Hunyuan3D-2.1

Tencent-Hunyuan长期未更新

腾讯混元开源的图生 3D 模型,能从单张图片生成带生产级 PBR 材质的高精度 3D 资产,适合直接用于游戏和影视管线。

From Images to High-Fidelity 3D Assets with Production-Ready PBR Material

暂无版本4,410star +24 / 7 天651fork同步于 2026-11-10 23:39 UTC

怎么上手

由 README 提炼,执行前请确认
pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
pip install -r requirements.txt
硬件要求按 README 说明

形状生成需10GB显存,纹理生成需21GB,两者合计29GB;支持macOS、Windows、Linux,未说明具体GPU厂商与型号。

下载:源码 zip·GitHub Releases(安装包)本站不镜像安装包

项目说明

由 README 自动翻译提炼

腾讯开源图生3D资产模型,输出带PBR材质的可用三维模型

腾讯混元开源的图生 3D 模型,能从单张图片生成带生产级 PBR 材质的高精度 3D 资产,适合直接用于游戏和影视管线。

核心能力

  • 用扩散模型从单张图片生成3D网格
  • PBR材质合成模拟金属反射等真实光照
  • 开源全部模型权重与训练代码可微调
  • 提供类diffusers风格的两段式推理API
  • 附带Gradio应用可本地起Web界面

容易踩的坑

纹理生成需先编译custom_rasterizer和DifferentiableRenderer两个子模块;需额外下载RealESRGAN_x4plus.pth到hy3dpaint/ckpt;模型权重从HuggingFace拉取,显存不足时需加--low_vram_mode。

内容由模型根据 README 生成,可能不完整或不准确,以官方文档为准。看原文 README

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    README 原文

    未翻译

    🔥 News

    • Jul 26, 2025: 🤗 We release the first open-source, simulation-capable, immersive 3D world generation model, HunyuanWorld-1.0!
    • Jun 19, 2025: 👋 We present the technical report of Hunyuan3D-2.1, please check out the details and spark some discussion!
    • Jun 13, 2025: 🤗 We release the first production-ready 3D asset generation model, Hunyuan3D-2.1!

    Join our Wechat and Discord group to discuss and find help from us.

    Wechat GroupXiaohongshuXDiscord

    🤗 Community Contribution Leaderboard

    1. By @visualbruno
    1. By @VR-Jobs

    ☯️ Hunyuan3D 2.1

    Architecture

    Tencent Hunyuan3D-2.1 is a scalable 3D asset creation system that advances state-of-the-art 3D generation through two pivotal innovations: Fully Open-Source Framework and Physically-Based Rendering (PBR) Texture Synthesis. For the first time, the system releases full model weights and training code, enabling community developers to directly fine-tune and extend the model for diverse downstream applications. This transparency accelerates academic research and industrial deployment. Moreover, replacing the prior RGB-based texture model, the upgraded PBR pipeline leverages physics-grounded material simulation to generate textures with photorealistic light interaction (e.g., metallic reflections, subsurface scattering).

    Performance

    We have evaluated Hunyuan3D 2.1 with other open-source as well as close-source 3d-generation methods. The numerical results indicate that Hunyuan3D 2.1 surpasses all baselines in the quality of generated textured 3D assets and the condition following ability.

    ModelULIP-T(⬆)ULIP-I(⬆)Uni3D-T(⬆)Uni3D-I(⬆)
    Michelangelo0.07520.11520.21330.2611
    Craftsman0.07450.12960.23750.2987
    TripoSG0.07670.12250.25060.3129
    Step1X-3D0.07350.11830.25540.3195
    Trellis0.07690.12670.24960.3116
    Direct3D-S20.07060.11340.23460.2930
    Hunyuan3D-Shape-2.10.07740.13950.25560.3213
    ModelCLIP-FiD(⬇)CMMD(⬇)CLIP-I(⬆)LPIPS(⬇)
    SyncMVD-IPA28.392.3970.88230.1423
    TexGen28.242.4480.88180.1331
    Hunyuan3D-2.026.442.3180.88930.1261
    Hunyuan3D-Paint-2.124.782.1910.92070.1211

    🎁 Models Zoo

    It takes 10 GB VRAM for shape generation, 21GB for texture generation and 29GB for shape and texture generation in total.

    ModelDescriptionDateSizeHuggingface
    Hunyuan3D-Shape-v2-1Image to Shape Model2025-06-143.3BDownload
    Hunyuan3D-Paint-v2-1Texture Generation Model2025-06-142BDownload

    🤗 Get Started with Hunyuan3D 2.1

    Hunyuan3D 2.1 supports Macos, Windows, Linux. You may follow the next steps to use Hunyuan3D 2.1 via:

    Install Requirements

    We test our model with Python 3.10 and PyTorch 2.5.1+cu124.

    pip install torch==2.5.1 torchvision==0.20.1 torchaudio==2.5.1 --index-url https://download.pytorch.org/whl/cu124
    pip install -r requirements.txt
    
    cd hy3dpaint/custom_rasterizer
    pip install -e .
    cd ../..
    cd hy3dpaint/DifferentiableRenderer
    bash compile_mesh_painter.sh
    cd ../..
    
    wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth -P hy3dpaint/ckpt
    

    Code Usage

    We designed a diffusers-like API to use our shape generation model - Hunyuan3D-Shape and texture synthesis model - Hunyuan3D-Paint.

    import sys
    sys.path.insert(0, './hy3dshape')
    sys.path.insert(0, './hy3dpaint')
    from textureGenPipeline import Hunyuan3DPaintPipeline
    from textureGenPipeline import Hunyuan3DPaintPipeline, Hunyuan3DPaintConfig
    from hy3dshape.pipelines import Hunyuan3DDiTFlowMatchingPipeline
    
    # let's generate a mesh first
    shape_pipeline = Hunyuan3DDiTFlowMatchingPipeline.from_pretrained('tencent/Hunyuan3D-2.1')
    mesh_untextured = shape_pipeline(image='assets/demo.png')[0]
    
    paint_pipeline = Hunyuan3DPaintPipeline(Hunyuan3DPaintConfig(max_num_view=6, resolution=512))
    mesh_textured = paint_pipeline(mesh_path, image_path='assets/demo.png')
    

    Gradio App

    You could also host a Gradio App in your own computer via:

    python3 gradio_app.py \
      --model_path tencent/Hunyuan3D-2.1 \
      --subfolder hunyuan3d-dit-v2-1 \
      --texgen_model_path tencent/Hunyuan3D-2.1 \
      --low_vram_mode
    

    🔗 BibTeX

    If you found this repository helpful, please cite our reports:

    @misc{hunyuan3d2025hunyuan3d,
        title={Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material},
        author={Tencent Hunyuan3D Team},
        year={2025},
        eprint={2506.15442},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
    }
    
    @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}
    }
    

    Acknowledgements

    We would like to thank the contributors to the TripoSG, Trellis, DINOv2, Stable Diffusion, FLUX, diffusers, HuggingFace, CraftsMan3D, Michelangelo, Hunyuan-DiT, and HunyuanVideo repositories, for their open research and exploration.

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