Instructions to use xocialize/trellis2-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use xocialize/trellis2-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir trellis2-mlx xocialize/trellis2-mlx
- Trellis
How to use xocialize/trellis2-mlx with Trellis:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
trellis2-mlx โ consolidated weights for the TRELLIS.2 Apple-Silicon port
Consolidated weights for xocialize/mlx-trellis2-swift โ a verified Swift-MLX re-port of Microsoft TRELLIS.2 (single image โ textured 3D mesh) that runs on Apple Silicon (no CUDA). This repo bundles, as one download, the exact model files that pipeline loads.
Built with DINOv3.
โ ๏ธ This is a pipeline artifact, not a model mirror
This repository exists only to package the weights the mlx-trellis2-swift pipeline needs into a single, engine-materializable download. The files are byte-for-byte upstream weights, renamed only by filename to the pipeline's component layout โ no weights are modified.
It is not a general-purpose model distribution, and not a mirror of DINOv3. The DINOv3 image conditioner (dino.safetensors) is redistributed here solely because the TRELLIS.2 pipeline requires it, under the DINOv3 License.
Want DINOv3 on its own? Please get it from Meta's official, access-controlled repository and accept Meta's terms โ do not use this repo to obtain standalone DINOv3 or to bypass Meta's gating:
https://huggingface.co/facebook/dinov3-vitl16-pretrain-lvd1689m
Contents
| File | Component | Upstream |
|---|---|---|
dino.safetensors |
DINOv3 ViT-L/16 image conditioner | facebook/dinov3-vitl16-pretrain-lvd1689m |
struct_flow.safetensors |
sparse-structure DiT | microsoft/TRELLIS.2-4B |
struct_dec.safetensors |
sparse-structure decoder | microsoft/TRELLIS-image-large |
shape_flow_512.safetensors ยท shape_flow_1024.safetensors |
shape SLat DiT (512 / 1024 tiers) | microsoft/TRELLIS.2-4B |
shape_dec.safetensors |
shape FlexiDualGrid VAE decoder | microsoft/TRELLIS.2-4B |
tex_flow_512.safetensors ยท tex_flow_1024.safetensors |
texture SLat DiT (512 / 1024 tiers) | microsoft/TRELLIS.2-4B |
tex_dec.safetensors |
texture VAE decoder | microsoft/TRELLIS.2-4B |
normalization.json |
per-channel SLat mean/std | microsoft/TRELLIS.2-4B pipeline.json |
Internal tensor keys are the original upstream keys (unmodified); only the file names follow the pipeline's component layout.
Usage
These weights are consumed automatically by the mlx-trellis2-swift MLXEngine imageTo3D package โ first-run materialization downloads this snapshot into the engine's model store. You do not normally fetch it by hand.
Licenses & attribution
- DINOv3 weights (
dino.safetensors) โ DINOv3 License (Meta Platforms, Inc.), the most-restrictive component and the governing license for shipping the conditioner. Any product distributing it must display "Built with DINOv3", include the DINOv3 License, and comply with the DINOv3 Acceptable Use Policy. - TRELLIS.2 / TRELLIS-image-large weights โ MIT (Microsoft).
- Packaging metadata (this card,
NOTICE,normalization.json, layout) โ MIT (Xocialize), see LICENSE.
See NOTICE for the full attribution.
Quantized