ComfyUI-3D-Pack

An extensive node suite that enables ComfyUI to process 3D inputs (Mesh & UV Texture, etc) using cutting edge algorithms (3DGS, NeRF, Differentiable Rendering, SDS/VSD Optimization, etc.)

Note: this project is still a WIP and not been released into ComFyUI package database yet

ps: I'll show some generated result soon

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[IMPORTANT!!!]
Currently this package is only been tested in following setups:

• Windows 10/11
• Miniconda/Conda Python 3.11.7
  • I tried install this package with ComfyUI embed python env first, but I can't find a way to build CUDA related libraries, e.g. diff-gaussian-rasterization, nvdiffrast, simple-knn.
• Torch version: 2.1.2+cu121/V.2.1.2+cu118

Install:

Assume you have already downloaded ComfyUI

First download Miniconda (One of the best way to manage a clean and separated python envirments)

• Alternatively you can check this tutorial: Installing ComfyUI with Miniconda On Windows and Mac

# Go to your Your ComfyUI root directory, for my example:
cd C:\Users\reall\Softwares\ComfyUI_windows_portable 

conda create -p ./python_miniconda_env/ComfyUI python=3.11

# conda will tell what command to use to activate the env
conda activate C:\Users\reall\Softwares\ComfyUI_windows_portable\python_miniconda_env\ComfyUI

# This package also works with cu118
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121

pip install -r ./ComfyUI/requirements.txt

# Then go to ComfyUI-3D-Pack directory under the .\ComfyUI\custom_nodes for my example is:
cd C:\Users\reall\Softwares\ComfyUI_windows_portable\ComfyUI\custom_nodes\ComfyUI-3D-Pack
# Finally you can double click following .bat script or run it in CLI:
install.bat

Just in case install.bat may not working in your PC, you could also run the following commands under this package's root directory:

# First make sure the Conda env: python_miniconda_env\ComfyUI is activated, then go to Go to ComfyUI Root Directory\ComfyUI\custom_nodes\ComfyUI-3D-Pack and:
pip install -r requirements.txt

git clone --recursive https://github.com/ashawkey/diff-gaussian-rasterization
pip install ./diff-gaussian-rasterization

pip install ./simple-knn

git clone --recursive https://github.com/NVlabs/nvdiffrast/`
pip install ./nvdiffrast

Run:

Copy the files inside folder __New_ComfyUI_Bats to your ComfyUI root directory, and double click run_nvidia_gpu_miniconda.bat to start ComfyUI!

• Alternatively you can just activate the Conda env: python_miniconda_env\ComfyUI, and go to your ComfyUI root directory then run command python ./ComfyUI/main.py

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Currently support:

• For use case please check example workflows

  • Note: you need to put Example Inputs Files & Folders under ComfyUI Root Directory\ComfyUI\input folder before you can run the example workflow

• Stack Orbit Camera Poses node to automatically generate all range of camera pose combinations

  • You can use it to conditioning the StableZero123 (You need to Download the checkpoint first), with full range of camera poses in one prompt pass

  • You can use it to generate the orbit camera poses and directly input to other 3D process node (e.g. GaussianSplatting and BakeTextureToMesh)

  • Example usage:

    

• Load 3D file (.obj, .ply, .glb)

  • Also have node that support save and load 3DGS .ply file

• 3D Gaussian Splatting, with:

  • Improved Differential Gaussian Rasterization
  • Better Compactness-based Densification method from Gsgen,
  • Support initialize gaussians from given 3D mesh (Optional)
  • Support mini-batch optimazation
  • Multi-View images as inputs
  • Export to .ply support

• Bake Multi-View images into UVTexture of given 3D mesh using Nvdiffrast, supports:

  • Export to .obj, .ply, .glb

• Deep Marching Tetrahedrons

  • Allow convert 3DGS .ply file to 3D mesh
    Note: I didn't spent time to turn the hyperprameters yet, the result will be improved in the future!

To-Do Next:

• Add interactive 3D UI inside ComfuUI to visulaize training and generated results for 3D representations
• Add DMTet algorithm to allow convertion from points cloud(Gaussian/.ply) to mesh (.obj, .ply, .glb)
• Improve DMTet result & add support to training it with images(RGB, Alpha, Normal Map)
• Add a general SDS/VSD Optimization algorithm to allow training 3D representations with diffusion model, The real fun begins here ;)
• Add a few best Nerf algorithms (No idea yet, instant-ngp maybe?)

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Tips

• The world & camera coordinate system is the same as OpenGL:

    World            Camera        
  
     +y              up  target                                              
     |               |  /                                            
     |               | /                                                
     |______+x       |/______right                                      
    /                /         
   /                /          
  /                /           
 +z               forward           

elevation: in (-90, 90), from +y to -y is (-90, 90)
azimuth: in (-180, 180), from +z to +x is (0, 90)

• If you encounter OpenGL errors (e.g., [F glutil.cpp:338] eglInitialize() failed), then set force_cuda_rasterize to true on corresponding node