trellis2-mlx / README.md
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Add shape_enc/tex_enc VAE encoders (TexturingPipeline); gated-access README (DINOv3 terms)
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metadata
license: other
license_name: dinov3-license
license_link: https://ai.meta.com/resources/models-and-libraries/dinov3-license/
library_name: mlx
tags:
  - mlx
  - apple-silicon
  - image-to-3d
  - 3d-generation
  - trellis
  - built-with-dinov3
extra_gated_prompt: >-
  This repository includes the DINOv3 ViT-L/16 image conditioner
  (dino.safetensors), redistributed under the DINOv3 License (Meta Platforms,
  Inc.) — see DINOv3_LICENSE.md and
  https://ai.meta.com/resources/models-and-libraries/dinov3-license/ . By
  requesting access you agree to comply with the DINOv3 License, including its
  Acceptable Use Policy and Trade Control terms, and to display "Built with
  DINOv3" attribution in any product or distribution that ships the conditioner.
  All other components are Microsoft TRELLIS.2 weights under MIT.
extra_gated_fields:
  I agree to the DINOv3 License and its Acceptable Use Policy: checkbox
  I will display "Built with DINOv3" attribution when redistributing: checkbox
extra_gated_button_content: Agree and access repository

trellis2-mlx — consolidated weights for the TRELLIS.2 Apple-Silicon port

Consolidated weights for xocialize/mlx-trellis2-swift — a verified Swift-MLX re-port of Microsoft TRELLIS.2 (single image → 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.

⚠️ 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 — 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 — 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 · 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
shape_enc.safetensors shape FlexiDualGrid VAE encoder (re-texturing / mesh→SLat) microsoft/TRELLIS.2-4B
tex_flow_512.safetensors · tex_flow_1024.safetensors texture SLat DiT (512 / 1024 tiers) microsoft/TRELLIS.2-4B
tex_dec.safetensors texture VAE decoder microsoft/TRELLIS.2-4B
tex_enc.safetensors texture VAE encoder (re-texturing) 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 — 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) — 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 — MIT (Microsoft).
  • Packaging metadata (this card, NOTICE, normalization.json, layout) — MIT (Xocialize), see LICENSE.

See NOTICE for the full attribution.