Run TRELLIS.2 Image-to-3D generation natively on Apple Silicon
I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as…
In plain words
Run TRELLIS.2 is a port of Microsoft's image-to-3D model that runs natively on Apple Silicon using PyTorch MPS instead of requiring CUDA. It converts single photographs into detailed 3D meshes with approximately 400,000 vertices in about 3.5 minutes on M4 Pro hardware. The project replaces GPU-specific operations with pure-PyTorch alternatives, enabling offline 3D generation without cloud dependency. It is designed for developers and creators working on Apple computers who need local 3D model generation from images.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I ported Microsoft's TRELLIS.2 (4B parameter image-to-3D model) to run on Apple Silicon via PyTorch MPS. The original requires CUDA with flash_attn, nvdiffrast, and custom sparse convolution kernels: none of which work on Mac. I replaced the CUDA-specific ops with pure-PyTorch alternatives: a gather-scatter sparse 3D convolution, SDPA attention for sparse transformers, and a Python-based mesh extraction replacing CUDA hashmap operations. Total changes are a few hundred lines across 9 files. Generates ~400K vertex meshes from single photos in about 3.5 minutes on M4 Pro (24GB). Not as fast as H100 (where it takes seconds), but it works offline with no cloud dependency. https://github.com/shivampkumar/trellis-mac
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