40 lines
1.8 KiB
Markdown
40 lines
1.8 KiB
Markdown
# ComfyUI-SegviGen
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A ComfyUI implementation of SegviGen, providing precise 3D part segmentation for SegviGen.
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## Installation
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1. Clone this repository to your `ComfyUI/custom_nodes/` folder.
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2. Install the required base dependencies:
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```bash
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pip install -r requirements.txt
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```
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3. Run the specialized installation script to handle custom CUDA wheels and internal libraries:
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```bash
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python install.py
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```
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## Key Features
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- **Automated Model Downloading**: All model components (Trellis base, SegviGen checkpoints, BiRefNet, DinoV3) are automatically downloaded on first use.
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- **Decoupled Loaders**: Individual control over Shape/Tex Encoders and Decoders for optimal VRAM management.
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- **Memory Management**: Built-in support for DMA-based loading (`load_torch_file`), RAM-safe initialization (`init_empty_weights`), and proactive cache clearing.
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- **Granular Pipeline**: Modular nodes for preprocessing, conditioning, sampling, and post-processing (VXZ, Latent Slats, Voxel, GLB).
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## Node Overview
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- **SegviGen Shape/Tex Loader Nodes**: Targeted loaders for individual TRELLIS components.
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- **SegviGen Checkpoint Loader**: Loads the SegviGen flow checkpoints (Full/Interactive).
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- **SegviGen Image Preprocessor**: Automates background removal (BiRefNet) and image preparation.
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- **SegviGen Image To Cond**: Generates conditioning embeddings (DinoV3).
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- **SegviGen Sampler**: Performs the core texture sampling with optional point-based guidance.
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- **SegviGen Slat To Voxel**: Decodes texture latents to voxel representation.
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## Acknowledgements
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Original SegviGen implementation by [fenghora](https://huggingface.co/fenghora/SegviGen).
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Uses [TRELLIS 2.0](https://huggingface.co/microsoft/TRELLIS.2-4B) by Microsoft.
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