The Practical Guide to Image-to-3D Generation on Mac

If you’ve been trying to run state-of-the-art image-to-3D models on your Mac, you’ve likely hit the same wall I did: everything is built for NVIDIA GPUs. Microsoft’s TRELLIS.2 is a powerhouse, but its reliance on CUDA-only libraries like flex_gemm and nvdiffrast effectively locked out anyone running Apple Silicon. That changed with the trellis-mac port. You can now run image-to-3D generation natively on your Mac without needing a cloud GPU or a dedicated PC rig.

Here is the reality of running this locally: it isn't instantaneous, but it is functional. On an M4 Pro with 24GB of unified memory, you’re looking at roughly 3.5 minutes to generate a 400K+ vertex mesh. The process is heavy, peaking at around 18GB of RAM, so don't expect to run this alongside a dozen Chrome tabs and a heavy IDE.

The magic here isn't just that it runs; it’s how the developer bypassed the CUDA bottleneck. By replacing hardcoded CUDA kernels with pure-PyTorch and Python alternatives, the port manages to replicate sparse 3D convolution and mesh extraction using spatial hashing and SDPA backends.

If you want to get this running, follow these steps:

1. Clone the repository and ensure you have Python 3.11+ installed.
2. Authenticate with HuggingFace to access the gated model weights.
3. Run the setup.sh script to create your virtual environment and apply the necessary patches to the TRELLIS.2 codebase.
4. Execute python generate.py path/to/image.png to start the pipeline.

There are trade-offs you need to be aware of before you commit to this workflow. Because the port replaces CUDA-specific differentiable rasterizers, you won't get texture exports—you’re limited to vertex colors for now. Additionally, hole filling is currently disabled, so your meshes might require a quick pass in Blender or MeshLab to clean up minor artifacts.

The pure-PyTorch sparse convolution is roughly 10x slower than the original CUDA implementation. This is the primary bottleneck, but it’s a small price to pay for the ability to iterate on 3D assets locally. If you are building a pipeline that requires rapid prototyping of 3D objects from 2D references, this is currently your best path forward on macOS.

Why does this matter for your workflow? It removes the friction of moving data between your local machine and a remote GPU instance. You keep your assets local, your privacy intact, and your development loop tight.

If you’re ready to experiment with local 3D generation, clone the repo and test it on your own hardware. It’s a significant step toward making high-end generative AI accessible to the Mac ecosystem. Try this today and share what you find in the comments—I’m curious to see how it handles different mesh complexities on older M1 or M2 chips.