PixWizardry/AI_Trellis2-WHLs-RTX-PRO-6000

⚡ Custom Wheels for RTX 6000 (Blackwell) - ComfyUI Trellis2

This repository documents pre-compiled Windows Wheels (.whl) optimized specifically for the NVIDIA RTX PRO 6000 Blackwell Workstation Edition. These wheels satisfy the hard-to-build dependencies required for Trellis2 implementation in ComfyUI.

These binaries are built to accelerate 3D generation and rendering pipelines, specifically targeting Compute Capability 12.0.

📥 Download

All .whl files are hosted on Hugging Face: 👉 Click here to download files from Hugging Face

⚠️ Hardware & Software Requirements

Do not install these wheels if you do not match the environment below. These are built for next-generation architecture and will likely fail on Ada Lovelace (4090) or Ampere (3090) cards due to the specific sm120 compilation.

Component	Requirement
GPU	NVIDIA RTX PRO 6000 (Blackwell)
Compute Capability	12.0 (sm120)
OS	Windows 11
Python	3.12.10
PyTorch	2.10
CUDA	13.0

📦 Included Wheels

The following libraries have been compiled:

1. Flash Attention (flash_attn-2.8.3)
2. Nvdiffrast (nvdiffrast-0.4.0)
3. FlexGEMM (flex_gemm-0.0.1)
4. CuMesh (cumesh-0.0.1)
5. O-Voxel (o_voxel-0.0.1)
6. Nvdiffrec (nvdiffrec-0.1.0)

🚀 ComfyUI Compatibility

These wheels are essential for running the Z-Trellis2 workflow. They have been tested with the following custom nodes:

• Recommended (Faster): visualbruno/ComfyUI-Trellis2
• Compatible: PozzettiAndrea/ComfyUI-TRELLIS2

Note: Initial testing indicates better performance/speed when using the visualbruno implementation with these specific binaries.

🛠️ Installation

This guide assumes you are using the standard ComfyUI GitHub version (cloned via git) on Windows 11.

Recommendation: It is highly recommended to install these wheels inside a Python Virtual Environment (venv) to ensure these specific CUDA-compiled wheels do not conflict with your system packages or other projects.

Step 1: Install Wheels

1. Download the .whl files from the Hugging Face Repository to a local folder (e.g., C:\wheels).
2. Open your terminal and ensure your virtual environment is active.
3. Run the following commands:

pip install "C:\wheels\flash_attn-2.8.3+cu130torch2.10.0cxx11abiTRUE-cp312-cp312-win_amd64.whl"
pip install "C:\wheels\nvdiffrast-0.4.0-cp312-cp312-win_amd64.whl"
pip install "C:\wheels\flex_gemm-0.0.1-cp312-cp312-win_amd64.whl"
pip install "C:\wheels\cumesh-0.0.1-cp312-cp312-win_amd64.whl"
pip install "C:\wheels\o_voxel-0.0.1-cp312-cp312-win_amd64.whl"
pip install "C:\wheels\nvdiffrec-0.1.0-cp312-cp312-win_amd64.whl"

Step 2: Apply Name Conversion Shim (Crucial)

The nvdiffrec wheel requires a specific module name (nvdiffrec_render) that differs from the package name. To ensure the Trellis nodes can find the module, run this Python command immediately after installation:

python -c "import site; import os; path = site.getsitepackages()[1]; f = open(os.path.join(path, 'nvdiffrec_render.py'), 'w'); f.write('from render import renderutils as _ru\nimport sys\nsys.modules[\"nvdiffrec_render\"] = _ru\nfrom render.renderutils import *'); f.close(); print('Shim created at: ' + path)"

This script creates a redirection file in your site-packages so import nvdiffrec_render works correctly.

🔗 Credits & Source Code

These wheels represent compiled versions of open-source libraries. Full credit goes to the original authors and researchers:

• Flash Attention: Tri Dao
• Other AI WHLs wildminder
• Nvdiffrast / Nvdiffrec: NVLabs
• FlexGEMM: JeffreyXiang
• CuMesh: JeffreyXiang
• TRELLIS 2 / O-Voxel: Microsoft

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Disclaimer: These files are provided "as is" for experimental builds on Blackwell architecture. Please check the original repositories for licensing information regarding commercial use.