps).to(dtype=torch.float32) sigmas = timesteps / num_train_timesteps if not use_dynamic_shifting: # when use_dynamic_shifting is True, we apply the timestep shifting on the fly based on the image resolution sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) self.timesteps = sigmas * num_train_timesteps self._step_index = None self._begin_index = None self.sigmas = sigmas.to("cpu") # to avoid too much CPU/GPU communication self.sigma_min = self.sigmas[-1].item() self.sigma_max = self.sigmas[0].item() @property def step_index(self): """ The index counter for current timestep. It will increase 1 after each scheduler step. """ return self._step_index @property def begin_index(self): """ The index for the first timestep. It should be set from pipeline with `set_begin_index` method. """ return self._begin_index # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index def set_begin_index(self, begin_index: int = 0): """ Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index (`int`): The begin index for the scheduler. """ self._begin_index = begin_index def scale_noise( self, sample: torch.FloatTensor, timestep: Union[float, torch.FloatTensor], noise: Optional[torch.FloatTensor] = None, ) -> torch.FloatTensor: """ Forward process in flow-matching Args: sample (`torch.FloatTensor`): The input sample. timestep (`int`, *optional*): The current timestep in the diffusion chain. Returns: `torch.FloatTensor`: A scaled input sample. """ # Make sure sigmas and timesteps have the same device and dtype as original_samples sigmas = self.sigmas.to(device=sample.device, dtype=sample.dtype) if sample.device.type == "mps" and torch.is_floating_point(timestep): # mps does not support float64 schedule_timesteps = self.timesteps.to(sample.device, dtype=torch.float32) timestep = timestep.to(sample.device, dtype=torch.float32) else: schedule_timesteps = self.timesteps.to(sample.device) timestep = timestep.to(sample.device) # self.begin_index is None when scheduler is used for training, or pipeline does not implement set_begin_index if self.begin_index is None: step_indices = [self.index_for_timestep(t, schedule_timesteps) for t in timestep] elif self.step_index is not None: # add_noise is called after first denoising step (for inpainting) step_indices = [self.step_index] * timestep.shape[0] else: # add noise is called before first denoising step to create initial latent(img2img) step_indices = [self.begin_index] * timestep.shape[0] sigma = sigmas[step_indices].flatten() while len(sigma.shape) < len(sample.shape): sigma = sigma.unsqueeze(-1) sample = sigma * noise + (1.0 - sigma) * sample return sample def _sigma_to_t(self, sigma): return sigma * self.config.num_train_timesteps def time_shift(self, mu: float, sigma: float, t: torch.Tensor): return math.exp(mu) / (math.exp(mu) + (1 / t - 1) ** sigma) def set_timesteps( self, num_inference_steps: int = None, device: Union[str, torch.device] = None, sigmas: Optional[List[float]] = None, mu: Optional[float] = None, ): """ Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ if self.config.use_dynamic_shifting and mu is None: raise ValueError(" you have a pass a value for `mu` when `use_dynamic_shifting` is set to be `True`") if sigmas is None: self.num_inference_steps = num_inference_steps timesteps = np.linspace( self._sigma_to_t(self.sigma_max), self._sigma_to_t(self.sigma_min), num_inference_steps ) sigmas = timesteps / self.config.num_train_timesteps if self.config.use_dynamic_shifting: sigmas = self.time_shift(mu, 1.0, sigmas) else: sigmas = self.config.shift * sigmas / (1 + (self.config.shift - 1) * sigmas) sigmas = torch.from_numpy(sigmas).to(dtype=torch.float32, device=device) timesteps = sigmas * self.config.num_train_timesteps self.timesteps = timesteps.to(device=device) self.sigmas = torch.cat([sigmas, torch.ones(1, device=sigmas.device)]) self._step_index = None self._begin_index = None def index_for_timestep(self, timestep, schedule_timesteps=None): if schedule_timesteps is None: schedule_timesteps = self.timesteps indices = (schedule_timesteps == timestep).nonzero() # The sigma index that is taken for the **very** first `step` # is always the second index (or the last index if there is only 1) # This way we can ensure we don't accidentally skip a sigma in # case we start in the middle of the denoising schedule (e.g. for image-to-image) pos = 1 if len(indices) > 1 else 0 return indices[pos].item() def _init_step_index(self, timestep): if self.begin_index is None: if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) self._step_index = self.index_for_timestep(timestep) else: self._step_index = self._begin_index def step( self, model_output: torch.FloatTensor, timestep: Union[float, torch.FloatTensor], sample: torch.FloatTensor, s_churn: float = 0.0, s_tmin: float = 0.0, s_tmax: float = float("inf"), s_noise: float = 1.0, generator: Optional[torch.Generator] = None, return_dict: bool = True, ) -> Union[FlowMatchEulerDiscreteSchedulerOutput, Tuple]: """ Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise). Args: model_output (`torch.FloatTensor`): The direct output from learned diffusion model. timestep (`float`): The current discrete timestep in the diffusion chain. sample (`torch.FloatTensor`): A current instance of a sample created by the diffusion process. s_churn (`float`): s_tmin (`float`): s_tmax (`float`): s_noise (`float`, defaults to 1.0): Scaling factor for noise added to the sample. generator (`torch.Generator`, *optional*): A random number generator. return_dict (`bool`): Whether or not to return a [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or tuple. Returns: [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] or `tuple`: If return_dict is `True`, [`~schedulers.scheduling_euler_discrete.EulerDiscreteSchedulerOutput`] is returned, otherwise a tuple is returned where the first element is the sample tensor. """ if ( isinstance(timestep, int) or isinstance(timestep, torch.IntTensor) or isinstance(timestep, torch.LongTensor) ): raise ValueError( ( "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" " one of the `scheduler.timesteps` as a timestep." ), ) if self.step_index is None: self._init_step_index(timestep) # Upcast to avoid precision issues when computing prev_sample sample = sample.to(torch.float32) sigma = self.sigmas[self.step_index] sigma_next = self.sigmas[self.step_index + 1] prev_sample = sample + (sigma_next - sigma) * model_output # Cast sample back to model compatible dtype prev_sample = prev_sample.to(model_output.dtype) # upon completion increase step index by one self._step_index += 1 if not return_dict: return (prev_sample,) return FlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample) def __len__(self): return self.config.num_train_timesteps @dataclass class ConsistencyFlowMatchEulerDiscreteSchedulerOutput(BaseOutput): prev_sample: torch.FloatTensor pred_original_sample: torch.FloatTensor class ConsistencyFlowMatchEulerDiscreteScheduler(SchedulerMixin, ConfigMixin): _compatibles = [] order = 1 @register_to_config def __init__( self, num_train_timesteps: int = 1000, pcm_timesteps: int = 50, ): sigmas = np.linspace(0, 1, num_train_timesteps) step_ratio = num_train_timesteps // pcm_timesteps euler_timesteps = (np.arange(1, pcm_timesteps) * step_ratio).round().astype(np.int64) - 1 euler_timesteps = np.asarray([0] + euler_timesteps.tolist()) self.euler_timesteps = euler_timesteps self.sigmas = sigmas[self.euler_timesteps] self.sigmas = torch.from_numpy((self.sigmas.copy())).to(dtype=torch.float32) self.timesteps = self.sigmas * num_train_timesteps self._step_index = None self._begin_index = None self.sigmas = self.sigmas.to("cpu") # to avoid too much CPU/GPU communication @property def step_index(self): """ The index counter for current timestep. It will increase 1 after each scheduler step. """ return self._step_index @property def begin_index(self): """ The index for the first timestep. It should be set from pipeline with `set_begin_index` method. """ return self._begin_index # Copied from diffusers.schedulers.scheduling_dpmsolver_multistep.DPMSolverMultistepScheduler.set_begin_index def set_begin_index(self, begin_index: int = 0): """ Sets the begin index for the scheduler. This function should be run from pipeline before the inference. Args: begin_index (`int`): The begin index for the scheduler. """ self._begin_index = begin_index def _sigma_to_t(self, sigma): return sigma * self.config.num_train_timesteps def set_timesteps( self, num_inference_steps: int = None, device: Union[str, torch.device] = None, sigmas: Optional[List[float]] = None, ): """ Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`): The number of diffusion steps used when generating samples with a pre-trained model. device (`str` or `torch.device`, *optional*): The device to which the timesteps should be moved to. If `None`, the timesteps are not moved. """ self.num_inference_steps = num_inference_steps if num_inference_steps is not None else len(sigmas) inference_indices = np.linspace( 0, self.config.pcm_timesteps, num=self.num_inference_steps, endpoint=False ) inference_indices = np.floor(inference_indices).astype(np.int64) inference_indices = torch.from_numpy(inference_indices).long() self.sigmas_ = self.sigmas[inference_indices] timesteps = self.sigmas_ * self.config.num_train_timesteps self.timesteps = timesteps.to(device=device) self.sigmas_ = torch.cat( [self.sigmas_, torch.ones(1, device=self.sigmas_.device)] ) self._step_index = None self._begin_index = None def index_for_timestep(self, timestep, schedule_timesteps=None): if schedule_timesteps is None: schedule_timesteps = self.timesteps indices = (schedule_timesteps == timestep).nonzero() # The sigma index that is taken for the **very** first `step` # is always the second index (or the last index if there is only 1) # This way we can ensure we don't accidentally skip a sigma in # case we start in the middle of the denoising schedule (e.g. for image-to-image) pos = 1 if len(indices) > 1 else 0 return indices[pos].item() def _init_step_index(self, timestep): if self.begin_index is None: if isinstance(timestep, torch.Tensor): timestep = timestep.to(self.timesteps.device) self._step_index = self.index_for_timestep(timestep) else: self._step_index = self._begin_index def step( self, model_output: torch.FloatTensor, timestep: Union[float, torch.FloatTensor], sample: torch.FloatTensor, generator: Optional[torch.Generator] = None, return_dict: bool = True, ) -> Union[ConsistencyFlowMatchEulerDiscreteSchedulerOutput, Tuple]: if ( isinstance(timestep, int) or isinstance(timestep, torch.IntTensor) or isinstance(timestep, torch.LongTensor) ): raise ValueError( ( "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to" " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass" " one of the `scheduler.timesteps` as a timestep." ), ) if self.step_index is None: self._init_step_index(timestep) sample = sample.to(torch.float32) sigma = self.sigmas_[self.step_index] sigma_next = self.sigmas_[self.step_index + 1] prev_sample = sample + (sigma_next - sigma) * model_output prev_sample = prev_sample.to(model_output.dtype) pred_original_sample = sample + (1.0 - sigma) * model_output pred_original_sample = pred_original_sample.to(model_output.dtype) self._step_index += 1 if not return_dict: return (prev_sample,) return ConsistencyFlowMatchEulerDiscreteSchedulerOutput(prev_sample=prev_sample, pred_original_sample=pred_original_sample) def __len__(self): return self.config.num_train_timesteps ================================================ FILE: hy3dgen/shapegen/surface_loaders.py ================================================ import numpy as np import torch import trimesh def normalize_mesh(mesh, scale=0.9999): bbox = mesh.bounds center = (bbox[1] + bbox[0]) / 2 scale_ = (bbox[1] - bbox[0]).max() mesh.apply_translation(-center) mesh.apply_scale(1 / scale_ * 2 * scale) return mesh def sample_pointcloud(mesh, num=200000): points, face_idx = mesh.sample(num, return_index=True) normals = mesh.face_normals[face_idx] points = torch.from_numpy(points.astype(np.float32)) normals = torch.from_numpy(normals.astype(np.float32)) return points, normals def load_surface(mesh, num_points=8192): mesh = normalize_mesh(mesh, scale=0.98) surface, normal = sample_pointcloud(mesh) rng = np.random.default_rng(seed=0) ind = rng.choice(surface.shape[0], num_points, replace=False) surface = torch.FloatTensor(surface[ind]) normal = torch.FloatTensor(normal[ind]) surface = torch.cat([surface, normal], dim=-1).unsqueeze(0) return surface, mesh def sharp_sample_pointcloud(mesh, num=16384): V = mesh.vertices N = mesh.face_normals VN = mesh.vertex_normals F = mesh.faces VN2 = np.ones(V.shape[0]) for i in range(3): dot = np.stack((VN2[F[:, i]], np.sum(VN[F[:, i]] * N, axis=-1)), axis=-1) VN2[F[:, i]] = np.min(dot, axis=-1) sharp_mask = VN2 < 0.985 # collect edge edge_a = np.concatenate((F[:, 0], F[:, 1], F[:, 2])) edge_b = np.concatenate((F[:, 1], F[:, 2], F[:, 0])) sharp_edge = ((sharp_mask[edge_a] * sharp_mask[edge_b])) edge_a = edge_a[sharp_edge > 0] edge_b = edge_b[sharp_edge > 0] sharp_verts_a = V[edge_a] sharp_verts_b = V[edge_b] sharp_verts_an = VN[edge_a] sharp_verts_bn = VN[edge_b] weights = np.linalg.norm(sharp_verts_b - sharp_verts_a, axis=-1) weights /= np.sum(weights) random_number = np.random.rand(num) w = np.random.rand(num, 1) index = np.searchsorted(weights.cumsum(), random_number) samples = w * sharp_verts_a[index] + (1 - w) * sharp_verts_b[index] normals = w * sharp_verts_an[index] + (1 - w) * sharp_verts_bn[index] return samples, normals def load_surface_sharpegde(mesh, num_points=4096, num_sharp_points=4096, sharpedge_flag=True): try: mesh_full = trimesh.util.concatenate(mesh.dump()) except Exception as err: mesh_full = trimesh.util.concatenate(mesh) mesh_full = normalize_mesh(mesh_full) origin_num = mesh_full.faces.shape[0] original_vertices = mesh_full.vertices original_faces = mesh_full.faces mesh = trimesh.Trimesh(vertices=original_vertices, faces=original_faces[:origin_num]) mesh_fill = trimesh.Trimesh(vertices=original_vertices, faces=original_faces[origin_num:]) area = mesh.area area_fill = mesh_fill.area sample_num = 499712 // 2 num_fill = int(sample_num * (area_fill / (area + area_fill))) num = sample_num - num_fill random_surface, random_normal = sample_pointcloud(mesh, num=num) if num_fill == 0: random_surface_fill, random_normal_fill = np.zeros((0, 3)), np.zeros((0, 3)) else: random_surface_fill, random_normal_fill = sample_pointcloud(mesh_fill, num=num_fill) random_sharp_surface, sharp_normal = sharp_sample_pointcloud(mesh, num=sample_num) # save_surface surface = np.concatenate((random_surface, random_normal), axis=1).astype(np.float16) surface_fill = np.concatenate((random_surface_fill, random_normal_fill), axis=1).astype(np.float16) sharp_surface = np.concatenate((random_sharp_surface, sharp_normal), axis=1).astype(np.float16) surface = np.concatenate((surface, surface_fill), axis=0) if sharpedge_flag: sharpedge_label = np.zeros((surface.shape[0], 1)) surface = np.concatenate((surface, sharpedge_label), axis=1) sharpedge_label = np.ones((sharp_surface.shape[0], 1)) sharp_surface = np.concatenate((sharp_surface, sharpedge_label), axis=1) rng = np.random.default_rng() ind = rng.choice(surface.shape[0], num_points, replace=False) surface = torch.FloatTensor(surface[ind]) ind = rng.choice(sharp_surface.shape[0], num_sharp_points, replace=False) sharp_surface = torch.FloatTensor(sharp_surface[ind]) return torch.cat([surface, sharp_surface], dim=0).unsqueeze(0), mesh_full class SurfaceLoader: def __init__(self, num_points=8192): self.num_points = num_points def __call__(self, mesh_or_mesh_path, num_points=None): if num_points is None: num_points = self.num_points mesh = mesh_or_mesh_path if isinstance(mesh, str): mesh = trimesh.load(mesh, force="mesh", merge_primitives=True) if isinstance(mesh, trimesh.scene.Scene): for idx, obj in enumerate(mesh.geometry.values()): if idx == 0: temp_mesh = obj else: temp_mesh = temp_mesh + obj mesh = temp_mesh surface, mesh = load_surface(mesh, num_points=num_points) return surface class SharpEdgeSurfaceLoader: def __init__(self, num_uniform_points=8192, num_sharp_points=8192, **kwargs): self.num_uniform_points = num_uniform_points self.num_sharp_points = num_sharp_points self.num_points = num_uniform_points + num_sharp_points def __call__(self, mesh_or_mesh_path, num_uniform_points=None, num_sharp_points=None): if num_uniform_points is None: num_uniform_points = self.num_uniform_points if num_sharp_points is None: num_sharp_points = self.num_sharp_points mesh = mesh_or_mesh_path if isinstance(mesh, str): mesh = trimesh.load(mesh, force="mesh", merge_primitives=True) if isinstance(mesh, trimesh.scene.Scene): for idx, obj in enumerate(mesh.geometry.values()): if idx == 0: temp_mesh = obj else: temp_mesh = temp_mesh + obj mesh = temp_mesh surface, mesh = load_surface_sharpegde(mesh, num_points=num_uniform_points, num_sharp_points=num_sharp_points) return surface ================================================ FILE: hy3dgen/shapegen/utils.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import logging import os from functools import wraps import torch def get_logger(name): logger = logging.getLogger(name) logger.setLevel(logging.INFO) console_handler = logging.StreamHandler() console_handler.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') console_handler.setFormatter(formatter) logger.addHandler(console_handler) return logger logger = get_logger('hy3dgen.shapgen') class synchronize_timer: """ Synchronized timer to count the inference time of `nn.Module.forward`. Supports both context manager and decorator usage. Example as context manager: ```python with synchronize_timer('name') as t: run() ``` Example as decorator: ```python @synchronize_timer('Export to trimesh') def export_to_trimesh(mesh_output): pass ``` """ def __init__(self, name=None): self.name = name def __enter__(self): """Context manager entry: start timing.""" if os.environ.get('HY3DGEN_DEBUG', '0') == '1': self.start = torch.cuda.Event(enable_timing=True) self.end = torch.cuda.Event(enable_timing=True) self.start.record() return lambda: self.time def __exit__(self, exc_type, exc_value, exc_tb): """Context manager exit: stop timing and log results.""" if os.environ.get('HY3DGEN_DEBUG', '0') == '1': self.end.record() torch.cuda.synchronize() self.time = self.start.elapsed_time(self.end) if self.name is not None: logger.info(f'{self.name} takes {self.time} ms') def __call__(self, func): """Decorator: wrap the function to time its execution.""" @wraps(func) def wrapper(*args, **kwargs): with self: result = func(*args, **kwargs) return result return wrapper def smart_load_model( model_path, subfolder, use_safetensors, variant, ): original_model_path = model_path # try local path base_dir = os.environ.get('HY3DGEN_MODELS', '~/.cache/hy3dgen') model_path = os.path.expanduser(os.path.join(base_dir, model_path, subfolder)) logger.info(f'Try to load model from local path: {model_path}') if not os.path.exists(model_path): logger.info('Model path not exists, try to download from huggingface') try: from huggingface_hub import snapshot_download # 只下载指定子目录 path = snapshot_download( repo_id=original_model_path, allow_patterns=[f"{subfolder}/*"], # 关键修改：模式匹配子文件夹 ) model_path = os.path.join(path, subfolder) # 保持路径拼接逻辑不变 except ImportError: logger.warning( "You need to install HuggingFace Hub to load models from the hub." ) raise RuntimeError(f"Model path {model_path} not found") except Exception as e: raise e if not os.path.exists(model_path): raise FileNotFoundError(f"Model path {original_model_path} not found") extension = 'ckpt' if not use_safetensors else 'safetensors' variant = '' if variant is None else f'.{variant}' ckpt_name = f'model{variant}.{extension}' config_path = os.path.join(model_path, 'config.yaml') ckpt_path = os.path.join(model_path, ckpt_name) return config_path, ckpt_path ================================================ FILE: hy3dgen/texgen/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from .pipelines import Hunyuan3DPaintPipeline, Hunyuan3DTexGenConfig ================================================ FILE: hy3dgen/texgen/custom_rasterizer/custom_rasterizer/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. ''' from .hierarchy import BuildHierarchy, BuildHierarchyWithColor from .io_obj import LoadObj, LoadObjWithTexture from .render import rasterize, interpolate ''' from .io_glb import * from .io_obj import * from .render import * ================================================ FILE: hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_glb.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import base64 import io import os import numpy as np from PIL import Image as PILImage from pygltflib import GLTF2 from scipy.spatial.transform import Rotation as R # Function to extract buffer data def get_buffer_data(gltf, buffer_view): buffer = gltf.buffers[buffer_view.buffer] buffer_data = gltf.get_data_from_buffer_uri(buffer.uri) byte_offset = buffer_view.byteOffset if buffer_view.byteOffset else 0 byte_length = buffer_view.byteLength return buffer_data[byte_offset:byte_offset + byte_length] # Function to extract attribute data def get_attribute_data(gltf, accessor_index): accessor = gltf.accessors[accessor_index] buffer_view = gltf.bufferViews[accessor.bufferView] buffer_data = get_buffer_data(gltf, buffer_view) comptype = {5120: np.int8, 5121: np.uint8, 5122: np.int16, 5123: np.uint16, 5125: np.uint32, 5126: np.float32} dtype = comptype[accessor.componentType] t2n = {'SCALAR': 1, 'VEC2': 2, 'VEC3': 3, 'VEC4': 4, 'MAT2': 4, 'MAT3': 9, 'MAT4': 16} num_components = t2n[accessor.type] # Calculate the correct slice of data byte_offset = accessor.byteOffset if accessor.byteOffset else 0 byte_stride = buffer_view.byteStride if buffer_view.byteStride else num_components * np.dtype(dtype).itemsize count = accessor.count # Extract the attribute data attribute_data = np.zeros((count, num_components), dtype=dtype) for i in range(count): start = byte_offset + i * byte_stride end = start + num_components * np.dtype(dtype).itemsize attribute_data[i] = np.frombuffer(buffer_data[start:end], dtype=dtype) return attribute_data # Function to extract image data def get_image_data(gltf, image, folder): if image.uri: if image.uri.startswith('data:'): # Data URI header, encoded = image.uri.split(',', 1) data = base64.b64decode(encoded) else: # External file fn = image.uri if not os.path.isabs(fn): fn = folder + '/' + fn with open(fn, 'rb') as f: data = f.read() else: buffer_view = gltf.bufferViews[image.bufferView] data = get_buffer_data(gltf, buffer_view) return data # Function to convert triangle strip to triangles def convert_triangle_strip_to_triangles(indices): triangles = [] for i in range(len(indices) - 2): if i % 2 == 0: triangles.append([indices[i], indices[i + 1], indices[i + 2]]) else: triangles.append([indices[i], indices[i + 2], indices[i + 1]]) return np.array(triangles).reshape(-1, 3) # Function to convert triangle fan to triangles def convert_triangle_fan_to_triangles(indices): triangles = [] for i in range(1, len(indices) - 1): triangles.append([indices[0], indices[i], indices[i + 1]]) return np.array(triangles).reshape(-1, 3) # Function to get the transformation matrix from a node def get_node_transform(node): if node.matrix: return np.array(node.matrix).reshape(4, 4).T else: T = np.eye(4) if node.translation: T[:3, 3] = node.translation if node.rotation: R_mat = R.from_quat(node.rotation).as_matrix() T[:3, :3] = R_mat if node.scale: S = np.diag(node.scale + [1]) T = T @ S return T def get_world_transform(gltf, node_index, parents, world_transforms): if parents[node_index] == -2: return world_transforms[node_index] node = gltf.nodes[node_index] if parents[node_index] == -1: world_transforms[node_index] = get_node_transform(node) parents[node_index] = -2 return world_transforms[node_index] parent_index = parents[node_index] parent_transform = get_world_transform(gltf, parent_index, parents, world_transforms) world_transforms[node_index] = parent_transform @ get_node_transform(node) parents[node_index] = -2 return world_transforms[node_index] def LoadGlb(path): # Load the GLB file using pygltflib gltf = GLTF2().load(path) primitives = [] images = {} # Iterate through the meshes in the GLB file world_transforms = [np.identity(4) for i in range(len(gltf.nodes))] parents = [-1 for i in range(len(gltf.nodes))] for node_index, node in enumerate(gltf.nodes): for idx in node.children: parents[idx] = node_index # for i in range(len(gltf.nodes)): # get_world_transform(gltf, i, parents, world_transform) for node_index, node in enumerate(gltf.nodes): if node.mesh is not None: world_transform = get_world_transform(gltf, node_index, parents, world_transforms) # Iterate through the primitives in the mesh mesh = gltf.meshes[node.mesh] for primitive in mesh.primitives: # Access the attributes of the primitive attributes = primitive.attributes.__dict__ mode = primitive.mode if primitive.mode is not None else 4 # Default to TRIANGLES result = {} if primitive.indices is not None: indices = get_attribute_data(gltf, primitive.indices) if mode == 4: # TRIANGLES face_indices = indices.reshape(-1, 3) elif mode == 5: # TRIANGLE_STRIP face_indices = convert_triangle_strip_to_triangles(indices) elif mode == 6: # TRIANGLE_FAN face_indices = convert_triangle_fan_to_triangles(indices) else: continue result['F'] = face_indices # Extract vertex positions if 'POSITION' in attributes and attributes['POSITION'] is not None: positions = get_attribute_data(gltf, attributes['POSITION']) # Apply the world transformation to the positions positions_homogeneous = np.hstack([positions, np.ones((positions.shape[0], 1))]) transformed_positions = (world_transform @ positions_homogeneous.T).T[:, :3] result['V'] = transformed_positions # Extract vertex colors if 'COLOR_0' in attributes and attributes['COLOR_0'] is not None: colors = get_attribute_data(gltf, attributes['COLOR_0']) if colors.shape[-1] > 3: colors = colors[..., :3] result['VC'] = colors # Extract UVs if 'TEXCOORD_0' in attributes and not attributes['TEXCOORD_0'] is None: uvs = get_attribute_data(gltf, attributes['TEXCOORD_0']) result['UV'] = uvs if primitive.material is not None: material = gltf.materials[primitive.material] if ( material.pbrMetallicRoughness is not None and material.pbrMetallicRoughness.baseColorTexture is not None ): texture_index = material.pbrMetallicRoughness.baseColorTexture.index texture = gltf.textures[texture_index] image_index = texture.source if not image_index in images: image = gltf.images[image_index] image_data = get_image_data(gltf, image, os.path.dirname(path)) pil_image = PILImage.open(io.BytesIO(image_data)) if pil_image.mode != 'RGB': pil_image = pil_image.convert('RGB') images[image_index] = pil_image result['TEX'] = image_index elif material.emissiveTexture is not None: texture_index = material.emissiveTexture.index texture = gltf.textures[texture_index] image_index = texture.source if not image_index in images: image = gltf.images[image_index] image_data = get_image_data(gltf, image, os.path.dirname(path)) pil_image = PILImage.open(io.BytesIO(image_data)) if pil_image.mode != 'RGB': pil_image = pil_image.convert('RGB') images[image_index] = pil_image result['TEX'] = image_index else: if material.pbrMetallicRoughness is not None: base_color = material.pbrMetallicRoughness.baseColorFactor else: base_color = np.array([0.8, 0.8, 0.8], dtype=np.float32) result['MC'] = base_color primitives.append(result) return primitives, images def RotatePrimitives(primitives, transform): for i in range(len(primitives)): if 'V' in primitives[i]: primitives[i]['V'] = primitives[i]['V'] @ transform.T if __name__ == '__main__': path = 'data/test.glb' LoadGlb(path) ================================================ FILE: hy3dgen/texgen/custom_rasterizer/custom_rasterizer/io_obj.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import cv2 import numpy as np def LoadObj(fn): lines = [l.strip() for l in open(fn)] vertices = [] faces = [] for l in lines: words = [w for w in l.split(' ') if w != ''] if len(words) == 0: continue if words[0] == 'v': v = [float(words[i]) for i in range(1, 4)] vertices.append(v) elif words[0] == 'f': f = [int(words[i]) - 1 for i in range(1, 4)] faces.append(f) return np.array(vertices).astype('float32'), np.array(faces).astype('int32') def LoadObjWithTexture(fn, tex_fn): lines = [l.strip() for l in open(fn)] vertices = [] vertex_textures = [] faces = [] face_textures = [] for l in lines: words = [w for w in l.split(' ') if w != ''] if len(words) == 0: continue if words[0] == 'v': v = [float(words[i]) for i in range(1, len(words))] vertices.append(v) elif words[0] == 'vt': v = [float(words[i]) for i in range(1, len(words))] vertex_textures.append(v) elif words[0] == 'f': f = [] ft = [] for i in range(1, len(words)): t = words[i].split('/') f.append(int(t[0]) - 1) ft.append(int(t[1]) - 1) for i in range(2, len(f)): faces.append([f[0], f[i - 1], f[i]]) face_textures.append([ft[0], ft[i - 1], ft[i]]) tex_image = cv2.cvtColor(cv2.imread(tex_fn), cv2.COLOR_BGR2RGB) return np.array(vertices).astype('float32'), np.array(vertex_textures).astype('float32'), np.array(faces).astype( 'int32'), np.array(face_textures).astype('int32'), tex_image ================================================ FILE: hy3dgen/texgen/custom_rasterizer/custom_rasterizer/render.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import custom_rasterizer_kernel import torch def rasterize(pos, tri, resolution, clamp_depth=torch.zeros(0), use_depth_prior=0): assert (pos.device == tri.device) findices, barycentric = custom_rasterizer_kernel.rasterize_image(pos[0], tri, clamp_depth, resolution[1], resolution[0], 1e-6, use_depth_prior) return findices, barycentric def interpolate(col, findices, barycentric, tri): f = findices - 1 + (findices == 0) vcol = col[0, tri.long()[f.long()]] result = barycentric.view(*barycentric.shape, 1) * vcol result = torch.sum(result, axis=-2) return result.view(1, *result.shape) ================================================ FILE: hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. ================================================ FILE: hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/grid_neighbor.cpp ================================================ #include "rasterizer.h" #include inline int pos2key(float* p, int resolution) { int x = (p[0] * 0.5 + 0.5) * resolution; int y = (p[1] * 0.5 + 0.5) * resolution; int z = (p[2] * 0.5 + 0.5) * resolution; return (x * resolution + y) * resolution + z; } inline void key2pos(int key, int resolution, float* p) { int x = key / resolution / resolution; int y = key / resolution % resolution; int z = key % resolution; p[0] = ((x + 0.5) / resolution - 0.5) * 2; p[1] = ((y + 0.5) / resolution - 0.5) * 2; p[2] = ((z + 0.5) / resolution - 0.5) * 2; } inline void key2cornerpos(int key, int resolution, float* p) { int x = key / resolution / resolution; int y = key / resolution % resolution; int z = key % resolution; p[0] = ((x + 0.75) / resolution - 0.5) * 2; p[1] = ((y + 0.25) / resolution - 0.5) * 2; p[2] = ((z + 0.75) / resolution - 0.5) * 2; } inline float* pos_ptr(int l, int i, int j, torch::Tensor t) { float* pdata = t.data_ptr(); int height = t.size(1); int width = t.size(2); return &pdata[((l * height + i) * width + j) * 4]; } struct Grid { std::vector seq2oddcorner; std::vector seq2evencorner; std::vector seq2grid; std::vector seq2normal; std::vector seq2neighbor; std::unordered_map grid2seq; std::vector downsample_seq; int num_origin_seq; int resolution; int stride; }; inline void pos_from_seq(Grid& grid, int seq, float* p) { auto k = grid.seq2grid[seq]; key2pos(k, grid.resolution, p); } inline int fetch_seq(Grid& grid, int l, int i, int j, torch::Tensor pdata) { float* p = pos_ptr(l, i, j, pdata); if (p[3] == 0) return -1; auto key = pos2key(p, grid.resolution); int seq = grid.grid2seq[key]; return seq; } inline int fetch_last_seq(Grid& grid, int i, int j, torch::Tensor pdata) { int num_layers = pdata.size(0); int l = 0; int idx = fetch_seq(grid, l, i, j, pdata); while (l < num_layers - 1) { l += 1; int new_idx = fetch_seq(grid, l, i, j, pdata); if (new_idx == -1) break; idx = new_idx; } return idx; } inline int fetch_nearest_seq(Grid& grid, int i, int j, int dim, float d, torch::Tensor pdata) { float p[3]; float max_dist = 1e10; int best_idx = -1; int num_layers = pdata.size(0); for (int l = 0; l < num_layers; ++l) { int idx = fetch_seq(grid, l, i, j, pdata); if (idx == -1) break; pos_from_seq(grid, idx, p); float dist = std::abs(d - p[(dim + 2) % 3]); if (dist < max_dist) { max_dist = dist; best_idx = idx; } } return best_idx; } inline int fetch_nearest_seq_layer(Grid& grid, int i, int j, int dim, float d, torch::Tensor pdata) { float p[3]; float max_dist = 1e10; int best_layer = -1; int num_layers = pdata.size(0); for (int l = 0; l < num_layers; ++l) { int idx = fetch_seq(grid, l, i, j, pdata); if (idx == -1) break; pos_from_seq(grid, idx, p); float dist = std::abs(d - p[(dim + 2) % 3]); if (dist < max_dist) { max_dist = dist; best_layer = l; } } return best_layer; } void FetchNeighbor(Grid& grid, int seq, float* pos, int dim, int boundary_info, std::vector& view_layer_positions, int* output_indices) { auto t = view_layer_positions[dim]; int height = t.size(1); int width = t.size(2); int top = 0; int ci = 0; int cj = 0; if (dim == 0) { ci = (pos[1]/2+0.5)*height; cj = (pos[0]/2+0.5)*width; } else if (dim == 1) { ci = (pos[1]/2+0.5)*height; cj = (pos[2]/2+0.5)*width; } else { ci = (-pos[2]/2+0.5)*height; cj = (pos[0]/2+0.5)*width; } int stride = grid.stride; for (int ni = ci + stride; ni >= ci - stride; ni -= stride) { for (int nj = cj - stride; nj <= cj + stride; nj += stride) { int idx = -1; if (ni == ci && nj == cj) idx = seq; else if (!(ni < 0 || ni >= height || nj < 0 || nj >= width)) { if (boundary_info == -1) idx = fetch_seq(grid, 0, ni, nj, t); else if (boundary_info == 1) idx = fetch_last_seq(grid, ni, nj, t); else idx = fetch_nearest_seq(grid, ni, nj, dim, pos[(dim + 2) % 3], t); } output_indices[top] = idx; top += 1; } } } void DownsampleGrid(Grid& src, Grid& tar) { src.downsample_seq.resize(src.seq2grid.size(), -1); tar.resolution = src.resolution / 2; tar.stride = src.stride * 2; float pos[3]; std::vector seq2normal_count; for (int i = 0; i < src.seq2grid.size(); ++i) { key2pos(src.seq2grid[i], src.resolution, pos); int k = pos2key(pos, tar.resolution); int s = seq2normal_count.size(); if (!tar.grid2seq.count(k)) { tar.grid2seq[k] = tar.seq2grid.size(); tar.seq2grid.emplace_back(k); seq2normal_count.emplace_back(0); seq2normal_count.emplace_back(0); seq2normal_count.emplace_back(0); //tar.seq2normal.emplace_back(src.seq2normal[i]); } else { s = tar.grid2seq[k] * 3; } seq2normal_count[s + src.seq2normal[i]] += 1; src.downsample_seq[i] = tar.grid2seq[k]; } tar.seq2normal.resize(seq2normal_count.size() / 3); for (int i = 0; i < seq2normal_count.size(); i += 3) { int t = 0; for (int j = 1; j < 3; ++j) { if (seq2normal_count[i + j] > seq2normal_count[i + t]) t = j; } tar.seq2normal[i / 3] = t; } } void NeighborGrid(Grid& grid, std::vector view_layer_positions, int v) { grid.seq2evencorner.resize(grid.seq2grid.size(), 0); grid.seq2oddcorner.resize(grid.seq2grid.size(), 0); std::unordered_set visited_seq; for (int vd = 0; vd < 3; ++vd) { auto t = view_layer_positions[vd]; auto t0 = view_layer_positions[v]; int height = t.size(1); int width = t.size(2); int num_layers = t.size(0); int num_view_layers = t0.size(0); for (int i = 0; i < height; ++i) { for (int j = 0; j < width; ++j) { for (int l = 0; l < num_layers; ++l) { int seq = fetch_seq(grid, l, i, j, t); if (seq == -1) break; int dim = grid.seq2normal[seq]; if (dim != v) continue; float pos[3]; pos_from_seq(grid, seq, pos); int ci = 0; int cj = 0; if (dim == 0) { ci = (pos[1]/2+0.5)*height; cj = (pos[0]/2+0.5)*width; } else if (dim == 1) { ci = (pos[1]/2+0.5)*height; cj = (pos[2]/2+0.5)*width; } else { ci = (-pos[2]/2+0.5)*height; cj = (pos[0]/2+0.5)*width; } if ((ci % (grid.stride * 2) < grid.stride) && (cj % (grid.stride * 2) >= grid.stride)) grid.seq2evencorner[seq] = 1; if ((ci % (grid.stride * 2) >= grid.stride) && (cj % (grid.stride * 2) < grid.stride)) grid.seq2oddcorner[seq] = 1; bool is_boundary = false; if (vd == v) { if (l == 0 || l == num_layers - 1) is_boundary = true; else { int seq_new = fetch_seq(grid, l + 1, i, j, t); if (seq_new == -1) is_boundary = true; } } int boundary_info = 0; if (is_boundary && (l == 0)) boundary_info = -1; else if (is_boundary) boundary_info = 1; if (visited_seq.count(seq)) continue; visited_seq.insert(seq); FetchNeighbor(grid, seq, pos, dim, boundary_info, view_layer_positions, &grid.seq2neighbor[seq * 9]); } } } } } void PadGrid(Grid& src, Grid& tar, std::vector& view_layer_positions) { auto& downsample_seq = src.downsample_seq; auto& seq2evencorner = src.seq2evencorner; auto& seq2oddcorner = src.seq2oddcorner; int indices[9]; std::vector mapped_even_corners(tar.seq2grid.size(), 0); std::vector mapped_odd_corners(tar.seq2grid.size(), 0); for (int i = 0; i < downsample_seq.size(); ++i) { if (seq2evencorner[i] > 0) { mapped_even_corners[downsample_seq[i]] = 1; } if (seq2oddcorner[i] > 0) { mapped_odd_corners[downsample_seq[i]] = 1; } } auto& tar_seq2normal = tar.seq2normal; auto& tar_seq2grid = tar.seq2grid; for (int i = 0; i < tar_seq2grid.size(); ++i) { if (mapped_even_corners[i] == 1 && mapped_odd_corners[i] == 1) continue; auto k = tar_seq2grid[i]; float p[3]; key2cornerpos(k, tar.resolution, p); int src_key = pos2key(p, src.resolution); if (!src.grid2seq.count(src_key)) { int seq = src.seq2grid.size(); src.grid2seq[src_key] = seq; src.seq2evencorner.emplace_back((mapped_even_corners[i] == 0)); src.seq2oddcorner.emplace_back((mapped_odd_corners[i] == 0)); src.seq2grid.emplace_back(src_key); src.seq2normal.emplace_back(tar_seq2normal[i]); FetchNeighbor(src, seq, p, tar_seq2normal[i], 0, view_layer_positions, indices); for (int j = 0; j < 9; ++j) { src.seq2neighbor.emplace_back(indices[j]); } src.downsample_seq.emplace_back(i); } else { int seq = src.grid2seq[src_key]; if (mapped_even_corners[i] == 0) src.seq2evencorner[seq] = 1; if (mapped_odd_corners[i] == 0) src.seq2oddcorner[seq] = 1; } } } std::vector> build_hierarchy(std::vector view_layer_positions, std::vector view_layer_normals, int num_level, int resolution) { if (view_layer_positions.size() != 3 || num_level < 1) { printf("Alert! We require 3 layers and at least 1 level! (%d %d)\n", view_layer_positions.size(), num_level); return {{},{},{},{}}; } std::vector grids; grids.resize(num_level); std::vector seq2pos; auto& seq2grid = grids[0].seq2grid; auto& seq2normal = grids[0].seq2normal; auto& grid2seq = grids[0].grid2seq; grids[0].resolution = resolution; grids[0].stride = 1; auto int64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false); auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false); for (int v = 0; v < 3; ++v) { int num_layers = view_layer_positions[v].size(0); int height = view_layer_positions[v].size(1); int width = view_layer_positions[v].size(2); float* data = view_layer_positions[v].data_ptr(); float* data_normal = view_layer_normals[v].data_ptr(); for (int l = 0; l < num_layers; ++l) { for (int i = 0; i < height; ++i) { for (int j = 0; j < width; ++j) { float* p = &data[(i * width + j) * 4]; float* n = &data_normal[(i * width + j) * 3]; if (p[3] == 0) continue; auto k = pos2key(p, resolution); if (!grid2seq.count(k)) { int dim = 0; for (int d = 0; d < 3; ++d) { if (std::abs(n[d]) > std::abs(n[dim])) dim = d; } dim = (dim + 1) % 3; grid2seq[k] = seq2grid.size(); seq2grid.emplace_back(k); seq2pos.push_back(p[0]); seq2pos.push_back(p[1]); seq2pos.push_back(p[2]); seq2normal.emplace_back(dim); } } } data += (height * width * 4); data_normal += (height * width * 3); } } for (int i = 0; i < num_level - 1; ++i) { DownsampleGrid(grids[i], grids[i + 1]); } for (int l = 0; l < num_level; ++l) { grids[l].seq2neighbor.resize(grids[l].seq2grid.size() * 9, -1); grids[l].num_origin_seq = grids[l].seq2grid.size(); for (int d = 0; d < 3; ++d) { NeighborGrid(grids[l], view_layer_positions, d); } } for (int i = num_level - 2; i >= 0; --i) { PadGrid(grids[i], grids[i + 1], view_layer_positions); } for (int i = grids[0].num_origin_seq; i < grids[0].seq2grid.size(); ++i) { int k = grids[0].seq2grid[i]; float p[3]; key2pos(k, grids[0].resolution, p); seq2pos.push_back(p[0]); seq2pos.push_back(p[1]); seq2pos.push_back(p[2]); } std::vector texture_positions(2); std::vector grid_neighbors(grids.size()); std::vector grid_downsamples(grids.size() - 1); std::vector grid_evencorners(grids.size()); std::vector grid_oddcorners(grids.size()); texture_positions[0] = torch::zeros({static_cast(seq2pos.size() / 3), static_cast(3)}, float_options); texture_positions[1] = torch::zeros({static_cast(seq2pos.size() / 3)}, float_options); float* positions_out_ptr = texture_positions[0].data_ptr(); memcpy(positions_out_ptr, seq2pos.data(), sizeof(float) * seq2pos.size()); positions_out_ptr = texture_positions[1].data_ptr(); for (int i = 0; i < grids[0].seq2grid.size(); ++i) { positions_out_ptr[i] = (i < grids[0].num_origin_seq); } for (int i = 0; i < grids.size(); ++i) { grid_neighbors[i] = torch::zeros({static_cast(grids[i].seq2grid.size()), static_cast(9)}, int64_options); int64_t* nptr = grid_neighbors[i].data_ptr(); for (int j = 0; j < grids[i].seq2neighbor.size(); ++j) { nptr[j] = grids[i].seq2neighbor[j]; } grid_evencorners[i] = torch::zeros({static_cast(grids[i].seq2evencorner.size())}, int64_options); grid_oddcorners[i] = torch::zeros({static_cast(grids[i].seq2oddcorner.size())}, int64_options); int64_t* dptr = grid_evencorners[i].data_ptr(); for (int j = 0; j < grids[i].seq2evencorner.size(); ++j) { dptr[j] = grids[i].seq2evencorner[j]; } dptr = grid_oddcorners[i].data_ptr(); for (int j = 0; j < grids[i].seq2oddcorner.size(); ++j) { dptr[j] = grids[i].seq2oddcorner[j]; } if (i + 1 < grids.size()) { grid_downsamples[i] = torch::zeros({static_cast(grids[i].downsample_seq.size())}, int64_options); int64_t* dptr = grid_downsamples[i].data_ptr(); for (int j = 0; j < grids[i].downsample_seq.size(); ++j) { dptr[j] = grids[i].downsample_seq[j]; } } } return {texture_positions, grid_neighbors, grid_downsamples, grid_evencorners, grid_oddcorners}; } std::vector> build_hierarchy_with_feat( std::vector view_layer_positions, std::vector view_layer_normals, std::vector view_layer_feats, int num_level, int resolution) { if (view_layer_positions.size() != 3 || num_level < 1) { printf("Alert! We require 3 layers and at least 1 level! (%d %d)\n", view_layer_positions.size(), num_level); return {{},{},{},{}}; } std::vector grids; grids.resize(num_level); std::vector seq2pos; std::vector seq2feat; auto& seq2grid = grids[0].seq2grid; auto& seq2normal = grids[0].seq2normal; auto& grid2seq = grids[0].grid2seq; grids[0].resolution = resolution; grids[0].stride = 1; auto int64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false); auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false); int feat_channel = 3; for (int v = 0; v < 3; ++v) { int num_layers = view_layer_positions[v].size(0); int height = view_layer_positions[v].size(1); int width = view_layer_positions[v].size(2); float* data = view_layer_positions[v].data_ptr(); float* data_normal = view_layer_normals[v].data_ptr(); float* data_feat = view_layer_feats[v].data_ptr(); feat_channel = view_layer_feats[v].size(3); for (int l = 0; l < num_layers; ++l) { for (int i = 0; i < height; ++i) { for (int j = 0; j < width; ++j) { float* p = &data[(i * width + j) * 4]; float* n = &data_normal[(i * width + j) * 3]; float* f = &data_feat[(i * width + j) * feat_channel]; if (p[3] == 0) continue; auto k = pos2key(p, resolution); if (!grid2seq.count(k)) { int dim = 0; for (int d = 0; d < 3; ++d) { if (std::abs(n[d]) > std::abs(n[dim])) dim = d; } dim = (dim + 1) % 3; grid2seq[k] = seq2grid.size(); seq2grid.emplace_back(k); seq2pos.push_back(p[0]); seq2pos.push_back(p[1]); seq2pos.push_back(p[2]); for (int c = 0; c < feat_channel; ++c) { seq2feat.emplace_back(f[c]); } seq2normal.emplace_back(dim); } } } data += (height * width * 4); data_normal += (height * width * 3); data_feat += (height * width * feat_channel); } } for (int i = 0; i < num_level - 1; ++i) { DownsampleGrid(grids[i], grids[i + 1]); } for (int l = 0; l < num_level; ++l) { grids[l].seq2neighbor.resize(grids[l].seq2grid.size() * 9, -1); grids[l].num_origin_seq = grids[l].seq2grid.size(); for (int d = 0; d < 3; ++d) { NeighborGrid(grids[l], view_layer_positions, d); } } for (int i = num_level - 2; i >= 0; --i) { PadGrid(grids[i], grids[i + 1], view_layer_positions); } for (int i = grids[0].num_origin_seq; i < grids[0].seq2grid.size(); ++i) { int k = grids[0].seq2grid[i]; float p[3]; key2pos(k, grids[0].resolution, p); seq2pos.push_back(p[0]); seq2pos.push_back(p[1]); seq2pos.push_back(p[2]); for (int c = 0; c < feat_channel; ++c) { seq2feat.emplace_back(0.5); } } std::vector texture_positions(2); std::vector texture_feats(1); std::vector grid_neighbors(grids.size()); std::vector grid_downsamples(grids.size() - 1); std::vector grid_evencorners(grids.size()); std::vector grid_oddcorners(grids.size()); texture_positions[0] = torch::zeros({static_cast(seq2pos.size() / 3), static_cast(3)}, float_options); texture_positions[1] = torch::zeros({static_cast(seq2pos.size() / 3)}, float_options); texture_feats[0] = torch::zeros({static_cast(seq2feat.size() / feat_channel), static_cast(feat_channel)}, float_options); float* positions_out_ptr = texture_positions[0].data_ptr(); memcpy(positions_out_ptr, seq2pos.data(), sizeof(float) * seq2pos.size()); positions_out_ptr = texture_positions[1].data_ptr(); for (int i = 0; i < grids[0].seq2grid.size(); ++i) { positions_out_ptr[i] = (i < grids[0].num_origin_seq); } float* feats_out_ptr = texture_feats[0].data_ptr(); memcpy(feats_out_ptr, seq2feat.data(), sizeof(float) * seq2feat.size()); for (int i = 0; i < grids.size(); ++i) { grid_neighbors[i] = torch::zeros({static_cast(grids[i].seq2grid.size()), static_cast(9)}, int64_options); int64_t* nptr = grid_neighbors[i].data_ptr(); for (int j = 0; j < grids[i].seq2neighbor.size(); ++j) { nptr[j] = grids[i].seq2neighbor[j]; } grid_evencorners[i] = torch::zeros({static_cast(grids[i].seq2evencorner.size())}, int64_options); grid_oddcorners[i] = torch::zeros({static_cast(grids[i].seq2oddcorner.size())}, int64_options); int64_t* dptr = grid_evencorners[i].data_ptr(); for (int j = 0; j < grids[i].seq2evencorner.size(); ++j) { dptr[j] = grids[i].seq2evencorner[j]; } dptr = grid_oddcorners[i].data_ptr(); for (int j = 0; j < grids[i].seq2oddcorner.size(); ++j) { dptr[j] = grids[i].seq2oddcorner[j]; } if (i + 1 < grids.size()) { grid_downsamples[i] = torch::zeros({static_cast(grids[i].downsample_seq.size())}, int64_options); int64_t* dptr = grid_downsamples[i].data_ptr(); for (int j = 0; j < grids[i].downsample_seq.size(); ++j) { dptr[j] = grids[i].downsample_seq[j]; } } } return {texture_positions, texture_feats, grid_neighbors, grid_downsamples, grid_evencorners, grid_oddcorners}; } ================================================ FILE: hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.cpp ================================================ #include "rasterizer.h" void rasterizeTriangleCPU(int idx, float* vt0, float* vt1, float* vt2, int width, int height, INT64* zbuffer, float* d, float occlusion_truncation) { float x_min = std::min(vt0[0], std::min(vt1[0],vt2[0])); float x_max = std::max(vt0[0], std::max(vt1[0],vt2[0])); float y_min = std::min(vt0[1], std::min(vt1[1],vt2[1])); float y_max = std::max(vt0[1], std::max(vt1[1],vt2[1])); for (int px = x_min; px < x_max + 1; ++px) { if (px < 0 || px >= width) continue; for (int py = y_min; py < y_max + 1; ++py) { if (py < 0 || py >= height) continue; float vt[2] = {px + 0.5, py + 0.5}; float baryCentricCoordinate[3]; calculateBarycentricCoordinate(vt0, vt1, vt2, vt, baryCentricCoordinate); if (isBarycentricCoordInBounds(baryCentricCoordinate)) { int pixel = py * width + px; if (zbuffer == 0) { zbuffer[pixel] = (INT64)(idx + 1); continue; } float depth = baryCentricCoordinate[0] * vt0[2] + baryCentricCoordinate[1] * vt1[2] + baryCentricCoordinate[2] * vt2[2]; float depth_thres = 0; if (d) { depth_thres = d[pixel] * 0.49999f + 0.5f + occlusion_truncation; } int z_quantize = depth * (2<<17); INT64 token = (INT64)z_quantize * MAXINT + (INT64)(idx + 1); if (depth < depth_thres) continue; zbuffer[pixel] = std::min(zbuffer[pixel], token); } } } } void barycentricFromImgcoordCPU(float* V, int* F, int* findices, INT64* zbuffer, int width, int height, int num_vertices, int num_faces, float* barycentric_map, int pix) { INT64 f = zbuffer[pix] % MAXINT; if (f == (MAXINT-1)) { findices[pix] = 0; barycentric_map[pix * 3] = 0; barycentric_map[pix * 3 + 1] = 0; barycentric_map[pix * 3 + 2] = 0; return; } findices[pix] = f; f -= 1; float barycentric[3] = {0, 0, 0}; if (f >= 0) { float vt[2] = {float(pix % width) + 0.5f, float(pix / width) + 0.5f}; float* vt0_ptr = V + (F[f * 3] * 4); float* vt1_ptr = V + (F[f * 3 + 1] * 4); float* vt2_ptr = V + (F[f * 3 + 2] * 4); float vt0[2] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f}; float vt1[2] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f}; float vt2[2] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f}; calculateBarycentricCoordinate(vt0, vt1, vt2, vt, barycentric); barycentric[0] = barycentric[0] / vt0_ptr[3]; barycentric[1] = barycentric[1] / vt1_ptr[3]; barycentric[2] = barycentric[2] / vt2_ptr[3]; float w = 1.0f / (barycentric[0] + barycentric[1] + barycentric[2]); barycentric[0] *= w; barycentric[1] *= w; barycentric[2] *= w; } barycentric_map[pix * 3] = barycentric[0]; barycentric_map[pix * 3 + 1] = barycentric[1]; barycentric_map[pix * 3 + 2] = barycentric[2]; } void rasterizeImagecoordsKernelCPU(float* V, int* F, float* d, INT64* zbuffer, float occlusion_trunc, int width, int height, int num_vertices, int num_faces, int f) { float* vt0_ptr = V + (F[f * 3] * 4); float* vt1_ptr = V + (F[f * 3 + 1] * 4); float* vt2_ptr = V + (F[f * 3 + 2] * 4); float vt0[3] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f, vt0_ptr[2] / vt0_ptr[3] * 0.49999f + 0.5f}; float vt1[3] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f, vt1_ptr[2] / vt1_ptr[3] * 0.49999f + 0.5f}; float vt2[3] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f, vt2_ptr[2] / vt2_ptr[3] * 0.49999f + 0.5f}; rasterizeTriangleCPU(f, vt0, vt1, vt2, width, height, zbuffer, d, occlusion_trunc); } std::vector rasterize_image_cpu(torch::Tensor V, torch::Tensor F, torch::Tensor D, int width, int height, float occlusion_truncation, int use_depth_prior) { int num_faces = F.size(0); int num_vertices = V.size(0); auto options = torch::TensorOptions().dtype(torch::kInt32).requires_grad(false); auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).requires_grad(false); auto findices = torch::zeros({height, width}, options); INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1); auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint; if (!use_depth_prior) { for (int i = 0; i < num_faces; ++i) { rasterizeImagecoordsKernelCPU(V.data_ptr(), F.data_ptr(), 0, (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces, i); } } else { for (int i = 0; i < num_faces; ++i) rasterizeImagecoordsKernelCPU(V.data_ptr(), F.data_ptr(), D.data_ptr(), (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces, i); } auto float_options = torch::TensorOptions().dtype(torch::kFloat32).requires_grad(false); auto barycentric = torch::zeros({height, width, 3}, float_options); for (int i = 0; i < width * height; ++i) barycentricFromImgcoordCPU(V.data_ptr(), F.data_ptr(), findices.data_ptr(), (INT64*)z_min.data_ptr(), width, height, num_vertices, num_faces, barycentric.data_ptr(), i); return {findices, barycentric}; } std::vector rasterize_image(torch::Tensor V, torch::Tensor F, torch::Tensor D, int width, int height, float occlusion_truncation, int use_depth_prior) { int device_id = V.get_device(); if (device_id == -1) return rasterize_image_cpu(V, F, D, width, height, occlusion_truncation, use_depth_prior); else return rasterize_image_gpu(V, F, D, width, height, occlusion_truncation, use_depth_prior); } PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("rasterize_image", &rasterize_image, "Custom image rasterization"); m.def("build_hierarchy", &build_hierarchy, "Custom image rasterization"); m.def("build_hierarchy_with_feat", &build_hierarchy_with_feat, "Custom image rasterization"); } ================================================ FILE: hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer.h ================================================ #ifndef RASTERIZER_H_ #define RASTERIZER_H_ #include #include #include #include // For CUDA context #define INT64 unsigned long long #define MAXINT 2147483647 __host__ __device__ inline float calculateSignedArea2(float* a, float* b, float* c) { return ((c[0] - a[0]) * (b[1] - a[1]) - (b[0] - a[0]) * (c[1] - a[1])); } __host__ __device__ inline void calculateBarycentricCoordinate(float* a, float* b, float* c, float* p, float* barycentric) { float beta_tri = calculateSignedArea2(a, p, c); float gamma_tri = calculateSignedArea2(a, b, p); float area = calculateSignedArea2(a, b, c); if (area == 0) { barycentric[0] = -1.0; barycentric[1] = -1.0; barycentric[2] = -1.0; return; } float tri_inv = 1.0 / area; float beta = beta_tri * tri_inv; float gamma = gamma_tri * tri_inv; float alpha = 1.0 - beta - gamma; barycentric[0] = alpha; barycentric[1] = beta; barycentric[2] = gamma; } __host__ __device__ inline bool isBarycentricCoordInBounds(float* barycentricCoord) { return barycentricCoord[0] >= 0.0 && barycentricCoord[0] <= 1.0 && barycentricCoord[1] >= 0.0 && barycentricCoord[1] <= 1.0 && barycentricCoord[2] >= 0.0 && barycentricCoord[2] <= 1.0; } std::vector rasterize_image_gpu(torch::Tensor V, torch::Tensor F, torch::Tensor D, int width, int height, float occlusion_truncation, int use_depth_prior); std::vector> build_hierarchy(std::vector view_layer_positions, std::vector view_layer_normals, int num_level, int resolution); std::vector> build_hierarchy_with_feat( std::vector view_layer_positions, std::vector view_layer_normals, std::vector view_layer_feats, int num_level, int resolution); #endif ================================================ FILE: hy3dgen/texgen/custom_rasterizer/lib/custom_rasterizer_kernel/rasterizer_gpu.cu ================================================ #include "rasterizer.h" __device__ void rasterizeTriangleGPU(int idx, float* vt0, float* vt1, float* vt2, int width, int height, INT64* zbuffer, float* d, float occlusion_truncation) { float x_min = std::min(vt0[0], std::min(vt1[0],vt2[0])); float x_max = std::max(vt0[0], std::max(vt1[0],vt2[0])); float y_min = std::min(vt0[1], std::min(vt1[1],vt2[1])); float y_max = std::max(vt0[1], std::max(vt1[1],vt2[1])); for (int px = x_min; px < x_max + 1; ++px) { if (px < 0 || px >= width) continue; for (int py = y_min; py < y_max + 1; ++py) { if (py < 0 || py >= height) continue; float vt[2] = {px + 0.5f, py + 0.5f}; float baryCentricCoordinate[3]; calculateBarycentricCoordinate(vt0, vt1, vt2, vt, baryCentricCoordinate); if (isBarycentricCoordInBounds(baryCentricCoordinate)) { int pixel = py * width + px; if (zbuffer == 0) { atomicExch(&zbuffer[pixel], (INT64)(idx + 1)); continue; } float depth = baryCentricCoordinate[0] * vt0[2] + baryCentricCoordinate[1] * vt1[2] + baryCentricCoordinate[2] * vt2[2]; float depth_thres = 0; if (d) { depth_thres = d[pixel] * 0.49999f + 0.5f + occlusion_truncation; } int z_quantize = depth * (2<<17); INT64 token = (INT64)z_quantize * MAXINT + (INT64)(idx + 1); if (depth < depth_thres) continue; atomicMin(&zbuffer[pixel], token); } } } } __global__ void barycentricFromImgcoordGPU(float* V, int* F, int* findices, INT64* zbuffer, int width, int height, int num_vertices, int num_faces, float* barycentric_map) { int pix = blockIdx.x * blockDim.x + threadIdx.x; if (pix >= width * height) return; INT64 f = zbuffer[pix] % MAXINT; if (f == (MAXINT-1)) { findices[pix] = 0; barycentric_map[pix * 3] = 0; barycentric_map[pix * 3 + 1] = 0; barycentric_map[pix * 3 + 2] = 0; return; } findices[pix] = f; f -= 1; float barycentric[3] = {0, 0, 0}; if (f >= 0) { float vt[2] = {float(pix % width) + 0.5f, float(pix / width) + 0.5f}; float* vt0_ptr = V + (F[f * 3] * 4); float* vt1_ptr = V + (F[f * 3 + 1] * 4); float* vt2_ptr = V + (F[f * 3 + 2] * 4); float vt0[2] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f}; float vt1[2] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f}; float vt2[2] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f}; calculateBarycentricCoordinate(vt0, vt1, vt2, vt, barycentric); barycentric[0] = barycentric[0] / vt0_ptr[3]; barycentric[1] = barycentric[1] / vt1_ptr[3]; barycentric[2] = barycentric[2] / vt2_ptr[3]; float w = 1.0f / (barycentric[0] + barycentric[1] + barycentric[2]); barycentric[0] *= w; barycentric[1] *= w; barycentric[2] *= w; } barycentric_map[pix * 3] = barycentric[0]; barycentric_map[pix * 3 + 1] = barycentric[1]; barycentric_map[pix * 3 + 2] = barycentric[2]; } __global__ void rasterizeImagecoordsKernelGPU(float* V, int* F, float* d, INT64* zbuffer, float occlusion_trunc, int width, int height, int num_vertices, int num_faces) { int f = blockIdx.x * blockDim.x + threadIdx.x; if (f >= num_faces) return; float* vt0_ptr = V + (F[f * 3] * 4); float* vt1_ptr = V + (F[f * 3 + 1] * 4); float* vt2_ptr = V + (F[f * 3 + 2] * 4); float vt0[3] = {(vt0_ptr[0] / vt0_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt0_ptr[1] / vt0_ptr[3]) * (height - 1) + 0.5f, vt0_ptr[2] / vt0_ptr[3] * 0.49999f + 0.5f}; float vt1[3] = {(vt1_ptr[0] / vt1_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt1_ptr[1] / vt1_ptr[3]) * (height - 1) + 0.5f, vt1_ptr[2] / vt1_ptr[3] * 0.49999f + 0.5f}; float vt2[3] = {(vt2_ptr[0] / vt2_ptr[3] * 0.5f + 0.5f) * (width - 1) + 0.5f, (0.5f + 0.5f * vt2_ptr[1] / vt2_ptr[3]) * (height - 1) + 0.5f, vt2_ptr[2] / vt2_ptr[3] * 0.49999f + 0.5f}; rasterizeTriangleGPU(f, vt0, vt1, vt2, width, height, zbuffer, d, occlusion_trunc); } std::vector rasterize_image_gpu(torch::Tensor V, torch::Tensor F, torch::Tensor D, int width, int height, float occlusion_truncation, int use_depth_prior) { int device_id = V.get_device(); cudaSetDevice(device_id); int num_faces = F.size(0); int num_vertices = V.size(0); auto options = torch::TensorOptions().dtype(torch::kInt32).device(torch::kCUDA, device_id).requires_grad(false); auto INT64_options = torch::TensorOptions().dtype(torch::kInt64).device(torch::kCUDA, device_id).requires_grad(false); auto findices = torch::zeros({height, width}, options); INT64 maxint = (INT64)MAXINT * (INT64)MAXINT + (MAXINT - 1); auto z_min = torch::ones({height, width}, INT64_options) * (int64_t)maxint; if (!use_depth_prior) { rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr(), F.data_ptr(), 0, (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces); } else { rasterizeImagecoordsKernelGPU<<<(num_faces+255)/256,256,0,at::cuda::getCurrentCUDAStream()>>>(V.data_ptr(), F.data_ptr(), D.data_ptr(), (INT64*)z_min.data_ptr(), occlusion_truncation, width, height, num_vertices, num_faces); } auto float_options = torch::TensorOptions().dtype(torch::kFloat32).device(torch::kCUDA, device_id).requires_grad(false); auto barycentric = torch::zeros({height, width, 3}, float_options); barycentricFromImgcoordGPU<<<(width * height + 255)/256, 256>>>(V.data_ptr(), F.data_ptr(), findices.data_ptr(), (INT64*)z_min.data_ptr(), width, height, num_vertices, num_faces, barycentric.data_ptr()); return {findices, barycentric}; } ================================================ FILE: hy3dgen/texgen/custom_rasterizer/setup.py ================================================ from setuptools import setup, find_packages from torch.utils.cpp_extension import BuildExtension, CUDAExtension # build custom rasterizer # build with `python setup.py install` # nvcc is needed custom_rasterizer_module = CUDAExtension('custom_rasterizer_kernel', [ 'lib/custom_rasterizer_kernel/rasterizer.cpp', 'lib/custom_rasterizer_kernel/grid_neighbor.cpp', 'lib/custom_rasterizer_kernel/rasterizer_gpu.cu', ]) setup( packages=find_packages(), version='0.1', name='custom_rasterizer', include_package_data=True, package_dir={'': '.'}, ext_modules=[ custom_rasterizer_module, ], cmdclass={ 'build_ext': BuildExtension } ) ================================================ FILE: hy3dgen/texgen/differentiable_renderer/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. ================================================ FILE: hy3dgen/texgen/differentiable_renderer/camera_utils.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import math import numpy as np import torch def transform_pos(mtx, pos, keepdim=False): t_mtx = torch.from_numpy(mtx).to( pos.device) if isinstance( mtx, np.ndarray) else mtx if pos.shape[-1] == 3: posw = torch.cat( [pos, torch.ones([pos.shape[0], 1]).to(pos.device)], axis=1) else: posw = pos if keepdim: return torch.matmul(posw, t_mtx.t())[...] else: return torch.matmul(posw, t_mtx.t())[None, ...] def get_mv_matrix(elev, azim, camera_distance, center=None): elev = -elev azim += 90 elev_rad = math.radians(elev) azim_rad = math.radians(azim) camera_position = np.array([camera_distance * math.cos(elev_rad) * math.cos(azim_rad), camera_distance * math.cos(elev_rad) * math.sin(azim_rad), camera_distance * math.sin(elev_rad)]) if center is None: center = np.array([0, 0, 0]) else: center = np.array(center) lookat = center - camera_position lookat = lookat / np.linalg.norm(lookat) up = np.array([0, 0, 1.0]) right = np.cross(lookat, up) right = right / np.linalg.norm(right) up = np.cross(right, lookat) up = up / np.linalg.norm(up) c2w = np.concatenate( [np.stack([right, up, -lookat], axis=-1), camera_position[:, None]], axis=-1) w2c = np.zeros((4, 4)) w2c[:3, :3] = np.transpose(c2w[:3, :3], (1, 0)) w2c[:3, 3:] = -np.matmul(np.transpose(c2w[:3, :3], (1, 0)), c2w[:3, 3:]) w2c[3, 3] = 1.0 return w2c.astype(np.float32) def get_orthographic_projection_matrix( left=-1, right=1, bottom=-1, top=1, near=0, far=2): """ 计算正交投影矩阵。 参数: left (float): 投影区域左侧边界。 right (float): 投影区域右侧边界。 bottom (float): 投影区域底部边界。 top (float): 投影区域顶部边界。 near (float): 投影区域近裁剪面距离。 far (float): 投影区域远裁剪面距离。 返回: numpy.ndarray: 正交投影矩阵。 """ ortho_matrix = np.eye(4, dtype=np.float32) ortho_matrix[0, 0] = 2 / (right - left) ortho_matrix[1, 1] = 2 / (top - bottom) ortho_matrix[2, 2] = -2 / (far - near) ortho_matrix[0, 3] = -(right + left) / (right - left) ortho_matrix[1, 3] = -(top + bottom) / (top - bottom) ortho_matrix[2, 3] = -(far + near) / (far - near) return ortho_matrix def get_perspective_projection_matrix(fovy, aspect_wh, near, far): fovy_rad = math.radians(fovy) return np.array([[1.0 / (math.tan(fovy_rad / 2.0) * aspect_wh), 0, 0, 0], [0, 1.0 / math.tan(fovy_rad / 2.0), 0, 0], [0, 0, -(far + near) / (far - near), - 2.0 * far * near / (far - near)], [0, 0, -1, 0]]).astype(np.float32) ================================================ FILE: hy3dgen/texgen/differentiable_renderer/compile_mesh_painter.bat ================================================ FOR /F "tokens=*" %%i IN ('python -m pybind11 --includes') DO SET PYINCLUDES=%%i echo %PYINCLUDES% g++ -O3 -Wall -shared -std=c++11 -fPIC %PYINCLUDES% mesh_processor.cpp -o mesh_processor.pyd -lpython3.12 ================================================ FILE: hy3dgen/texgen/differentiable_renderer/mesh_processor.cpp ================================================ #include #include #include #include #include #include #include namespace py = pybind11; using namespace std; std::pair, py::array_t> meshVerticeInpaint_smooth(py::array_t texture, py::array_t mask, py::array_t vtx_pos, py::array_t vtx_uv, py::array_t pos_idx, py::array_t uv_idx) { auto texture_buf = texture.request(); auto mask_buf = mask.request(); auto vtx_pos_buf = vtx_pos.request(); auto vtx_uv_buf = vtx_uv.request(); auto pos_idx_buf = pos_idx.request(); auto uv_idx_buf = uv_idx.request(); int texture_height = texture_buf.shape[0]; int texture_width = texture_buf.shape[1]; int texture_channel = texture_buf.shape[2]; float* texture_ptr = static_cast(texture_buf.ptr); uint8_t* mask_ptr = static_cast(mask_buf.ptr); int vtx_num = vtx_pos_buf.shape[0]; float* vtx_pos_ptr = static_cast(vtx_pos_buf.ptr); float* vtx_uv_ptr = static_cast(vtx_uv_buf.ptr); int* pos_idx_ptr = static_cast(pos_idx_buf.ptr); int* uv_idx_ptr = static_cast(uv_idx_buf.ptr); vector vtx_mask(vtx_num, 0.0f); vector> vtx_color(vtx_num, vector(texture_channel, 0.0f)); vector uncolored_vtxs; vector> G(vtx_num); for (int i = 0; i < uv_idx_buf.shape[0]; ++i) { for (int k = 0; k < 3; ++k) { int vtx_uv_idx = uv_idx_ptr[i * 3 + k]; int vtx_idx = pos_idx_ptr[i * 3 + k]; int uv_v = round(vtx_uv_ptr[vtx_uv_idx * 2] * (texture_width - 1)); int uv_u = round((1.0 - vtx_uv_ptr[vtx_uv_idx * 2 + 1]) * (texture_height - 1)); if (mask_ptr[uv_u * texture_width + uv_v] > 0) { vtx_mask[vtx_idx] = 1.0f; for (int c = 0; c < texture_channel; ++c) { vtx_color[vtx_idx][c] = texture_ptr[(uv_u * texture_width + uv_v) * texture_channel + c]; } }else{ uncolored_vtxs.push_back(vtx_idx); } G[pos_idx_ptr[i * 3 + k]].push_back(pos_idx_ptr[i * 3 + (k + 1) % 3]); } } int smooth_count = 2; int last_uncolored_vtx_count = 0; while (smooth_count>0) { int uncolored_vtx_count = 0; for (int vtx_idx : uncolored_vtxs) { vector sum_color(texture_channel, 0.0f); float total_weight = 0.0f; array vtx_0 = {vtx_pos_ptr[vtx_idx * 3], vtx_pos_ptr[vtx_idx * 3 + 1], vtx_pos_ptr[vtx_idx * 3 + 2]}; for (int connected_idx : G[vtx_idx]) { if (vtx_mask[connected_idx] > 0) { array vtx1 = {vtx_pos_ptr[connected_idx * 3], vtx_pos_ptr[connected_idx * 3 + 1], vtx_pos_ptr[connected_idx * 3 + 2]}; float dist_weight = 1.0f / max(sqrt(pow(vtx_0[0] - vtx1[0], 2) + pow(vtx_0[1] - vtx1[1], 2) + \ pow(vtx_0[2] - vtx1[2], 2)), 1E-4); dist_weight = dist_weight * dist_weight; for (int c = 0; c < texture_channel; ++c) { sum_color[c] += vtx_color[connected_idx][c] * dist_weight; } total_weight += dist_weight; } } if (total_weight > 0.0f) { for (int c = 0; c < texture_channel; ++c) { vtx_color[vtx_idx][c] = sum_color[c] / total_weight; } vtx_mask[vtx_idx] = 1.0f; } else { uncolored_vtx_count++; } } if(last_uncolored_vtx_count==uncolored_vtx_count){ smooth_count--; }else{ smooth_count++; } last_uncolored_vtx_count = uncolored_vtx_count; } // Create new arrays for the output py::array_t new_texture(texture_buf.size); py::array_t new_mask(mask_buf.size); auto new_texture_buf = new_texture.request(); auto new_mask_buf = new_mask.request(); float* new_texture_ptr = static_cast(new_texture_buf.ptr); uint8_t* new_mask_ptr = static_cast(new_mask_buf.ptr); // Copy original texture and mask to new arrays std::copy(texture_ptr, texture_ptr + texture_buf.size, new_texture_ptr); std::copy(mask_ptr, mask_ptr + mask_buf.size, new_mask_ptr); for (int face_idx = 0; face_idx < uv_idx_buf.shape[0]; ++face_idx) { for (int k = 0; k < 3; ++k) { int vtx_uv_idx = uv_idx_ptr[face_idx * 3 + k]; int vtx_idx = pos_idx_ptr[face_idx * 3 + k]; if (vtx_mask[vtx_idx] == 1.0f) { int uv_v = round(vtx_uv_ptr[vtx_uv_idx * 2] * (texture_width - 1)); int uv_u = round((1.0 - vtx_uv_ptr[vtx_uv_idx * 2 + 1]) * (texture_height - 1)); for (int c = 0; c < texture_channel; ++c) { new_texture_ptr[(uv_u * texture_width + uv_v) * texture_channel + c] = vtx_color[vtx_idx][c]; } new_mask_ptr[uv_u * texture_width + uv_v] = 255; } } } // Reshape the new arrays to match the original texture and mask shapes new_texture.resize({texture_height, texture_width, 3}); new_mask.resize({texture_height, texture_width}); return std::make_pair(new_texture, new_mask); } std::pair, py::array_t> meshVerticeInpaint(py::array_t texture, py::array_t mask, py::array_t vtx_pos, py::array_t vtx_uv, py::array_t pos_idx, py::array_t uv_idx, const std::string& method = "smooth") { if (method == "smooth") { return meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx); } else { throw std::invalid_argument("Invalid method. Use 'smooth' or 'forward'."); } } PYBIND11_MODULE(mesh_processor, m) { m.def("meshVerticeInpaint", &meshVerticeInpaint, "A function to process mesh", py::arg("texture"), py::arg("mask"), py::arg("vtx_pos"), py::arg("vtx_uv"), py::arg("pos_idx"), py::arg("uv_idx"), py::arg("method") = "smooth"); } ================================================ FILE: hy3dgen/texgen/differentiable_renderer/mesh_processor.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import numpy as np def meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx): texture_height, texture_width, texture_channel = texture.shape vtx_num = vtx_pos.shape[0] vtx_mask = np.zeros(vtx_num, dtype=np.float32) vtx_color = [np.zeros(texture_channel, dtype=np.float32) for _ in range(vtx_num)] uncolored_vtxs = [] G = [[] for _ in range(vtx_num)] for i in range(uv_idx.shape[0]): for k in range(3): vtx_uv_idx = uv_idx[i, k] vtx_idx = pos_idx[i, k] uv_v = int(round(vtx_uv[vtx_uv_idx, 0] * (texture_width - 1))) uv_u = int(round((1.0 - vtx_uv[vtx_uv_idx, 1]) * (texture_height - 1))) if mask[uv_u, uv_v] > 0: vtx_mask[vtx_idx] = 1.0 vtx_color[vtx_idx] = texture[uv_u, uv_v] else: uncolored_vtxs.append(vtx_idx) G[pos_idx[i, k]].append(pos_idx[i, (k + 1) % 3]) smooth_count = 2 last_uncolored_vtx_count = 0 while smooth_count > 0: uncolored_vtx_count = 0 for vtx_idx in uncolored_vtxs: sum_color = np.zeros(texture_channel, dtype=np.float32) total_weight = 0.0 vtx_0 = vtx_pos[vtx_idx] for connected_idx in G[vtx_idx]: if vtx_mask[connected_idx] > 0: vtx1 = vtx_pos[connected_idx] dist = np.sqrt(np.sum((vtx_0 - vtx1) ** 2)) dist_weight = 1.0 / max(dist, 1e-4) dist_weight *= dist_weight sum_color += vtx_color[connected_idx] * dist_weight total_weight += dist_weight if total_weight > 0: vtx_color[vtx_idx] = sum_color / total_weight vtx_mask[vtx_idx] = 1.0 else: uncolored_vtx_count += 1 if last_uncolored_vtx_count == uncolored_vtx_count: smooth_count -= 1 else: smooth_count += 1 last_uncolored_vtx_count = uncolored_vtx_count new_texture = texture.copy() new_mask = mask.copy() for face_idx in range(uv_idx.shape[0]): for k in range(3): vtx_uv_idx = uv_idx[face_idx, k] vtx_idx = pos_idx[face_idx, k] if vtx_mask[vtx_idx] == 1.0: uv_v = int(round(vtx_uv[vtx_uv_idx, 0] * (texture_width - 1))) uv_u = int(round((1.0 - vtx_uv[vtx_uv_idx, 1]) * (texture_height - 1))) new_texture[uv_u, uv_v] = vtx_color[vtx_idx] new_mask[uv_u, uv_v] = 255 return new_texture, new_mask def meshVerticeInpaint(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx, method="smooth"): if method == "smooth": return meshVerticeInpaint_smooth(texture, mask, vtx_pos, vtx_uv, pos_idx, uv_idx) else: raise ValueError("Invalid method. Use 'smooth' or 'forward'.") ================================================ FILE: hy3dgen/texgen/differentiable_renderer/mesh_render.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import cv2 import numpy as np import torch import torch.nn.functional as F import trimesh from PIL import Image from .camera_utils import ( transform_pos, get_mv_matrix, get_orthographic_projection_matrix, get_perspective_projection_matrix, ) from .mesh_processor import meshVerticeInpaint from .mesh_utils import load_mesh, save_mesh def stride_from_shape(shape): stride = [1] for x in reversed(shape[1:]): stride.append(stride[-1] * x) return list(reversed(stride)) def scatter_add_nd_with_count(input, count, indices, values, weights=None): # input: [..., C], D dimension + C channel # count: [..., 1], D dimension # indices: [N, D], long # values: [N, C] D = indices.shape[-1] C = input.shape[-1] size = input.shape[:-1] stride = stride_from_shape(size) assert len(size) == D input = input.view(-1, C) # [HW, C] count = count.view(-1, 1) flatten_indices = (indices * torch.tensor(stride, dtype=torch.long, device=indices.device)).sum(-1) # [N] if weights is None: weights = torch.ones_like(values[..., :1]) input.scatter_add_(0, flatten_indices.unsqueeze(1).repeat(1, C), values) count.scatter_add_(0, flatten_indices.unsqueeze(1), weights) return input.view(*size, C), count.view(*size, 1) def linear_grid_put_2d(H, W, coords, values, return_count=False): # coords: [N, 2], float in [0, 1] # values: [N, C] C = values.shape[-1] indices = coords * torch.tensor( [H - 1, W - 1], dtype=torch.float32, device=coords.device ) indices_00 = indices.floor().long() # [N, 2] indices_00[:, 0].clamp_(0, H - 2) indices_00[:, 1].clamp_(0, W - 2) indices_01 = indices_00 + torch.tensor( [0, 1], dtype=torch.long, device=indices.device ) indices_10 = indices_00 + torch.tensor( [1, 0], dtype=torch.long, device=indices.device ) indices_11 = indices_00 + torch.tensor( [1, 1], dtype=torch.long, device=indices.device ) h = indices[..., 0] - indices_00[..., 0].float() w = indices[..., 1] - indices_00[..., 1].float() w_00 = (1 - h) * (1 - w) w_01 = (1 - h) * w w_10 = h * (1 - w) w_11 = h * w result = torch.zeros(H, W, C, device=values.device, dtype=values.dtype) # [H, W, C] count = torch.zeros(H, W, 1, device=values.device, dtype=values.dtype) # [H, W, 1] weights = torch.ones_like(values[..., :1]) # [N, 1] result, count = scatter_add_nd_with_count( result, count, indices_00, values * w_00.unsqueeze(1), weights * w_00.unsqueeze(1)) result, count = scatter_add_nd_with_count( result, count, indices_01, values * w_01.unsqueeze(1), weights * w_01.unsqueeze(1)) result, count = scatter_add_nd_with_count( result, count, indices_10, values * w_10.unsqueeze(1), weights * w_10.unsqueeze(1)) result, count = scatter_add_nd_with_count( result, count, indices_11, values * w_11.unsqueeze(1), weights * w_11.unsqueeze(1)) if return_count: return result, count mask = (count.squeeze(-1) > 0) result[mask] = result[mask] / count[mask].repeat(1, C) return result class MeshRender(): def __init__( self, camera_distance=1.45, camera_type='orth', default_resolution=1024, texture_size=1024, use_antialias=True, max_mip_level=None, filter_mode='linear', bake_mode='linear', raster_mode='cr', device='cuda'): self.device = device self.set_default_render_resolution(default_resolution) self.set_default_texture_resolution(texture_size) self.camera_distance = camera_distance self.use_antialias = use_antialias self.max_mip_level = max_mip_level self.filter_mode = filter_mode self.bake_angle_thres = 75 self.bake_unreliable_kernel_size = int( (2 / 512) * max(self.default_resolution[0], self.default_resolution[1])) self.bake_mode = bake_mode self.raster_mode = raster_mode if self.raster_mode == 'cr': import custom_rasterizer as cr self.raster = cr else: raise f'No raster named {self.raster_mode}' if camera_type == 'orth': self.ortho_scale = 1.2 self.camera_proj_mat = get_orthographic_projection_matrix( left=-self.ortho_scale * 0.5, right=self.ortho_scale * 0.5, bottom=-self.ortho_scale * 0.5, top=self.ortho_scale * 0.5, near=0.1, far=100 ) elif camera_type == 'perspective': self.camera_proj_mat = get_perspective_projection_matrix( 49.13, self.default_resolution[1] / self.default_resolution[0], 0.01, 100.0 ) else: raise f'No camera type {camera_type}' def raster_rasterize(self, pos, tri, resolution, ranges=None, grad_db=True): if self.raster_mode == 'cr': rast_out_db = None if pos.dim() == 2: pos = pos.unsqueeze(0) findices, barycentric = self.raster.rasterize(pos, tri, resolution) rast_out = torch.cat((barycentric, findices.unsqueeze(-1)), dim=-1) rast_out = rast_out.unsqueeze(0) else: raise f'No raster named {self.raster_mode}' return rast_out, rast_out_db def raster_interpolate(self, uv, rast_out, uv_idx, rast_db=None, diff_attrs=None): if self.raster_mode == 'cr': textd = None barycentric = rast_out[0, ..., :-1] findices = rast_out[0, ..., -1] if uv.dim() == 2: uv = uv.unsqueeze(0) textc = self.raster.interpolate(uv, findices, barycentric, uv_idx) else: raise f'No raster named {self.raster_mode}' return textc, textd def raster_texture(self, tex, uv, uv_da=None, mip_level_bias=None, mip=None, filter_mode='auto', boundary_mode='wrap', max_mip_level=None): if self.raster_mode == 'cr': raise f'Texture is not implemented in cr' else: raise f'No raster named {self.raster_mode}' return color def raster_antialias(self, color, rast, pos, tri, topology_hash=None, pos_gradient_boost=1.0): if self.raster_mode == 'cr': # Antialias has not been supported yet color = color else: raise f'No raster named {self.raster_mode}' return color def load_mesh( self, mesh, scale_factor=1.15, auto_center=True, ): vtx_pos, pos_idx, vtx_uv, uv_idx, texture_data = load_mesh(mesh) self.mesh_copy = mesh self.set_mesh(vtx_pos, pos_idx, vtx_uv=vtx_uv, uv_idx=uv_idx, scale_factor=scale_factor, auto_center=auto_center ) if texture_data is not None: self.set_texture(texture_data) def save_mesh(self): texture_data = self.get_texture() texture_data = Image.fromarray((texture_data * 255).astype(np.uint8)) return save_mesh(self.mesh_copy, texture_data) def set_mesh( self, vtx_pos, pos_idx, vtx_uv=None, uv_idx=None, scale_factor=1.15, auto_center=True ): self.vtx_pos = torch.from_numpy(vtx_pos).to(self.device).float() self.pos_idx = torch.from_numpy(pos_idx).to(self.device).to(torch.int) if (vtx_uv is not None) and (uv_idx is not None): self.vtx_uv = torch.from_numpy(vtx_uv).to(self.device).float() self.uv_idx = torch.from_numpy(uv_idx).to(self.device).to(torch.int) else: self.vtx_uv = None self.uv_idx = None self.vtx_pos[:, [0, 1]] = -self.vtx_pos[:, [0, 1]] self.vtx_pos[:, [1, 2]] = self.vtx_pos[:, [2, 1]] if (vtx_uv is not None) and (uv_idx is not None): self.vtx_uv[:, 1] = 1.0 - self.vtx_uv[:, 1] if auto_center: max_bb = (self.vtx_pos - 0).max(0)[0] min_bb = (self.vtx_pos - 0).min(0)[0] center = (max_bb + min_bb) / 2 scale = torch.norm(self.vtx_pos - center, dim=1).max() * 2.0 self.vtx_pos = (self.vtx_pos - center) * \ (scale_factor / float(scale)) self.scale_factor = scale_factor def set_texture(self, tex): if isinstance(tex, np.ndarray): tex = Image.fromarray((tex * 255).astype(np.uint8)) elif isinstance(tex, torch.Tensor): tex = tex.cpu().numpy() tex = Image.fromarray((tex * 255).astype(np.uint8)) tex = tex.resize(self.texture_size).convert('RGB') tex = np.array(tex) / 255.0 self.tex = torch.from_numpy(tex).to(self.device) self.tex = self.tex.float() def set_default_render_resolution(self, default_resolution): if isinstance(default_resolution, int): default_resolution = (default_resolution, default_resolution) self.default_resolution = default_resolution def set_default_texture_resolution(self, texture_size): if isinstance(texture_size, int): texture_size = (texture_size, texture_size) self.texture_size = texture_size def get_mesh(self): vtx_pos = self.vtx_pos.cpu().numpy() pos_idx = self.pos_idx.cpu().numpy() vtx_uv = self.vtx_uv.cpu().numpy() uv_idx = self.uv_idx.cpu().numpy() # 坐标变换的逆变换 vtx_pos[:, [1, 2]] = vtx_pos[:, [2, 1]] vtx_pos[:, [0, 1]] = -vtx_pos[:, [0, 1]] vtx_uv[:, 1] = 1.0 - vtx_uv[:, 1] return vtx_pos, pos_idx, vtx_uv, uv_idx def get_texture(self): return self.tex.cpu().numpy() def to(self, device): self.device = device for attr_name in dir(self): attr_value = getattr(self, attr_name) if isinstance(attr_value, torch.Tensor): setattr(self, attr_name, attr_value.to(self.device)) def color_rgb_to_srgb(self, image): if isinstance(image, Image.Image): image_rgb = torch.tesnor( np.array(image) / 255.0).float().to( self.device) elif isinstance(image, np.ndarray): image_rgb = torch.tensor(image).float() else: image_rgb = image.to(self.device) image_srgb = torch.where( image_rgb <= 0.0031308, 12.92 * image_rgb, 1.055 * torch.pow(image_rgb, 1 / 2.4) - 0.055 ) if isinstance(image, Image.Image): image_srgb = Image.fromarray( (image_srgb.cpu().numpy() * 255).astype( np.uint8)) elif isinstance(image, np.ndarray): image_srgb = image_srgb.cpu().numpy() else: image_srgb = image_srgb.to(image.device) return image_srgb def _render( self, glctx, mvp, pos, pos_idx, uv, uv_idx, tex, resolution, max_mip_level, keep_alpha, filter_mode ): pos_clip = transform_pos(mvp, pos) if isinstance(resolution, (int, float)): resolution = [resolution, resolution] rast_out, rast_out_db = self.raster_rasterize( glctx, pos_clip, pos_idx, resolution=resolution) tex = tex.contiguous() if filter_mode == 'linear-mipmap-linear': texc, texd = self.raster_interpolate( uv[None, ...], rast_out, uv_idx, rast_db=rast_out_db, diff_attrs='all') color = self.raster_texture( tex[None, ...], texc, texd, filter_mode='linear-mipmap-linear', max_mip_level=max_mip_level) else: texc, _ = self.raster_interpolate(uv[None, ...], rast_out, uv_idx) color = self.raster_texture(tex[None, ...], texc, filter_mode=filter_mode) visible_mask = torch.clamp(rast_out[..., -1:], 0, 1) color = color * visible_mask # Mask out background. if self.use_antialias: color = self.raster_antialias(color, rast_out, pos_clip, pos_idx) if keep_alpha: color = torch.cat([color, visible_mask], dim=-1) return color[0, ...] def render( self, elev, azim, camera_distance=None, center=None, resolution=None, tex=None, keep_alpha=True, bgcolor=None, filter_mode=None, return_type='th' ): proj = self.camera_proj_mat r_mv = get_mv_matrix( elev=elev, azim=azim, camera_distance=self.camera_distance if camera_distance is None else camera_distance, center=center) r_mvp = np.matmul(proj, r_mv).astype(np.float32) if tex is not None: if isinstance(tex, Image.Image): tex = torch.tensor(np.array(tex) / 255.0) elif isinstance(tex, np.ndarray): tex = torch.tensor(tex) if tex.dim() == 2: tex = tex.unsqueeze(-1) tex = tex.float().to(self.device) image = self._render(r_mvp, self.vtx_pos, self.pos_idx, self.vtx_uv, self.uv_idx, self.tex if tex is None else tex, self.default_resolution if resolution is None else resolution, self.max_mip_level, True, filter_mode if filter_mode else self.filter_mode) mask = (image[..., [-1]] == 1).float() if bgcolor is None: bgcolor = [0 for _ in range(image.shape[-1] - 1)] image = image * mask + (1 - mask) * \ torch.tensor(bgcolor + [0]).to(self.device) if keep_alpha == False: image = image[..., :-1] if return_type == 'np': image = image.cpu().numpy() elif return_type == 'pl': image = image.squeeze(-1).cpu().numpy() * 255 image = Image.fromarray(image.astype(np.uint8)) return image def render_normal( self, elev, azim, camera_distance=None, center=None, resolution=None, bg_color=[1, 1, 1], use_abs_coor=False, normalize_rgb=True, return_type='th' ): pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center) if resolution is None: resolution = self.default_resolution if isinstance(resolution, (int, float)): resolution = [resolution, resolution] rast_out, rast_out_db = self.raster_rasterize( pos_clip, self.pos_idx, resolution=resolution) if use_abs_coor: mesh_triangles = self.vtx_pos[self.pos_idx[:, :3], :] else: pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4] mesh_triangles = pos_camera[self.pos_idx[:, :3], :] face_normals = F.normalize( torch.cross(mesh_triangles[:, 1, :] - mesh_triangles[:, 0, :], mesh_triangles[:, 2, :] - mesh_triangles[:, 0, :], dim=-1), dim=-1) vertex_normals = trimesh.geometry.mean_vertex_normals(vertex_count=self.vtx_pos.shape[0], faces=self.pos_idx.cpu(), face_normals=face_normals.cpu(), ) vertex_normals = torch.from_numpy( vertex_normals).float().to(self.device).contiguous() # Interpolate normal values across the rasterized pixels normal, _ = self.raster_interpolate( vertex_normals[None, ...], rast_out, self.pos_idx) visible_mask = torch.clamp(rast_out[..., -1:], 0, 1) normal = normal * visible_mask + \ torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 - visible_mask) if normalize_rgb: normal = (normal + 1) * 0.5 if self.use_antialias: normal = self.raster_antialias(normal, rast_out, pos_clip, self.pos_idx) image = normal[0, ...] if return_type == 'np': image = image.cpu().numpy() elif return_type == 'pl': image = image.cpu().numpy() * 255 image = Image.fromarray(image.astype(np.uint8)) return image def convert_normal_map(self, image): # blue is front, red is left, green is top if isinstance(image, Image.Image): image = np.array(image) mask = (image == [255, 255, 255]).all(axis=-1) image = (image / 255.0) * 2.0 - 1.0 image[..., [1]] = -image[..., [1]] image[..., [1, 2]] = image[..., [2, 1]] image[..., [0]] = -image[..., [0]] image = (image + 1.0) * 0.5 image = (image * 255).astype(np.uint8) image[mask] = [127, 127, 255] return Image.fromarray(image) def get_pos_from_mvp(self, elev, azim, camera_distance, center): proj = self.camera_proj_mat r_mv = get_mv_matrix( elev=elev, azim=azim, camera_distance=self.camera_distance if camera_distance is None else camera_distance, center=center) pos_camera = transform_pos(r_mv, self.vtx_pos, keepdim=True) pos_clip = transform_pos(proj, pos_camera) return pos_camera, pos_clip def render_depth( self, elev, azim, camera_distance=None, center=None, resolution=None, return_type='th' ): pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center) if resolution is None: resolution = self.default_resolution if isinstance(resolution, (int, float)): resolution = [resolution, resolution] rast_out, rast_out_db = self.raster_rasterize( pos_clip, self.pos_idx, resolution=resolution) pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4] tex_depth = pos_camera[:, 2].reshape(1, -1, 1).contiguous() # Interpolate depth values across the rasterized pixels depth, _ = self.raster_interpolate(tex_depth, rast_out, self.pos_idx) visible_mask = torch.clamp(rast_out[..., -1:], 0, 1) depth_max, depth_min = depth[visible_mask > 0].max(), depth[visible_mask > 0].min() depth = (depth - depth_min) / (depth_max - depth_min) depth = depth * visible_mask # Mask out background. if self.use_antialias: depth = self.raster_antialias(depth, rast_out, pos_clip, self.pos_idx) image = depth[0, ...] if return_type == 'np': image = image.cpu().numpy() elif return_type == 'pl': image = image.squeeze(-1).cpu().numpy() * 255 image = Image.fromarray(image.astype(np.uint8)) return image def render_position(self, elev, azim, camera_distance=None, center=None, resolution=None, bg_color=[1, 1, 1], return_type='th'): pos_camera, pos_clip = self.get_pos_from_mvp(elev, azim, camera_distance, center) if resolution is None: resolution = self.default_resolution if isinstance(resolution, (int, float)): resolution = [resolution, resolution] rast_out, rast_out_db = self.raster_rasterize( pos_clip, self.pos_idx, resolution=resolution) tex_position = 0.5 - self.vtx_pos[:, :3] / self.scale_factor tex_position = tex_position.contiguous() # Interpolate depth values across the rasterized pixels position, _ = self.raster_interpolate( tex_position[None, ...], rast_out, self.pos_idx) visible_mask = torch.clamp(rast_out[..., -1:], 0, 1) position = position * visible_mask + \ torch.tensor(bg_color, dtype=torch.float32, device=self.device) * (1 - visible_mask) if self.use_antialias: position = self.raster_antialias(position, rast_out, pos_clip, self.pos_idx) image = position[0, ...] if return_type == 'np': image = image.cpu().numpy() elif return_type == 'pl': image = image.squeeze(-1).cpu().numpy() * 255 image = Image.fromarray(image.astype(np.uint8)) return image def render_uvpos(self, return_type='th'): image = self.uv_feature_map(self.vtx_pos * 0.5 + 0.5) if return_type == 'np': image = image.cpu().numpy() elif return_type == 'pl': image = image.cpu().numpy() * 255 image = Image.fromarray(image.astype(np.uint8)) return image def uv_feature_map(self, vert_feat, bg=None): vtx_uv = self.vtx_uv * 2 - 1.0 vtx_uv = torch.cat( [vtx_uv, torch.zeros_like(self.vtx_uv)], dim=1).unsqueeze(0) vtx_uv[..., -1] = 1 uv_idx = self.uv_idx rast_out, rast_out_db = self.raster_rasterize( vtx_uv, uv_idx, resolution=self.texture_size) feat_map, _ = self.raster_interpolate(vert_feat[None, ...], rast_out, uv_idx) feat_map = feat_map[0, ...] if bg is not None: visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)[0, ...] feat_map[visible_mask == 0] = bg return feat_map def render_sketch_from_geometry(self, normal_image, depth_image): normal_image_np = normal_image.cpu().numpy() depth_image_np = depth_image.cpu().numpy() normal_image_np = (normal_image_np * 255).astype(np.uint8) depth_image_np = (depth_image_np * 255).astype(np.uint8) normal_image_np = cv2.cvtColor(normal_image_np, cv2.COLOR_RGB2GRAY) normal_edges = cv2.Canny(normal_image_np, 80, 150) depth_edges = cv2.Canny(depth_image_np, 30, 80) combined_edges = np.maximum(normal_edges, depth_edges) sketch_image = torch.from_numpy(combined_edges).to( normal_image.device).float() / 255.0 sketch_image = sketch_image.unsqueeze(-1) return sketch_image def render_sketch_from_depth(self, depth_image): depth_image_np = depth_image.cpu().numpy() depth_image_np = (depth_image_np * 255).astype(np.uint8) depth_edges = cv2.Canny(depth_image_np, 30, 80) combined_edges = depth_edges sketch_image = torch.from_numpy(combined_edges).to( depth_image.device).float() / 255.0 sketch_image = sketch_image.unsqueeze(-1) return sketch_image def back_project(self, image, elev, azim, camera_distance=None, center=None, method=None): if isinstance(image, Image.Image): image = torch.tensor(np.array(image) / 255.0) elif isinstance(image, np.ndarray): image = torch.tensor(image) if image.dim() == 2: image = image.unsqueeze(-1) image = image.float().to(self.device) resolution = image.shape[:2] channel = image.shape[-1] texture = torch.zeros(self.texture_size + (channel,)).to(self.device) cos_map = torch.zeros(self.texture_size + (1,)).to(self.device) proj = self.camera_proj_mat r_mv = get_mv_matrix( elev=elev, azim=azim, camera_distance=self.camera_distance if camera_distance is None else camera_distance, center=center) pos_camera = transform_pos(r_mv, self.vtx_pos, keepdim=True) pos_clip = transform_pos(proj, pos_camera) pos_camera = pos_camera[:, :3] / pos_camera[:, 3:4] v0 = pos_camera[self.pos_idx[:, 0], :] v1 = pos_camera[self.pos_idx[:, 1], :] v2 = pos_camera[self.pos_idx[:, 2], :] face_normals = F.normalize( torch.cross( v1 - v0, v2 - v0, dim=-1), dim=-1) vertex_normals = trimesh.geometry.mean_vertex_normals(vertex_count=self.vtx_pos.shape[0], faces=self.pos_idx.cpu(), face_normals=face_normals.cpu(), ) vertex_normals = torch.from_numpy( vertex_normals).float().to(self.device).contiguous() tex_depth = pos_camera[:, 2].reshape(1, -1, 1).contiguous() rast_out, rast_out_db = self.raster_rasterize( pos_clip, self.pos_idx, resolution=resolution) visible_mask = torch.clamp(rast_out[..., -1:], 0, 1)[0, ...] normal, _ = self.raster_interpolate( vertex_normals[None, ...], rast_out, self.pos_idx) normal = normal[0, ...] uv, _ = self.raster_interpolate(self.vtx_uv[None, ...], rast_out, self.uv_idx) depth, _ = self.raster_interpolate(tex_depth, rast_out, self.pos_idx) depth = depth[0, ...] depth_max, depth_min = depth[visible_mask > 0].max(), depth[visible_mask > 0].min() depth_normalized = (depth - depth_min) / (depth_max - depth_min) depth_image = depth_normalized * visible_mask # Mask out background. sketch_image = self.render_sketch_from_depth(depth_image) lookat = torch.tensor([[0, 0, -1]], device=self.device) cos_image = torch.nn.functional.cosine_similarity( lookat, normal.view(-1, 3)) cos_image = cos_image.view(normal.shape[0], normal.shape[1], 1) cos_thres = np.cos(self.bake_angle_thres / 180 * np.pi) cos_image[cos_image < cos_thres] = 0 # shrink kernel_size = self.bake_unreliable_kernel_size * 2 + 1 kernel = torch.ones( (1, 1, kernel_size, kernel_size), dtype=torch.float32).to( sketch_image.device) visible_mask = visible_mask.permute(2, 0, 1).unsqueeze(0).float() visible_mask = F.conv2d( 1.0 - visible_mask, kernel, padding=kernel_size // 2) visible_mask = 1.0 - (visible_mask > 0).float() # 二值化 visible_mask = visible_mask.squeeze(0).permute(1, 2, 0) sketch_image = sketch_image.permute(2, 0, 1).unsqueeze(0) sketch_image = F.conv2d(sketch_image, kernel, padding=kernel_size // 2) sketch_image = (sketch_image > 0).float() # 二值化 sketch_image = sketch_image.squeeze(0).permute(1, 2, 0) visible_mask = visible_mask * (sketch_image < 0.5) cos_image[visible_mask == 0] = 0 method = self.bake_mode if method is None else method if method == 'linear': proj_mask = (visible_mask != 0).view(-1) uv = uv.squeeze(0).contiguous().view(-1, 2)[proj_mask] image = image.squeeze(0).contiguous().view(-1, channel)[proj_mask] cos_image = cos_image.contiguous().view(-1, 1)[proj_mask] sketch_image = sketch_image.contiguous().view(-1, 1)[proj_mask] texture = linear_grid_put_2d( self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], image) cos_map = linear_grid_put_2d( self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], cos_image) boundary_map = linear_grid_put_2d( self.texture_size[1], self.texture_size[0], uv[..., [1, 0]], sketch_image) else: raise f'No bake mode {method}' return texture, cos_map, boundary_map def bake_texture(self, colors, elevs, azims, camera_distance=None, center=None, exp=6, weights=None): for i in range(len(colors)): if isinstance(colors[i], Image.Image): colors[i] = torch.tensor( np.array( colors[i]) / 255.0, device=self.device).float() if weights is None: weights = [1.0 for _ in range(colors)] textures = [] cos_maps = [] for color, elev, azim, weight in zip(colors, elevs, azims, weights): texture, cos_map, _ = self.back_project( color, elev, azim, camera_distance, center) cos_map = weight * (cos_map ** exp) textures.append(texture) cos_maps.append(cos_map) texture_merge, trust_map_merge = self.fast_bake_texture( textures, cos_maps) return texture_merge, trust_map_merge @torch.no_grad() def fast_bake_texture(self, textures, cos_maps): channel = textures[0].shape[-1] texture_merge = torch.zeros( self.texture_size + (channel,)).to(self.device) trust_map_merge = torch.zeros(self.texture_size + (1,)).to(self.device) for texture, cos_map in zip(textures, cos_maps): view_sum = (cos_map > 0).sum() painted_sum = ((cos_map > 0) * (trust_map_merge > 0)).sum() if painted_sum / view_sum > 0.99: continue texture_merge += texture * cos_map trust_map_merge += cos_map texture_merge = texture_merge / torch.clamp(trust_map_merge, min=1E-8) return texture_merge, trust_map_merge > 1E-8 def uv_inpaint(self, texture, mask): if isinstance(texture, torch.Tensor): texture_np = texture.cpu().numpy() elif isinstance(texture, np.ndarray): texture_np = texture elif isinstance(texture, Image.Image): texture_np = np.array(texture) / 255.0 vtx_pos, pos_idx, vtx_uv, uv_idx = self.get_mesh() texture_np, mask = meshVerticeInpaint( texture_np, mask, vtx_pos, vtx_uv, pos_idx, uv_idx) texture_np = cv2.inpaint( (texture_np * 255).astype( np.uint8), 255 - mask, 3, cv2.INPAINT_NS) return texture_np ================================================ FILE: hy3dgen/texgen/differentiable_renderer/mesh_utils.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import trimesh def load_mesh(mesh): vtx_pos = mesh.vertices if hasattr(mesh, 'vertices') else None pos_idx = mesh.faces if hasattr(mesh, 'faces') else None vtx_uv = mesh.visual.uv if hasattr(mesh.visual, 'uv') else None uv_idx = mesh.faces if hasattr(mesh, 'faces') else None texture_data = None return vtx_pos, pos_idx, vtx_uv, uv_idx, texture_data def save_mesh(mesh, texture_data): material = trimesh.visual.texture.SimpleMaterial(image=texture_data, diffuse=(255, 255, 255)) texture_visuals = trimesh.visual.TextureVisuals(uv=mesh.visual.uv, image=texture_data, material=material) mesh.visual = texture_visuals return mesh ================================================ FILE: hy3dgen/texgen/differentiable_renderer/setup.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from setuptools import setup, Extension import pybind11 import sys import platform def get_platform_specific_args(): system = platform.system().lower() cpp_std = 'c++14' # Make configurable if needed if sys.platform == 'win32': compile_args = ['/O2', f'/std:{cpp_std}', '/EHsc', '/MP', '/DWIN32_LEAN_AND_MEAN', '/bigobj'] link_args = [] extra_includes = [] elif system == 'linux': compile_args = ['-O3', f'-std={cpp_std}', '-fPIC', '-Wall', '-Wextra', '-pthread'] link_args = ['-fPIC', '-pthread'] extra_includes = [] elif sys.platform == 'darwin': compile_args = ['-O3', f'-std={cpp_std}', '-fPIC', '-Wall', '-Wextra', '-stdlib=libc++', '-mmacosx-version-min=10.14'] link_args = ['-fPIC', '-stdlib=libc++', '-mmacosx-version-min=10.14', '-dynamiclib'] extra_includes = [] else: raise RuntimeError(f"Unsupported platform: {system}") return compile_args, link_args, extra_includes extra_compile_args, extra_link_args, platform_includes = get_platform_specific_args() include_dirs = [pybind11.get_include(), pybind11.get_include(user=True)] include_dirs.extend(platform_includes) ext_modules = [ Extension( "mesh_processor", ["mesh_processor.cpp"], include_dirs=include_dirs, language='c++', extra_compile_args=extra_compile_args, extra_link_args=extra_link_args, ), ] setup( name="mesh_processor", ext_modules=ext_modules, install_requires=['pybind11>=2.6.0'], python_requires='>=3.6', ) ================================================ FILE: hy3dgen/texgen/hunyuanpaint/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. ================================================ FILE: hy3dgen/texgen/hunyuanpaint/pipeline.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from typing import Any, Callable, Dict, List, Optional, Union import numpy import numpy as np import torch import torch.distributed import torch.utils.checkpoint import transformers from PIL import Image import diffusers from diffusers import ( AutoencoderKL, DDPMScheduler, DiffusionPipeline, EulerAncestralDiscreteScheduler, UNet2DConditionModel, ImagePipelineOutput ) from diffusers.callbacks import MultiPipelineCallbacks, PipelineCallback from diffusers.image_processor import PipelineImageInput from diffusers.image_processor import VaeImageProcessor from diffusers.pipelines.stable_diffusion.pipeline_output import StableDiffusionPipelineOutput from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline, \ retrieve_timesteps, rescale_noise_cfg from diffusers.schedulers import KarrasDiffusionSchedulers, LCMScheduler from diffusers.utils import deprecate from einops import rearrange from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer, CLIPVisionModelWithProjection from .unet.modules import UNet2p5DConditionModel, \ compute_multi_resolution_mask, compute_multi_resolution_discrete_voxel_indice def guidance_scale_embedding(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 def append_dims(x, target_dims): """Appends dimensions to the end of a tensor until it has target_dims dimensions.""" dims_to_append = target_dims - x.ndim if dims_to_append < 0: raise ValueError(f"input has {x.ndim} dims but target_dims is {target_dims}, which is less") return x[(...,) + (None,) * dims_to_append] # From LCMScheduler.get_scalings_for_boundary_condition_discrete def scalings_for_boundary_conditions(timestep, sigma_data=0.5, timestep_scaling=10.0): scaled_timestep = timestep_scaling * timestep c_skip = sigma_data ** 2 / (scaled_timestep ** 2 + sigma_data ** 2) c_out = scaled_timestep / (scaled_timestep ** 2 + sigma_data ** 2) ** 0.5 return c_skip, c_out # Compare LCMScheduler.step, Step 4 def get_predicted_original_sample(model_output, timesteps, sample, prediction_type, alphas, sigmas, N_gen): alphas = extract_into_tensor(alphas, timesteps, sample.shape, N_gen) sigmas = extract_into_tensor(sigmas, timesteps, sample.shape, N_gen) model_output = rearrange(model_output, '(b n) c h w -> b n c h w', n=N_gen) if prediction_type == "epsilon": pred_x_0 = (sample - sigmas * model_output) / alphas elif prediction_type == "sample": pred_x_0 = model_output elif prediction_type == "v_prediction": pred_x_0 = alphas * sample - sigmas * model_output else: raise ValueError( f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`" f" are supported." ) return pred_x_0 # Based on step 4 in DDIMScheduler.step def get_predicted_noise(model_output, timesteps, sample, prediction_type, alphas, sigmas, N_gen): alphas = extract_into_tensor(alphas, timesteps, sample.shape, N_gen) sigmas = extract_into_tensor(sigmas, timesteps, sample.shape, N_gen) model_output = rearrange(model_output, '(b n) c h w -> b n c h w', n=N_gen) if prediction_type == "epsilon": pred_epsilon = model_output elif prediction_type == "sample": pred_epsilon = (sample - alphas * model_output) / sigmas elif prediction_type == "v_prediction": pred_epsilon = alphas * model_output + sigmas * sample else: raise ValueError( f"Prediction type {prediction_type} is not supported; currently, `epsilon`, `sample`, and `v_prediction`" f" are supported." ) return pred_epsilon def extract_into_tensor(a, t, x_shape, N_gen): # b, *_ = t.shape out = a.gather(-1, t) out = out.repeat(N_gen) out = rearrange(out, '(b n) -> b n', n=N_gen) b, c, *_ = out.shape return out.reshape(b, c, *((1,) * (len(x_shape) - 2))) class DDIMSolver: def __init__(self, alpha_cumprods, timesteps=1000, ddim_timesteps=50): # DDIM sampling parameters step_ratio = timesteps // ddim_timesteps self.ddim_timesteps = (np.arange(1, ddim_timesteps + 1) * step_ratio).round().astype(np.int64) - 1 self.ddim_alpha_cumprods = alpha_cumprods[self.ddim_timesteps] self.ddim_alpha_cumprods_prev = np.asarray( [alpha_cumprods[0]] + alpha_cumprods[self.ddim_timesteps[:-1]].tolist() ) # convert to torch tensors self.ddim_timesteps = torch.from_numpy(self.ddim_timesteps).long() self.ddim_alpha_cumprods = torch.from_numpy(self.ddim_alpha_cumprods) self.ddim_alpha_cumprods_prev = torch.from_numpy(self.ddim_alpha_cumprods_prev) def to(self, device): self.ddim_timesteps = self.ddim_timesteps.to(device) self.ddim_alpha_cumprods = self.ddim_alpha_cumprods.to(device) self.ddim_alpha_cumprods_prev = self.ddim_alpha_cumprods_prev.to(device) return self def ddim_step(self, pred_x0, pred_noise, timestep_index, N_gen): alpha_cumprod_prev = extract_into_tensor(self.ddim_alpha_cumprods_prev, timestep_index, pred_x0.shape, N_gen) dir_xt = (1.0 - alpha_cumprod_prev).sqrt() * pred_noise x_prev = alpha_cumprod_prev.sqrt() * pred_x0 + dir_xt return x_prev @torch.no_grad() def update_ema(target_params, source_params, rate=0.99): """ Update target parameters to be closer to those of source parameters using an exponential moving average. :param target_params: the target parameter sequence. :param source_params: the source parameter sequence. :param rate: the EMA rate (closer to 1 means slower). """ for targ, src in zip(target_params, source_params): targ.detach().mul_(rate).add_(src, alpha=1 - rate) def to_rgb_image(maybe_rgba: Image.Image): if maybe_rgba.mode == 'RGB': return maybe_rgba elif maybe_rgba.mode == 'RGBA': rgba = maybe_rgba img = numpy.random.randint(127, 128, size=[rgba.size[1], rgba.size[0], 3], dtype=numpy.uint8) img = Image.fromarray(img, 'RGB') img.paste(rgba, mask=rgba.getchannel('A')) return img else: raise ValueError("Unsupported image type.", maybe_rgba.mode) class HunyuanPaintPipeline(StableDiffusionPipeline): def __init__( self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2p5DConditionModel, scheduler: KarrasDiffusionSchedulers, feature_extractor: CLIPImageProcessor, safety_checker=None, use_torch_compile=False, ): DiffusionPipeline.__init__(self) safety_checker = None self.register_modules( vae=torch.compile(vae) if use_torch_compile else vae, text_encoder=text_encoder, tokenizer=tokenizer, unet=unet, scheduler=scheduler, safety_checker=safety_checker, feature_extractor=torch.compile(feature_extractor) if use_torch_compile else feature_extractor, ) self.solver = DDIMSolver( scheduler.alphas_cumprod.numpy(), timesteps=scheduler.config.num_train_timesteps, ddim_timesteps=30, ).to('cuda') self.vae_scale_factor = 2 ** (len(self.vae.config.block_out_channels) - 1) self.image_processor = VaeImageProcessor(vae_scale_factor=self.vae_scale_factor) self.is_turbo = False def set_turbo(self, is_turbo: bool): self.is_turbo = is_turbo @torch.no_grad() def encode_images(self, images): B = images.shape[0] images = rearrange(images, 'b n c h w -> (b n) c h w') dtype = next(self.vae.parameters()).dtype images = (images - 0.5) * 2.0 posterior = self.vae.encode(images.to(dtype)).latent_dist latents = posterior.sample() * self.vae.config.scaling_factor latents = rearrange(latents, '(b n) c h w -> b n c h w', b=B) return latents @torch.no_grad() def __call__( self, image: Image.Image = None, prompt=None, negative_prompt='watermark, ugly, deformed, noisy, blurry, low contrast', *args, num_images_per_prompt: Optional[int] = 1, guidance_scale=2.0, output_type: Optional[str] = "pil", width=512, height=512, num_inference_steps=28, return_dict=True, **cached_condition, ): device = self._execution_device if image is None: raise ValueError("Inputting embeddings not supported for this pipeline. Please pass an image.") assert not isinstance(image, torch.Tensor) if not isinstance(image, List): image = [image] image = [to_rgb_image(img) for img in image] image_vae = [torch.tensor(np.array(img) / 255.0) for img in image] image_vae = [img_vae.unsqueeze(0).permute(0, 3, 1, 2).unsqueeze(0) for img_vae in image_vae] image_vae = torch.cat(image_vae, dim=1) image_vae = image_vae.to(device=device, dtype=self.vae.dtype) batch_size, N_ref = image_vae.shape[0], image_vae.shape[1] assert batch_size == 1 assert num_images_per_prompt == 1 ref_latents = self.encode_images(image_vae) def convert_pil_list_to_tensor(images): bg_c = [1., 1., 1.] images_tensor = [] for batch_imgs in images: view_imgs = [] for pil_img in batch_imgs: img = numpy.asarray(pil_img, dtype=numpy.float32) / 255. if img.shape[2] > 3: alpha = img[:, :, 3:] img = img[:, :, :3] * alpha + bg_c * (1 - alpha) img = torch.from_numpy(img).permute(2, 0, 1).unsqueeze(0).contiguous().half().to("cuda") view_imgs.append(img) view_imgs = torch.cat(view_imgs, dim=0) images_tensor.append(view_imgs.unsqueeze(0)) images_tensor = torch.cat(images_tensor, dim=0) return images_tensor if "normal_imgs" in cached_condition: if isinstance(cached_condition["normal_imgs"], List): cached_condition["normal_imgs"] = convert_pil_list_to_tensor(cached_condition["normal_imgs"]) cached_condition['normal_imgs'] = self.encode_images(cached_condition["normal_imgs"]) if "position_imgs" in cached_condition: if isinstance(cached_condition["position_imgs"], List): cached_condition["position_imgs"] = convert_pil_list_to_tensor(cached_condition["position_imgs"]) cached_condition['position_maps'] = cached_condition['position_imgs'] cached_condition["position_imgs"] = self.encode_images(cached_condition["position_imgs"]) if 'camera_info_gen' in cached_condition: camera_info = cached_condition['camera_info_gen'] # B,N if isinstance(camera_info, List): camera_info = torch.tensor(camera_info) camera_info = camera_info.to(device).to(torch.int64) cached_condition['camera_info_gen'] = camera_info if 'camera_info_ref' in cached_condition: camera_info = cached_condition['camera_info_ref'] # B,N if isinstance(camera_info, List): camera_info = torch.tensor(camera_info) camera_info = camera_info.to(device).to(torch.int64) cached_condition['camera_info_ref'] = camera_info cached_condition['ref_latents'] = ref_latents if self.is_turbo: if 'position_maps' in cached_condition: cached_condition['position_attn_mask'] = ( compute_multi_resolution_mask(cached_condition['position_maps']) ) cached_condition['position_voxel_indices'] = ( compute_multi_resolution_discrete_voxel_indice(cached_condition['position_maps']) ) if (guidance_scale > 1) and (not self.is_turbo): negative_ref_latents = torch.zeros_like(cached_condition['ref_latents']) cached_condition['ref_latents'] = torch.cat([negative_ref_latents, cached_condition['ref_latents']]) cached_condition['ref_scale'] = torch.as_tensor([0.0, 1.0]).to(cached_condition['ref_latents']) if "normal_imgs" in cached_condition: cached_condition['normal_imgs'] = torch.cat( (cached_condition['normal_imgs'], cached_condition['normal_imgs'])) if "position_imgs" in cached_condition: cached_condition['position_imgs'] = torch.cat( (cached_condition['position_imgs'], cached_condition['position_imgs'])) if 'position_maps' in cached_condition: cached_condition['position_maps'] = torch.cat( (cached_condition['position_maps'], cached_condition['position_maps'])) if 'camera_info_gen' in cached_condition: cached_condition['camera_info_gen'] = torch.cat( (cached_condition['camera_info_gen'], cached_condition['camera_info_gen'])) if 'camera_info_ref' in cached_condition: cached_condition['camera_info_ref'] = torch.cat( (cached_condition['camera_info_ref'], cached_condition['camera_info_ref'])) prompt_embeds = self.unet.learned_text_clip_gen.repeat(num_images_per_prompt, 1, 1) negative_prompt_embeds = torch.zeros_like(prompt_embeds) latents: torch.Tensor = self.denoise( None, *args, cross_attention_kwargs=None, guidance_scale=guidance_scale, num_images_per_prompt=num_images_per_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, num_inference_steps=num_inference_steps, output_type='latent', width=width, height=height, **cached_condition ).images if not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0] else: image = latents image = self.image_processor.postprocess(image, output_type=output_type) if not return_dict: return (image,) return ImagePipelineOutput(images=image) def denoise( self, prompt: Union[str, List[str]] = None, height: Optional[int] = None, width: Optional[int] = None, num_inference_steps: int = 50, timesteps: List[int] = None, sigmas: List[float] = 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.Tensor] = None, prompt_embeds: Optional[torch.Tensor] = None, negative_prompt_embeds: Optional[torch.Tensor] = None, ip_adapter_image: Optional[PipelineImageInput] = None, ip_adapter_image_embeds: Optional[List[torch.Tensor]] = None, output_type: Optional[str] = "pil", return_dict: bool = True, cross_attention_kwargs: Optional[Dict[str, Any]] = None, guidance_rescale: float = 0.0, clip_skip: Optional[int] = None, callback_on_step_end: Optional[ Union[Callable[[int, int, Dict], None], PipelineCallback, MultiPipelineCallbacks] ] = 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`. 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. sigmas (`List[float]`, *optional*): Custom sigmas to use for the denoising process with schedulers which support a `sigmas` argument in their `set_timesteps` method. If not defined, the default behavior when `num_inference_steps` is passed will be used. 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.Tensor`, *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.Tensor`, *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.Tensor`, *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.Tensor]`, *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. 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). guidance_rescale (`float`, *optional*, defaults to 0.0): Guidance rescale factor from [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://arxiv.org/pdf/2305.08891.pdf). Guidance rescale factor should fix overexposure when using zero terminal SNR. 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`, `PipelineCallback`, `MultiPipelineCallbacks`, *optional*): A function or a subclass of `PipelineCallback` or `MultiPipelineCallbacks` that is called at the end of each denoising step during the inference. 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 pipeline 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`", ) if isinstance(callback_on_step_end, (PipelineCallback, MultiPipelineCallbacks)): callback_on_step_end_tensor_inputs = callback_on_step_end.tensor_inputs # 0. Default height and width to unet height = height or self.unet.config.sample_size * self.vae_scale_factor width = width or self.unet.config.sample_size * self.vae_scale_factor # to deal with lora scaling and other possible forward hooks # 1. Check inputs. Raise error if not correct self.check_inputs( prompt, height, width, callback_steps, negative_prompt, prompt_embeds, negative_prompt_embeds, ip_adapter_image, ip_adapter_image_embeds, callback_on_step_end_tensor_inputs, ) self._guidance_scale = guidance_scale self._guidance_rescale = guidance_rescale self._clip_skip = clip_skip self._cross_attention_kwargs = cross_attention_kwargs self._interrupt = False # 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 # 3. Encode input prompt lora_scale = ( self.cross_attention_kwargs.get("scale", None) if self.cross_attention_kwargs is not None else None ) prompt_embeds, negative_prompt_embeds = self.encode_prompt( prompt, device, num_images_per_prompt, self.do_classifier_free_guidance if self.is_turbo else False, negative_prompt, prompt_embeds=prompt_embeds, negative_prompt_embeds=negative_prompt_embeds, lora_scale=lora_scale, clip_skip=self.clip_skip, ) # 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) and (not self.is_turbo): 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 if self.is_turbo else False, ) # 4. Prepare if self.is_turbo: bsz = 3 N_gen = 15 index = torch.range(29, 0, -bsz, device='cuda').long() timesteps = self.solver.ddim_timesteps[index] self.scheduler.set_timesteps(timesteps=timesteps.cpu(), device='cuda') else: timesteps, num_inference_steps = retrieve_timesteps( self.scheduler, num_inference_steps, device, timesteps, sigmas ) assert num_images_per_prompt == 1 # 5. Prepare latent variables num_channels_latents = self.unet.config.in_channels latents = self.prepare_latents( batch_size * kwargs['num_in_batch'], # num_images_per_prompt, num_channels_latents, height, width, prompt_embeds.dtype, device, generator, latents, ) # 6. 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) # 6.1 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 ) # 6.2 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. Denoising loop num_warmup_steps = len(timesteps) - num_inference_steps * self.scheduler.order self._num_timesteps = len(timesteps) with self.progress_bar(total=num_inference_steps) as progress_bar: for i, t in enumerate(timesteps): if self.interrupt: continue # expand the latents if we are doing classifier free guidance latents = rearrange(latents, '(b n) c h w -> b n c h w', n=kwargs['num_in_batch']) latent_model_input = ( torch.cat([latents] * 2) if ((self.do_classifier_free_guidance) and (not self.is_turbo)) else latents ) latent_model_input = rearrange(latent_model_input, 'b n c h w -> (b n) c h w') latent_model_input = self.scheduler.scale_model_input(latent_model_input, t) latent_model_input = rearrange(latent_model_input, '(b n) c h w ->b n c h w', n=kwargs['num_in_batch']) # 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, added_cond_kwargs=added_cond_kwargs, return_dict=False, **kwargs )[0] latents = rearrange(latents, 'b n c h w -> (b n) c h w') # perform guidance if (self.do_classifier_free_guidance) and (not self.is_turbo): noise_pred_uncond, noise_pred_text = noise_pred.chunk(2) noise_pred = noise_pred_uncond + self.guidance_scale * (noise_pred_text - noise_pred_uncond) if (self.do_classifier_free_guidance) and (self.guidance_rescale > 0.0) and (not self.is_turbo): # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=self.guidance_rescale) # compute the previous noisy sample x_t -> x_t-1 latents = \ self.scheduler.step(noise_pred, t, latents[:, :num_channels_latents, :, :], **extra_step_kwargs, return_dict=False)[0] 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 not output_type == "latent": image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False, generator=generator)[ 0 ] image, has_nsfw_concept = self.run_safety_checker(image, device, prompt_embeds.dtype) else: image = latents has_nsfw_concept = None if has_nsfw_concept is None: do_denormalize = [True] * image.shape[0] else: do_denormalize = [not has_nsfw for has_nsfw in has_nsfw_concept] image = self.image_processor.postprocess(image, output_type=output_type, do_denormalize=do_denormalize) # Offload all models self.maybe_free_model_hooks() if not return_dict: return (image, has_nsfw_concept) return StableDiffusionPipelineOutput(images=image, nsfw_content_detected=has_nsfw_concept) ================================================ FILE: hy3dgen/texgen/hunyuanpaint/unet/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. ================================================ FILE: hy3dgen/texgen/hunyuanpaint/unet/modules.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import copy import json import os from typing import Any, Dict, List, Optional, Tuple, Union import torch import torch.nn as nn import torch.nn.functional as F from diffusers.models import UNet2DConditionModel from diffusers.models.attention_processor import Attention from diffusers.models.transformers.transformer_2d import BasicTransformerBlock from einops import rearrange def _chunked_feed_forward(ff: nn.Module, hidden_states: torch.Tensor, chunk_dim: int, chunk_size: int): # "feed_forward_chunk_size" can be used to save memory if hidden_states.shape[chunk_dim] % chunk_size != 0: raise ValueError( f"`hidden_states` dimension to be chunked: {hidden_states.shape[chunk_dim]}" f"has to be divisible by chunk size: {chunk_size}." f" Make sure to set an appropriate `chunk_size` when calling `unet.enable_forward_chunking`." ) num_chunks = hidden_states.shape[chunk_dim] // chunk_size ff_output = torch.cat( [ff(hid_slice) for hid_slice in hidden_states.chunk(num_chunks, dim=chunk_dim)], dim=chunk_dim, ) return ff_output class Basic2p5DTransformerBlock(torch.nn.Module): def __init__(self, transformer: BasicTransformerBlock,layer_name,use_ma=True,use_ra=True,is_turbo=False) -> None: super().__init__() self.transformer = transformer self.layer_name = layer_name self.use_ma = use_ma self.use_ra = use_ra self.is_turbo = is_turbo # multiview attn if self.use_ma: self.attn_multiview = Attention( query_dim=self.dim, heads=self.num_attention_heads, dim_head=self.attention_head_dim, dropout=self.dropout, bias=self.attention_bias, cross_attention_dim=None, upcast_attention=self.attn1.upcast_attention, out_bias=True, ) # ref attn if self.use_ra: self.attn_refview = Attention( query_dim=self.dim, heads=self.num_attention_heads, dim_head=self.attention_head_dim, dropout=self.dropout, bias=self.attention_bias, cross_attention_dim=None, upcast_attention=self.attn1.upcast_attention, out_bias=True, ) if self.is_turbo: self._initialize_attn_weights() def _initialize_attn_weights(self): if self.use_ma: self.attn_multiview.load_state_dict(self.attn1.state_dict()) with torch.no_grad(): for layer in self.attn_multiview.to_out: for param in layer.parameters(): param.zero_() if self.use_ra: self.attn_refview.load_state_dict(self.attn1.state_dict()) with torch.no_grad(): for layer in self.attn_refview.to_out: for param in layer.parameters(): param.zero_() def __getattr__(self, name: str): try: return super().__getattr__(name) except AttributeError: return getattr(self.transformer, name) def forward( self, hidden_states: torch.Tensor, attention_mask: Optional[torch.Tensor] = None, encoder_hidden_states: Optional[torch.Tensor] = None, encoder_attention_mask: Optional[torch.Tensor] = None, timestep: Optional[torch.LongTensor] = None, cross_attention_kwargs: Dict[str, Any] = None, class_labels: Optional[torch.LongTensor] = None, added_cond_kwargs: Optional[Dict[str, torch.Tensor]] = None, ) -> torch.Tensor: # Notice that normalization is always applied before the real computation in the following blocks. # 0. Self-Attention batch_size = hidden_states.shape[0] cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} num_in_batch = cross_attention_kwargs.pop('num_in_batch', 1) mode = cross_attention_kwargs.pop('mode', None) if not self.is_turbo: mva_scale = cross_attention_kwargs.pop('mva_scale', 1.0) ref_scale = cross_attention_kwargs.pop('ref_scale', 1.0) else: position_attn_mask = cross_attention_kwargs.pop("position_attn_mask", None) position_voxel_indices = cross_attention_kwargs.pop("position_voxel_indices", None) mva_scale = 1.0 ref_scale = 1.0 condition_embed_dict = cross_attention_kwargs.pop("condition_embed_dict", None) if self.norm_type == "ada_norm": norm_hidden_states = self.norm1(hidden_states, timestep) elif self.norm_type == "ada_norm_zero": norm_hidden_states, gate_msa, shift_mlp, scale_mlp, gate_mlp = self.norm1( hidden_states, timestep, class_labels, hidden_dtype=hidden_states.dtype ) elif self.norm_type in ["layer_norm", "layer_norm_i2vgen"]: norm_hidden_states = self.norm1(hidden_states) elif self.norm_type == "ada_norm_continuous": norm_hidden_states = self.norm1(hidden_states, added_cond_kwargs["pooled_text_emb"]) elif self.norm_type == "ada_norm_single": shift_msa, scale_msa, gate_msa, shift_mlp, scale_mlp, gate_mlp = ( self.scale_shift_table[None] + timestep.reshape(batch_size, 6, -1) ).chunk(6, dim=1) norm_hidden_states = self.norm1(hidden_states) norm_hidden_states = norm_hidden_states * (1 + scale_msa) + shift_msa else: raise ValueError("Incorrect norm used") if self.pos_embed is not None: norm_hidden_states = self.pos_embed(norm_hidden_states) # 1. Prepare GLIGEN inputs cross_attention_kwargs = cross_attention_kwargs.copy() if cross_attention_kwargs is not None else {} gligen_kwargs = cross_attention_kwargs.pop("gligen", None) attn_output = self.attn1( norm_hidden_states, encoder_hidden_states=encoder_hidden_states if self.only_cross_attention else None, attention_mask=attention_mask, **cross_attention_kwargs, ) if self.norm_type == "ada_norm_zero": attn_output = gate_msa.unsqueeze(1) * attn_output elif self.norm_type == "ada_norm_single": attn_output = gate_msa * attn_output hidden_states = attn_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) # 1.2 Reference Attention if 'w' in mode: condition_embed_dict[self.layer_name] = rearrange( norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch ) # B, (N L), C if 'r' in mode and self.use_ra: condition_embed = condition_embed_dict[self.layer_name].unsqueeze(1).repeat(1, num_in_batch, 1, 1) # B N L C condition_embed = rearrange(condition_embed, 'b n l c -> (b n) l c') attn_output = self.attn_refview( norm_hidden_states, encoder_hidden_states=condition_embed, attention_mask=None, **cross_attention_kwargs ) if not self.is_turbo: ref_scale_timing = ref_scale if isinstance(ref_scale, torch.Tensor): ref_scale_timing = ref_scale.unsqueeze(1).repeat(1, num_in_batch).view(-1) for _ in range(attn_output.ndim - 1): ref_scale_timing = ref_scale_timing.unsqueeze(-1) hidden_states = ref_scale_timing * attn_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) # 1.3 Multiview Attention if num_in_batch > 1 and self.use_ma: multivew_hidden_states = rearrange(norm_hidden_states, '(b n) l c -> b (n l) c', n=num_in_batch) if self.is_turbo: position_mask = None if position_attn_mask is not None: if multivew_hidden_states.shape[1] in position_attn_mask: position_mask = position_attn_mask[multivew_hidden_states.shape[1]] position_indices = None if position_voxel_indices is not None: if multivew_hidden_states.shape[1] in position_voxel_indices: position_indices = position_voxel_indices[multivew_hidden_states.shape[1]] attn_output = self.attn_multiview( multivew_hidden_states, encoder_hidden_states=multivew_hidden_states, attention_mask=position_mask, position_indices=position_indices, **cross_attention_kwargs ) else: attn_output = self.attn_multiview( multivew_hidden_states, encoder_hidden_states=multivew_hidden_states, **cross_attention_kwargs ) attn_output = rearrange(attn_output, 'b (n l) c -> (b n) l c', n=num_in_batch) hidden_states = mva_scale * attn_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) # 1.2 GLIGEN Control if gligen_kwargs is not None: hidden_states = self.fuser(hidden_states, gligen_kwargs["objs"]) # 3. Cross-Attention if self.attn2 is not None: if self.norm_type == "ada_norm": norm_hidden_states = self.norm2(hidden_states, timestep) elif self.norm_type in ["ada_norm_zero", "layer_norm", "layer_norm_i2vgen"]: norm_hidden_states = self.norm2(hidden_states) elif self.norm_type == "ada_norm_single": # For PixArt norm2 isn't applied here: # https://github.com/PixArt-alpha/PixArt-alpha/blob/0f55e922376d8b797edd44d25d0e7464b260dcab/diffusion/model/nets/PixArtMS.py#L70C1-L76C103 norm_hidden_states = hidden_states elif self.norm_type == "ada_norm_continuous": norm_hidden_states = self.norm2(hidden_states, added_cond_kwargs["pooled_text_emb"]) else: raise ValueError("Incorrect norm") if self.pos_embed is not None and self.norm_type != "ada_norm_single": norm_hidden_states = self.pos_embed(norm_hidden_states) attn_output = self.attn2( norm_hidden_states, encoder_hidden_states=encoder_hidden_states, attention_mask=encoder_attention_mask, **cross_attention_kwargs, ) hidden_states = attn_output + hidden_states # 4. Feed-forward # i2vgen doesn't have this norm 🤷‍♂️ if self.norm_type == "ada_norm_continuous": norm_hidden_states = self.norm3(hidden_states, added_cond_kwargs["pooled_text_emb"]) elif not self.norm_type == "ada_norm_single": norm_hidden_states = self.norm3(hidden_states) if self.norm_type == "ada_norm_zero": norm_hidden_states = norm_hidden_states * (1 + scale_mlp[:, None]) + shift_mlp[:, None] if self.norm_type == "ada_norm_single": norm_hidden_states = self.norm2(hidden_states) norm_hidden_states = norm_hidden_states * (1 + scale_mlp) + shift_mlp if self._chunk_size is not None: # "feed_forward_chunk_size" can be used to save memory ff_output = _chunked_feed_forward(self.ff, norm_hidden_states, self._chunk_dim, self._chunk_size) else: ff_output = self.ff(norm_hidden_states) if self.norm_type == "ada_norm_zero": ff_output = gate_mlp.unsqueeze(1) * ff_output elif self.norm_type == "ada_norm_single": ff_output = gate_mlp * ff_output hidden_states = ff_output + hidden_states if hidden_states.ndim == 4: hidden_states = hidden_states.squeeze(1) return hidden_states @torch.no_grad() def compute_voxel_grid_mask(position, grid_resolution=8): position = position.half() B,N,_,H,W = position.shape assert H%grid_resolution==0 and W%grid_resolution==0 valid_mask = (position != 1).all(dim=2, keepdim=True) valid_mask = valid_mask.expand_as(position) position[valid_mask==False] = 0 position = rearrange( position, 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', num_h=grid_resolution, num_w=grid_resolution ) valid_mask = rearrange( valid_mask, 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', num_h=grid_resolution, num_w=grid_resolution ) grid_position = position.sum(dim=(-2, -1)) count_masked = valid_mask.sum(dim=(-2, -1)) grid_position = grid_position / count_masked.clamp(min=1) grid_position[count_masked<5] = 0 grid_position = grid_position.permute(0,1,4,2,3) grid_position = rearrange(grid_position, 'b n c h w -> b n (h w) c') grid_position_expanded_1 = grid_position.unsqueeze(2).unsqueeze(4) # 形状变为 B, N, 1, L, 1, 3 grid_position_expanded_2 = grid_position.unsqueeze(1).unsqueeze(3) # 形状变为 B, 1, N, 1, L, 3 # 计算欧氏距离 distances = torch.norm(grid_position_expanded_1 - grid_position_expanded_2, dim=-1) # 形状为 B, N, N, L, L weights = distances grid_distance = 1.73/grid_resolution #weights = weights*-32 #weights = weights.clamp(min=-10000.0) weights = weights< grid_distance return weights def compute_multi_resolution_mask(position_maps, grid_resolutions=[32, 16, 8]): position_attn_mask = {} with torch.no_grad(): for grid_resolution in grid_resolutions: position_mask = compute_voxel_grid_mask(position_maps, grid_resolution) position_mask = rearrange(position_mask, 'b ni nj li lj -> b (ni li) (nj lj)') position_attn_mask[position_mask.shape[1]] = position_mask return position_attn_mask @torch.no_grad() def compute_discrete_voxel_indice(position, grid_resolution=8, voxel_resolution=128): position = position.half() B,N,_,H,W = position.shape assert H%grid_resolution==0 and W%grid_resolution==0 valid_mask = (position != 1).all(dim=2, keepdim=True) valid_mask = valid_mask.expand_as(position) position[valid_mask==False] = 0 position = rearrange( position, 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', num_h=grid_resolution, num_w=grid_resolution ) valid_mask = rearrange( valid_mask, 'b n c (num_h grid_h) (num_w grid_w) -> b n num_h num_w c grid_h grid_w', num_h=grid_resolution, num_w=grid_resolution ) grid_position = position.sum(dim=(-2, -1)) count_masked = valid_mask.sum(dim=(-2, -1)) grid_position = grid_position / count_masked.clamp(min=1) grid_position[count_masked<5] = 0 grid_position = grid_position.permute(0,1,4,2,3).clamp(0, 1) # B N C H W voxel_indices = grid_position * (voxel_resolution - 1) voxel_indices = torch.round(voxel_indices).long() return voxel_indices def compute_multi_resolution_discrete_voxel_indice( position_maps, grid_resolutions=[64, 32, 16, 8], voxel_resolutions=[512, 256, 128, 64] ): voxel_indices = {} with torch.no_grad(): for grid_resolution, voxel_resolution in zip(grid_resolutions, voxel_resolutions): voxel_indice = compute_discrete_voxel_indice(position_maps, grid_resolution, voxel_resolution) voxel_indice = rearrange(voxel_indice, 'b n c h w -> b (n h w) c') voxel_indices[voxel_indice.shape[1]] = {'voxel_indices':voxel_indice, 'voxel_resolution':voxel_resolution} return voxel_indices class UNet2p5DConditionModel(torch.nn.Module): def __init__(self, unet: UNet2DConditionModel) -> None: super().__init__() self.unet = unet self.use_ma = True self.use_ra = True self.use_camera_embedding = True self.use_dual_stream = True self.is_turbo = False if self.use_dual_stream: self.unet_dual = copy.deepcopy(unet) self.init_attention(self.unet_dual) self.init_attention(self.unet, use_ma=self.use_ma, use_ra=self.use_ra, is_turbo=self.is_turbo) self.init_condition() self.init_camera_embedding() @staticmethod def from_pretrained(pretrained_model_name_or_path, **kwargs): torch_dtype = kwargs.pop('torch_dtype', torch.float32) config_path = os.path.join(pretrained_model_name_or_path, 'config.json') unet_ckpt_path = os.path.join(pretrained_model_name_or_path, 'diffusion_pytorch_model.bin') with open(config_path, 'r', encoding='utf-8') as file: config = json.load(file) unet = UNet2DConditionModel(**config) unet = UNet2p5DConditionModel(unet) unet_ckpt = torch.load(unet_ckpt_path, map_location='cpu', weights_only=True) unet.load_state_dict(unet_ckpt, strict=True) unet = unet.to(torch_dtype) return unet def init_condition(self): self.unet.conv_in = torch.nn.Conv2d( 12, self.unet.conv_in.out_channels, kernel_size=self.unet.conv_in.kernel_size, stride=self.unet.conv_in.stride, padding=self.unet.conv_in.padding, dilation=self.unet.conv_in.dilation, groups=self.unet.conv_in.groups, bias=self.unet.conv_in.bias is not None) self.unet.learned_text_clip_gen = nn.Parameter(torch.randn(1, 77, 1024)) self.unet.learned_text_clip_ref = nn.Parameter(torch.randn(1, 77, 1024)) def init_camera_embedding(self): if self.use_camera_embedding: time_embed_dim = 1280 self.max_num_ref_image = 5 self.max_num_gen_image = 12 * 3 + 4 * 2 self.unet.class_embedding = nn.Embedding(self.max_num_ref_image + self.max_num_gen_image, time_embed_dim) def init_attention(self, unet, use_ma=False, use_ra=False, is_turbo=False): for down_block_i, down_block in enumerate(unet.down_blocks): if hasattr(down_block, "has_cross_attention") and down_block.has_cross_attention: for attn_i, attn in enumerate(down_block.attentions): for transformer_i, transformer in enumerate(attn.transformer_blocks): if isinstance(transformer, BasicTransformerBlock): attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( transformer, f'down_{down_block_i}_{attn_i}_{transformer_i}', use_ma, use_ra, is_turbo ) if hasattr(unet.mid_block, "has_cross_attention") and unet.mid_block.has_cross_attention: for attn_i, attn in enumerate(unet.mid_block.attentions): for transformer_i, transformer in enumerate(attn.transformer_blocks): if isinstance(transformer, BasicTransformerBlock): attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( transformer, f'mid_{attn_i}_{transformer_i}', use_ma, use_ra, is_turbo ) for up_block_i, up_block in enumerate(unet.up_blocks): if hasattr(up_block, "has_cross_attention") and up_block.has_cross_attention: for attn_i, attn in enumerate(up_block.attentions): for transformer_i, transformer in enumerate(attn.transformer_blocks): if isinstance(transformer, BasicTransformerBlock): attn.transformer_blocks[transformer_i] = Basic2p5DTransformerBlock( transformer, f'up_{up_block_i}_{attn_i}_{transformer_i}', use_ma, use_ra, is_turbo ) def __getattr__(self, name: str): try: return super().__getattr__(name) except AttributeError: return getattr(self.unet, name) def forward( self, sample, timestep, encoder_hidden_states, *args, down_intrablock_additional_residuals=None, down_block_res_samples=None, mid_block_res_sample=None, **cached_condition, ): B, N_gen, _, H, W = sample.shape assert H == W if self.use_camera_embedding: camera_info_gen = cached_condition['camera_info_gen'] + self.max_num_ref_image camera_info_gen = rearrange(camera_info_gen, 'b n -> (b n)') else: camera_info_gen = None sample = [sample] if 'normal_imgs' in cached_condition: sample.append(cached_condition["normal_imgs"]) if 'position_imgs' in cached_condition: sample.append(cached_condition["position_imgs"]) sample = torch.cat(sample, dim=2) sample = rearrange(sample, 'b n c h w -> (b n) c h w') encoder_hidden_states_gen = encoder_hidden_states.unsqueeze(1).repeat(1, N_gen, 1, 1) encoder_hidden_states_gen = rearrange(encoder_hidden_states_gen, 'b n l c -> (b n) l c') if self.use_ra: if 'condition_embed_dict' in cached_condition: condition_embed_dict = cached_condition['condition_embed_dict'] else: condition_embed_dict = {} ref_latents = cached_condition['ref_latents'] N_ref = ref_latents.shape[1] if self.use_camera_embedding: camera_info_ref = cached_condition['camera_info_ref'] camera_info_ref = rearrange(camera_info_ref, 'b n -> (b n)') else: camera_info_ref = None ref_latents = rearrange(ref_latents, 'b n c h w -> (b n) c h w') encoder_hidden_states_ref = self.unet.learned_text_clip_ref.unsqueeze(1).repeat(B, N_ref, 1, 1) encoder_hidden_states_ref = rearrange(encoder_hidden_states_ref, 'b n l c -> (b n) l c') noisy_ref_latents = ref_latents timestep_ref = 0 if self.use_dual_stream: unet_ref = self.unet_dual else: unet_ref = self.unet unet_ref( noisy_ref_latents, timestep_ref, encoder_hidden_states=encoder_hidden_states_ref, class_labels=camera_info_ref, # **kwargs return_dict=False, cross_attention_kwargs={ 'mode': 'w', 'num_in_batch': N_ref, 'condition_embed_dict': condition_embed_dict}, ) cached_condition['condition_embed_dict'] = condition_embed_dict else: condition_embed_dict = None mva_scale = cached_condition.get('mva_scale', 1.0) ref_scale = cached_condition.get('ref_scale', 1.0) if self.is_turbo: cross_attention_kwargs_ = { 'mode': 'r', 'num_in_batch': N_gen, 'condition_embed_dict': condition_embed_dict, 'position_attn_mask':position_attn_mask, 'position_voxel_indices':position_voxel_indices, 'mva_scale': mva_scale, 'ref_scale': ref_scale, } else: cross_attention_kwargs_ = { 'mode': 'r', 'num_in_batch': N_gen, 'condition_embed_dict': condition_embed_dict, 'mva_scale': mva_scale, 'ref_scale': ref_scale, } return self.unet( sample, timestep, encoder_hidden_states_gen, *args, class_labels=camera_info_gen, down_intrablock_additional_residuals=[ sample.to(dtype=self.unet.dtype) for sample in down_intrablock_additional_residuals ] if down_intrablock_additional_residuals is not None else None, down_block_additional_residuals=[ sample.to(dtype=self.unet.dtype) for sample in down_block_res_samples ] if down_block_res_samples is not None else None, mid_block_additional_residual=( mid_block_res_sample.to(dtype=self.unet.dtype) if mid_block_res_sample is not None else None ), return_dict=False, cross_attention_kwargs=cross_attention_kwargs_, ) ================================================ FILE: hy3dgen/texgen/pipelines.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import logging import numpy as np import os import torch from PIL import Image from typing import List, Union, Optional from .differentiable_renderer.mesh_render import MeshRender from .utils.dehighlight_utils import Light_Shadow_Remover from .utils.multiview_utils import Multiview_Diffusion_Net from .utils.imagesuper_utils import Image_Super_Net from .utils.uv_warp_utils import mesh_uv_wrap logger = logging.getLogger(__name__) class Hunyuan3DTexGenConfig: def __init__(self, light_remover_ckpt_path, multiview_ckpt_path, subfolder_name): self.device = 'cuda' self.light_remover_ckpt_path = light_remover_ckpt_path self.multiview_ckpt_path = multiview_ckpt_path self.candidate_camera_azims = [0, 90, 180, 270, 0, 180] self.candidate_camera_elevs = [0, 0, 0, 0, 90, -90] self.candidate_view_weights = [1, 0.1, 0.5, 0.1, 0.05, 0.05] self.render_size = 2048 self.texture_size = 2048 self.bake_exp = 4 self.merge_method = 'fast' self.pipe_dict = {'hunyuan3d-paint-v2-0': 'hunyuanpaint', 'hunyuan3d-paint-v2-0-turbo': 'hunyuanpaint-turbo'} self.pipe_name = self.pipe_dict[subfolder_name] class Hunyuan3DPaintPipeline: @classmethod def from_pretrained(cls, model_path, subfolder='hunyuan3d-paint-v2-0-turbo'): original_model_path = model_path if not os.path.exists(model_path): # try local path base_dir = os.environ.get('HY3DGEN_MODELS', '~/.cache/hy3dgen') model_path = os.path.expanduser(os.path.join(base_dir, model_path)) delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0') multiview_model_path = os.path.join(model_path, subfolder) if not os.path.exists(delight_model_path) or not os.path.exists(multiview_model_path): try: import huggingface_hub # download from huggingface model_path = huggingface_hub.snapshot_download( repo_id=original_model_path, allow_patterns=["hunyuan3d-delight-v2-0/*"] ) model_path = huggingface_hub.snapshot_download( repo_id=original_model_path, allow_patterns=[f'{subfolder}/*'] ) delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0') multiview_model_path = os.path.join(model_path, subfolder) return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path, subfolder)) except Exception: import traceback traceback.print_exc() raise RuntimeError(f"Something wrong while loading {model_path}") else: return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path, subfolder)) else: delight_model_path = os.path.join(model_path, 'hunyuan3d-delight-v2-0') multiview_model_path = os.path.join(model_path, subfolder) return cls(Hunyuan3DTexGenConfig(delight_model_path, multiview_model_path, subfolder)) def __init__(self, config): self.config = config self.models = {} self.render = MeshRender( default_resolution=self.config.render_size, texture_size=self.config.texture_size) self.load_models() def load_models(self): # empty cude cache torch.cuda.empty_cache() # Load model self.models['delight_model'] = Light_Shadow_Remover(self.config) self.models['multiview_model'] = Multiview_Diffusion_Net(self.config) # self.models['super_model'] = Image_Super_Net(self.config) def enable_model_cpu_offload(self, gpu_id: Optional[int] = None, device: Union[torch.device, str] = "cuda"): self.models['delight_model'].pipeline.enable_model_cpu_offload(gpu_id=gpu_id, device=device) self.models['multiview_model'].pipeline.enable_model_cpu_offload(gpu_id=gpu_id, device=device) def render_normal_multiview(self, camera_elevs, camera_azims, use_abs_coor=True): normal_maps = [] for elev, azim in zip(camera_elevs, camera_azims): normal_map = self.render.render_normal( elev, azim, use_abs_coor=use_abs_coor, return_type='pl') normal_maps.append(normal_map) return normal_maps def render_position_multiview(self, camera_elevs, camera_azims): position_maps = [] for elev, azim in zip(camera_elevs, camera_azims): position_map = self.render.render_position( elev, azim, return_type='pl') position_maps.append(position_map) return position_maps def bake_from_multiview(self, views, camera_elevs, camera_azims, view_weights, method='graphcut'): project_textures, project_weighted_cos_maps = [], [] project_boundary_maps = [] for view, camera_elev, camera_azim, weight in zip( views, camera_elevs, camera_azims, view_weights): project_texture, project_cos_map, project_boundary_map = self.render.back_project( view, camera_elev, camera_azim) project_cos_map = weight * (project_cos_map ** self.config.bake_exp) project_textures.append(project_texture) project_weighted_cos_maps.append(project_cos_map) project_boundary_maps.append(project_boundary_map) if method == 'fast': texture, ori_trust_map = self.render.fast_bake_texture( project_textures, project_weighted_cos_maps) else: raise f'no method {method}' return texture, ori_trust_map > 1E-8 def texture_inpaint(self, texture, mask): texture_np = self.render.uv_inpaint(texture, mask) texture = torch.tensor(texture_np / 255).float().to(texture.device) return texture def recenter_image(self, image, border_ratio=0.2): if image.mode == 'RGB': return image elif image.mode == 'L': image = image.convert('RGB') return image alpha_channel = np.array(image)[:, :, 3] non_zero_indices = np.argwhere(alpha_channel > 0) if non_zero_indices.size == 0: raise ValueError("Image is fully transparent") min_row, min_col = non_zero_indices.min(axis=0) max_row, max_col = non_zero_indices.max(axis=0) cropped_image = image.crop((min_col, min_row, max_col + 1, max_row + 1)) width, height = cropped_image.size border_width = int(width * border_ratio) border_height = int(height * border_ratio) new_width = width + 2 * border_width new_height = height + 2 * border_height square_size = max(new_width, new_height) new_image = Image.new('RGBA', (square_size, square_size), (255, 255, 255, 0)) paste_x = (square_size - new_width) // 2 + border_width paste_y = (square_size - new_height) // 2 + border_height new_image.paste(cropped_image, (paste_x, paste_y)) return new_image @torch.no_grad() def __call__(self, mesh, image): if not isinstance(image, List): image = [image] images_prompt = [] for i in range(len(image)): if isinstance(image[i], str): image_prompt = Image.open(image[i]) else: image_prompt = image[i] images_prompt.append(image_prompt) images_prompt = [self.recenter_image(image_prompt) for image_prompt in images_prompt] images_prompt = [self.models['delight_model'](image_prompt) for image_prompt in images_prompt] mesh = mesh_uv_wrap(mesh) self.render.load_mesh(mesh) selected_camera_elevs, selected_camera_azims, selected_view_weights = \ self.config.candidate_camera_elevs, self.config.candidate_camera_azims, self.config.candidate_view_weights normal_maps = self.render_normal_multiview( selected_camera_elevs, selected_camera_azims, use_abs_coor=True) position_maps = self.render_position_multiview( selected_camera_elevs, selected_camera_azims) camera_info = [(((azim // 30) + 9) % 12) // {-20: 1, 0: 1, 20: 1, -90: 3, 90: 3}[ elev] + {-20: 0, 0: 12, 20: 24, -90: 36, 90: 40}[elev] for azim, elev in zip(selected_camera_azims, selected_camera_elevs)] multiviews = self.models['multiview_model'](images_prompt, normal_maps + position_maps, camera_info) for i in range(len(multiviews)): # multiviews[i] = self.models['super_model'](multiviews[i]) multiviews[i] = multiviews[i].resize( (self.config.render_size, self.config.render_size)) texture, mask = self.bake_from_multiview(multiviews, selected_camera_elevs, selected_camera_azims, selected_view_weights, method=self.config.merge_method) mask_np = (mask.squeeze(-1).cpu().numpy() * 255).astype(np.uint8) texture = self.texture_inpaint(texture, mask_np) self.render.set_texture(texture) textured_mesh = self.render.save_mesh() return textured_mesh ================================================ FILE: hy3dgen/texgen/utils/__init__.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. ================================================ FILE: hy3dgen/texgen/utils/alignImg4Tex_utils.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import torch from diffusers import EulerAncestralDiscreteScheduler from diffusers import StableDiffusionControlNetPipeline, StableDiffusionXLControlNetImg2ImgPipeline, ControlNetModel, \ AutoencoderKL class Img2img_Control_Ip_adapter: def __init__(self, device): controlnet = ControlNetModel.from_pretrained('lllyasviel/control_v11f1p_sd15_depth', torch_dtype=torch.float16, variant="fp16", use_safetensors=True) pipe = StableDiffusionControlNetPipeline.from_pretrained( 'runwayml/stable-diffusion-v1-5', controlnet=controlnet, torch_dtype=torch.float16, use_safetensors=True ) pipe.load_ip_adapter('h94/IP-Adapter', subfolder="models", weight_name="ip-adapter-plus_sd15.safetensors") pipe.set_ip_adapter_scale(0.7) pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config) # pipe.enable_model_cpu_offload() self.pipe = pipe.to(device) def __call__( self, prompt, control_image, ip_adapter_image, negative_prompt, height=512, width=512, num_inference_steps=20, guidance_scale=8.0, controlnet_conditioning_scale=1.0, output_type="pil", **kwargs, ): results = self.pipe( prompt=prompt, negative_prompt=negative_prompt, image=control_image, ip_adapter_image=ip_adapter_image, generator=torch.manual_seed(42), seed=42, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, controlnet_conditioning_scale=controlnet_conditioning_scale, strength=1, # clip_skip=2, height=height, width=width, output_type=output_type, **kwargs, ).images[0] return results ################################################################ class HesModel: def __init__(self, ): controlnet_depth = ControlNetModel.from_pretrained( 'diffusers/controlnet-depth-sdxl-1.0', torch_dtype=torch.float16, variant="fp16", use_safetensors=True ) self.pipe = StableDiffusionXLControlNetImg2ImgPipeline.from_pretrained( 'stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16, variant="fp16", controlnet=controlnet_depth, use_safetensors=True, ) self.pipe.vae = AutoencoderKL.from_pretrained( 'madebyollin/sdxl-vae-fp16-fix', torch_dtype=torch.float16 ) self.pipe.load_ip_adapter('h94/IP-Adapter', subfolder="sdxl_models", weight_name="ip-adapter_sdxl.safetensors") self.pipe.set_ip_adapter_scale(0.7) self.pipe.to("cuda") def __call__(self, init_image, control_image, ip_adapter_image=None, prompt='3D image', negative_prompt='2D image', seed=42, strength=0.8, num_inference_steps=40, guidance_scale=7.5, controlnet_conditioning_scale=0.5, **kwargs ): image = self.pipe( prompt=prompt, image=init_image, control_image=control_image, ip_adapter_image=ip_adapter_image, negative_prompt=negative_prompt, num_inference_steps=num_inference_steps, guidance_scale=guidance_scale, strength=strength, controlnet_conditioning_scale=controlnet_conditioning_scale, seed=seed, **kwargs ).images[0] return image ================================================ FILE: hy3dgen/texgen/utils/counter_utils.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. class RunningStats(): def __init__(self) -> None: self.count = 0 self.sum = 0 self.mean = 0 self.min = None self.max = None def add_value(self, value): self.count += 1 self.sum += value self.mean = self.sum / self.count if self.min is None or value < self.min: self.min = value if self.max is None or value > self.max: self.max = value def get_count(self): return self.count def get_sum(self): return self.sum def get_mean(self): return self.mean def get_min(self): return self.min def get_max(self): return self.max ================================================ FILE: hy3dgen/texgen/utils/dehighlight_utils.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import cv2 import numpy as np import torch from PIL import Image from diffusers import StableDiffusionInstructPix2PixPipeline, EulerAncestralDiscreteScheduler class Light_Shadow_Remover(): def __init__(self, config): self.device = config.device self.cfg_image = 1.5 self.cfg_text = 1.0 pipeline = StableDiffusionInstructPix2PixPipeline.from_pretrained( config.light_remover_ckpt_path, torch_dtype=torch.float16, safety_checker=None, ) pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config) pipeline.set_progress_bar_config(disable=True) self.pipeline = pipeline.to(self.device, torch.float16) def recorrect_rgb(self, src_image, target_image, alpha_channel, scale=0.95): def flat_and_mask(bgr, a): mask = torch.where(a > 0.5, True, False) bgr_flat = bgr.reshape(-1, bgr.shape[-1]) mask_flat = mask.reshape(-1) bgr_flat_masked = bgr_flat[mask_flat, :] return bgr_flat_masked src_flat = flat_and_mask(src_image, alpha_channel) target_flat = flat_and_mask(target_image, alpha_channel) corrected_bgr = torch.zeros_like(src_image) for i in range(3): src_mean, src_stddev = torch.mean(src_flat[:, i]), torch.std(src_flat[:, i]) target_mean, target_stddev = torch.mean(target_flat[:, i]), torch.std(target_flat[:, i]) corrected_bgr[:, :, i] = torch.clamp( (src_image[:, :, i] - scale * src_mean) * (target_stddev / src_stddev) + scale * target_mean, 0, 1) src_mse = torch.mean((src_image - target_image) ** 2) modify_mse = torch.mean((corrected_bgr - target_image) ** 2) if src_mse < modify_mse: corrected_bgr = torch.cat([src_image, alpha_channel], dim=-1) else: corrected_bgr = torch.cat([corrected_bgr, alpha_channel], dim=-1) return corrected_bgr @torch.no_grad() def __call__(self, image): image = image.resize((512, 512)) if image.mode == 'RGBA': image_array = np.array(image) alpha_channel = image_array[:, :, 3] erosion_size = 3 kernel = np.ones((erosion_size, erosion_size), np.uint8) alpha_channel = cv2.erode(alpha_channel, kernel, iterations=1) image_array[alpha_channel == 0, :3] = 255 image_array[:, :, 3] = alpha_channel image = Image.fromarray(image_array) image_tensor = torch.tensor(np.array(image) / 255.0).to(self.device) alpha = image_tensor[:, :, 3:] rgb_target = image_tensor[:, :, :3] else: image_tensor = torch.tensor(np.array(image) / 255.0).to(self.device) alpha = torch.ones_like(image_tensor)[:, :, :1] rgb_target = image_tensor[:, :, :3] image = image.convert('RGB') image = self.pipeline( prompt="", image=image, generator=torch.manual_seed(42), height=512, width=512, num_inference_steps=50, image_guidance_scale=self.cfg_image, guidance_scale=self.cfg_text, ).images[0] image_tensor = torch.tensor(np.array(image)/255.0).to(self.device) rgb_src = image_tensor[:,:,:3] image = self.recorrect_rgb(rgb_src, rgb_target, alpha) image = image[:,:,:3]*image[:,:,3:] + torch.ones_like(image[:,:,:3])*(1.0-image[:,:,3:]) image = Image.fromarray((image.cpu().numpy()*255).astype(np.uint8)) return image ================================================ FILE: hy3dgen/texgen/utils/imagesuper_utils.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import torch from diffusers import StableDiffusionUpscalePipeline class Image_Super_Net(): def __init__(self, config): self.up_pipeline_x4 = StableDiffusionUpscalePipeline.from_pretrained( 'stabilityai/stable-diffusion-x4-upscaler', torch_dtype=torch.float16, ).to(config.device) self.up_pipeline_x4.set_progress_bar_config(disable=True) def __call__(self, image, prompt=''): with torch.no_grad(): upscaled_image = self.up_pipeline_x4( prompt=[prompt], image=image, num_inference_steps=5, ).images[0] return upscaled_image ================================================ FILE: hy3dgen/texgen/utils/multiview_utils.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import os import random import numpy as np import torch from typing import List from diffusers import DiffusionPipeline from diffusers import EulerAncestralDiscreteScheduler, LCMScheduler class Multiview_Diffusion_Net(): def __init__(self, config) -> None: self.device = config.device self.view_size = 512 multiview_ckpt_path = config.multiview_ckpt_path current_file_path = os.path.abspath(__file__) custom_pipeline_path = os.path.join(os.path.dirname(current_file_path), '..', 'hunyuanpaint') pipeline = DiffusionPipeline.from_pretrained( multiview_ckpt_path, custom_pipeline=custom_pipeline_path, torch_dtype=torch.float16) if config.pipe_name in ['hunyuanpaint']: pipeline.scheduler = EulerAncestralDiscreteScheduler.from_config(pipeline.scheduler.config, timestep_spacing='trailing') elif config.pipe_name in ['hunyuanpaint-turbo']: pipeline.scheduler = LCMScheduler.from_config(pipeline.scheduler.config, timestep_spacing='trailing') pipeline.set_turbo(True) # pipeline.prepare() pipeline.set_progress_bar_config(disable=True) self.pipeline = pipeline.to(self.device) def seed_everything(self, seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) os.environ["PL_GLOBAL_SEED"] = str(seed) def __call__(self, input_images, control_images, camera_info): self.seed_everything(0) if not isinstance(input_images, List): input_images = [input_images] input_images = [input_image.resize((self.view_size, self.view_size)) for input_image in input_images] for i in range(len(control_images)): control_images[i] = control_images[i].resize((self.view_size, self.view_size)) if control_images[i].mode == 'L': control_images[i] = control_images[i].point(lambda x: 255 if x > 1 else 0, mode='1') kwargs = dict(generator=torch.Generator(device=self.pipeline.device).manual_seed(0)) num_view = len(control_images) // 2 normal_image = [[control_images[i] for i in range(num_view)]] position_image = [[control_images[i + num_view] for i in range(num_view)]] camera_info_gen = [camera_info] camera_info_ref = [[0]] kwargs['width'] = self.view_size kwargs['height'] = self.view_size kwargs['num_in_batch'] = num_view kwargs['camera_info_gen'] = camera_info_gen kwargs['camera_info_ref'] = camera_info_ref kwargs["normal_imgs"] = normal_image kwargs["position_imgs"] = position_image mvd_image = self.pipeline(input_images, num_inference_steps=30, **kwargs).images return mvd_image ================================================ FILE: hy3dgen/texgen/utils/simplify_mesh_utils.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import trimesh def remesh_mesh(mesh_path, remesh_path, method='trimesh'): if method == 'trimesh': mesh_simplify_trimesh(mesh_path, remesh_path) else: raise f'Method {method} has not been implemented.' def mesh_simplify_trimesh(inputpath, outputpath): import pymeshlab ms = pymeshlab.MeshSet() ms.load_new_mesh(inputpath, load_in_a_single_layer=True) ms.save_current_mesh(outputpath.replace('.glb', '.obj'), save_textures=False) courent = trimesh.load(outputpath.replace('.glb', '.obj'), force='mesh') face_num = courent.faces.shape[0] if face_num > 100000: courent = courent.simplify_quadric_decimation(40000) courent.export(outputpath) ================================================ FILE: hy3dgen/texgen/utils/uv_warp_utils.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import trimesh import xatlas def mesh_uv_wrap(mesh): if isinstance(mesh, trimesh.Scene): mesh = mesh.dump(concatenate=True) if len(mesh.faces) > 500000000: raise ValueError("The mesh has more than 500,000,000 faces, which is not supported.") vmapping, indices, uvs = xatlas.parametrize(mesh.vertices, mesh.faces) mesh.vertices = mesh.vertices[vmapping] mesh.faces = indices mesh.visual.uv = uvs return mesh ================================================ FILE: hy3dgen/text2image.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. import os import random import numpy as np import torch from diffusers import AutoPipelineForText2Image def seed_everything(seed): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) os.environ["PL_GLOBAL_SEED"] = str(seed) class HunyuanDiTPipeline: def __init__( self, model_path="Tencent-Hunyuan/HunyuanDiT-v1.1-Diffusers-Distilled", device='cuda' ): self.device = device self.pipe = AutoPipelineForText2Image.from_pretrained( model_path, torch_dtype=torch.float16, enable_pag=True, pag_applied_layers=["blocks.(16|17|18|19)"] ).to(device) self.pos_txt = ",白色背景,3D风格,最佳质量" self.neg_txt = "文本,特写,裁剪,出框,最差质量,低质量,JPEG伪影,PGLY,重复,病态," \ "残缺,多余的手指,变异的手,画得不好的手,画得不好的脸,变异,畸形,模糊,脱水,糟糕的解剖学," \ "糟糕的比例,多余的肢体,克隆的脸,毁容,恶心的比例,畸形的肢体,缺失的手臂,缺失的腿," \ "额外的手臂,额外的腿,融合的手指,手指太多,长脖子" def compile(self): # accelarate hunyuan-dit transformer,first inference will cost long time torch.set_float32_matmul_precision('high') self.pipe.transformer = torch.compile(self.pipe.transformer, fullgraph=True) # self.pipe.vae.decode = torch.compile(self.pipe.vae.decode, fullgraph=True) generator = torch.Generator(device=self.pipe.device) # infer once for hot-start out_img = self.pipe( prompt='美少女战士', negative_prompt='模糊', num_inference_steps=25, pag_scale=1.3, width=1024, height=1024, generator=generator, return_dict=False )[0][0] @torch.no_grad() def __call__(self, prompt, seed=0): seed_everything(seed) generator = torch.Generator(device=self.pipe.device) generator = generator.manual_seed(int(seed)) out_img = self.pipe( prompt=prompt[:60] + self.pos_txt, negative_prompt=self.neg_txt, num_inference_steps=25, pag_scale=1.3, width=1024, height=1024, generator=generator, return_dict=False )[0][0] return out_img ================================================ FILE: minimal_demo.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from PIL import Image from hy3dgen.rembg import BackgroundRemover from hy3dgen.shapegen import Hunyuan3DDiTFlowMatchingPipeline from hy3dgen.texgen import Hunyuan3DPaintPipeline model_path = 'tencent/Hunyuan3D-2' pipeline_shapegen = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained(model_path) pipeline_texgen = Hunyuan3DPaintPipeline.from_pretrained(model_path) image_path = 'assets/demo.png' image = Image.open(image_path).convert("RGBA") if image.mode == 'RGB': rembg = BackgroundRemover() image = rembg(image) mesh = pipeline_shapegen(image=image)[0] mesh = pipeline_texgen(mesh, image=image) mesh.export('demo.glb') ================================================ FILE: minimal_vae_demo.py ================================================ import torch from hy3dgen.shapegen.models.autoencoders import ShapeVAE from hy3dgen.shapegen.surface_loaders import SharpEdgeSurfaceLoader # vae = ShapeVAE.from_pretrained( # 'tencent/Hunyuan3D-2', # subfolder='hunyuan3d-vae-v2-0-withencoder', # use_safetensors=False, # pc_size = 30720, # pc_sharpedge_size= 30720, # ) # loader = SharpEdgeSurfaceLoader( # num_sharp_points=30720, # num_uniform_points=30720, # ) vae = ShapeVAE.from_pretrained( 'tencent/Hunyuan3D-2mini', subfolder='hunyuan3d-vae-v2-mini-withencoder', use_safetensors=False, ) loader = SharpEdgeSurfaceLoader( num_sharp_points=5120, num_uniform_points=5120, ) surface = loader('demo.glb').to('cuda', dtype=torch.float16) latents = vae.encode(surface) latents = vae.decode(latents) mesh = vae.latents2mesh( latents, output_type='trimesh', bounds=1.01, mc_level=0.0, num_chunks=20000, octree_resolution=256, mc_algo='mc', enable_pbar=True ) from hy3dgen.shapegen.pipelines import export_to_trimesh mesh = export_to_trimesh(mesh)[0] mesh.export('output.glb') ================================================ FILE: requirements.txt ================================================ ninja pybind11 diffusers einops opencv-python numpy torch transformers torchvision #taming-transformers-rom1504 #ConfigArgParse #ipdb omegaconf #sentencepiece tqdm # Mesh Processing trimesh pymeshlab pygltflib xatlas #kornia #facexlib # Training accelerate #pytorch_lightning #scikit-learn #scikit-image # Demo only gradio fastapi uvicorn rembg onnxruntime #gevent #geventhttpclient ================================================ FILE: setup.py ================================================ # Hunyuan 3D is licensed under the TENCENT HUNYUAN NON-COMMERCIAL LICENSE AGREEMENT # except for the third-party components listed below. # Hunyuan 3D does not impose any additional limitations beyond what is outlined # in the repsective licenses of these third-party components. # Users must comply with all terms and conditions of original licenses of these third-party # components and must ensure that the usage of the third party components adheres to # all relevant laws and regulations. # For avoidance of doubts, Hunyuan 3D means the large language models and # their software and algorithms, including trained model weights, parameters (including # optimizer states), machine-learning model code, inference-enabling code, training-enabling code, # fine-tuning enabling code and other elements of the foregoing made publicly available # by Tencent in accordance with TENCENT HUNYUAN COMMUNITY LICENSE AGREEMENT. from setuptools import setup, find_packages setup( name="hy3dgen", version="2.0.2", url="https://github.com/Tencent/Hunyuan3D-2", packages=find_packages(), include_package_data=True, package_data={"hy3dgen": ["assets/*", "assets/**/*"]}, install_requires=[ 'gradio', "tqdm>=4.66.3", 'numpy', 'ninja', 'diffusers', 'pybind11', 'opencv-python', 'einops', "transformers>=4.48.0", 'omegaconf', 'trimesh', 'pymeshlab', 'pygltflib', 'xatlas', 'accelerate', 'gradio', 'fastapi', 'uvicorn', 'rembg', 'onnxruntime' ] )