ComfyUI_TRELLIS

ComfyUI_TRELLIS
★ 181

三维生成结构化潜变量ComfyUI插件TRELLIS集成
在ComfyUI中集成TRELLIS,生成与处理结构化3D潜变量,支持可扩展且多用途的三维生成工作流。
💡 在ComfyUI内使用TRELLIS生成可扩展的结构化3D潜变量用于建模。
🍴 7 Forks💻 Jupyter Notebook🔄 2025-08-17
📦
网盘下载
复制链接后前往夸克网盘下载
https://pan.quark.cn/s/c1eafc754fbb
📦 requirements.txt
--extra-index-url
https://download.pytorch.org/whl/cu117
torch==2.0.0
torchvision==0.15.0
omegaconf
torchmetrics==0.10.3
fvcore
iopath
xformers==0.0.18
submitit
--extra-index-url
https://pypi.nvidia.com
cuml-cu11
📄 README

ComfyUI_TRELLIS

You can use TRELLIS in comfyUI

TRELLIS, Structured 3D Latents for Scalable and Versatile 3D Generation


Update 2025/04/05

  • if update kaolin likes torch2.51 to 2.6 diff-gaussian-rasterization need rebuild. 如果升级kaolin,diff-gaussian-rasterization需要重新编译
  • support txt to 3D and meshto3D
  • kaolin support torch2.6 now;
  • 1. Installation

    In the ./ComfyUI /custom_node directory, run the following:

    git clone https://github.com/smthemex/ComfyUI_TRELLIS.git

    2. Requirements

    本插件的测试环境是python3.11,torch2.6 cu126…

    The testing environment for this node is Python 3.11, torch2.6 cu126…

    pip install -r requirements.txt

    以下必须要安装成功,否则无法运行!!!

    以下示例是按torch2.5.1 and cu124安装,你可以改成你当前环境的cu和torch,源于issue3

    The following must be installed successfully, otherwise it cannot run !!!

    Example for torch2.5.1 and cu124,you can change to torch2.4.0 or other from issue3

    xformers 和 flash-attention 可以只安装一项

    xformers and Flash Attention can be installed with only one option

    pip install https://github.com/bdashore3/flash-attention/releases/download/v2.7.1.post1/flash_attn-2.7.1.post1+cu124torch2.5.1cxx11abiFALSE-cp310-cp310-win_amd64.whl
    
    pip install kaolin -f https://nvidia-kaolin.s3.us-east-2.amazonaws.com/torch-2.5.1_cu124.html
    # if torch 2.6
    git clone git@github.com:NVIDIAGameWorks/kaolin.git
    cd kaolin
    pip install .
    
    git clone https://github.com/NVlabs/nvdiffrast.git ./tmp/extensions/nvdiffrast
    pip install ./tmp/extensions/nvdiffrast
    #if install nvdiffrast error ,see below how to fix it 
    
    git clone --recurse-submodules https://github.com/JeffreyXiang/diffoctreerast.git ./tmp/extensions/diffoctreerast
    pip install ./tmp/extensions/diffoctreerast
    
    git clone https://github.com/autonomousvision/mip-splatting.git ./tmp/extensions/mip-splatting
    pip install ./tmp/extensions/mip-splatting/submodules/diff-gaussian-rasterization/
    # if update torch2.6 ,must del diff-gaussian-rasterization and reinstall it
    
    pip install spconv-cu120	 #if cuda>120
    # pip install spconv-cu118  # if cuda118 
    
    # 在..ComfyUI_TRELLIS目录下,复制vox2seq插件目录到temp,然后pip安装 Under ComfyUI_TRELLIS directory,Copy the Vox2seq plugin to temp and install it using pip  
    cp -r ./extensions/vox2seq ./tmp/extensions/vox2seq
    pip install ./tmp/extensions/vox2seq
    

    2.1 Other Need

  • kaolin find wheel,or install normal在这里找kaolin的各种版本轮子
  • flash attention or windows/linux find wheel,在这里找flash attention的各种版本轮子
  • visualstudio visual studio2019 or high windows必须安装
  • spconv find your cuda version ,if version.120 use spconv-cu120 cuda版本大于120的只能用spconv-cu120,其他根据对应地址版本安装
  • if somebody install nvdiffrast fail can see how to fix it in here
  • if install utils3d fail can see how to fix it in here @planb788
  • 2.2 visualstudio & cuda

  • 必须将visualstudio的cl.exe加入系统的环境变量path中,以下是windows系统示例,具体以自己的系统目录为准;
  • The cl.exe of VisualStudio must be added to the system’s environment variable path. Here is an example, please refer to your own system directory for details;
  •  Path:        C:\Program Files(x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.28610\bin\Hostx64\x64 # or  other version 或者其他版本
     Path:        C:\Program Files(x86)\Microsoft Visual Studio\2019\Community\VC\Tools\MSVC\14.29.28610\bin\Hostx64\x64\cl.exe # or  other version 或者其他版本
     Path:        C:\Users\yourname\AppData\Roaming\Python\Python311\Scripts # python 
     CUDA_PATH:   C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.4 # or  other version 或者其他版本
    

    2.3 if use glb2fbx

    Need ‘ pip install bpy ‘ and install ‘ blender ‘

    3. Models Required

  • 3.1 TRELLIS_repo,download or using online… if use img mode JeffreyXiang/TRELLIS-image-large or txt mode JeffreyXiang/TRELLIS-text-large 如果预下载 ,在repo位置填写:x:/your/path/JeffreyXiang/TRELLIS-image-large ;if pre download ,fill local path in repo like this: x:/your/path/JeffreyXiang/TRELLIS-image-large
  • ├── anypath/JeffreyXiang/TRELLIS-image-large/
    |   ├── pipeline.json
    |   ├── ckpts/
    |            ├── slat_dec_gs_swin8_B_64l8gs32_fp16.json
    |            ├── slat_dec_gs_swin8_B_64l8gs32_fp16.safetensors
    |            ├── slat_dec_mesh_swin8_B_64l8m256c_fp16.json
    |            ├── slat_dec_mesh_swin8_B_64l8m256c_fp16.safetensors
    |            ├── slat_dec_rf_swin8_B_64l8r16_fp16.json
    |            ├── slat_dec_rf_swin8_B_64l8r16_fp16.safetensors
    |            ├── slat_enc_swin8_B_64l8_fp16.json
    |            ├── slat_enc_swin8_B_64l8_fp16.safetensors
    |            ├── slat_flow_img_dit_L_64l8p2_fp16.json
    |            ├── slat_flow_img_dit_L_64l8p2_fp16.safetensors
    |            ├── ss_dec_conv3d_16l8_fp16.json
    |            ├── ss_dec_conv3d_16l8_fp16.safetensors
    |            ├── ss_enc_conv3d_16l8_fp16.json
    |            ├── ss_enc_conv3d_16l8_fp16.safetensors
    |            ├── ss_flow_img_dit_L_16l8_fp16.json
    |            ├── ss_flow_img_dit_L_16l8_fp16.safetensors
  • 3.2 dinov2 and clip
  • 因为官方的代码每次加载dinov2都要连GitHub,所以我改成了离线版的,需要下载dinov2模型,地址 ,clip是常规的openai/clip-vit-large-patch14 模型,也为方便大陆用户改成了离线版

    模型放在comfyUI/models/dinov2和clip目录下

    ├── ComfyUI/models/dinov2 # if use img  mode
    |      ├── dinov2_vitl14_reg4_pretrain.pth
    ├── ComfyUI/models/clip # if use txt  mode
    |      ├── clip_l.safetensors


    4 Example

  • img to 3D or txt to 3D
  • 5 Citation

    microsoft/TRELLIS

    @article{xiang2024structured,
        title   = {Structured 3D Latents for Scalable and Versatile 3D Generation},
        author  = {Xiang, Jianfeng and Lv, Zelong and Xu, Sicheng and Deng, Yu and Wang, Ruicheng and Zhang, Bowen and Chen, Dong and Tong, Xin and Yang, Jiaolong},
        journal = {arXiv preprint arXiv:2412.01506},
        year    = {2024}
    }

    facebookresearch/dinov2

    @misc{oquab2023dinov2,
      title={DINOv2: Learning Robust Visual Features without Supervision},
      author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
      journal={arXiv:2304.07193},
      year={2023}
    }
    @misc{darcet2023vitneedreg,
      title={Vision Transformers Need Registers},
      author={Darcet, Timothée and Oquab, Maxime and Mairal, Julien and Bojanowski, Piotr},
      journal={arXiv:2309.16588},
      year={2023}
    }