trellis2-mlx-8bit

appautomaton/trellis2-mlx-8bit

trellis2-mlx-8bit is a image-to-3d model from appautomaton. Use it for the image-to-3d task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as other.

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Public

Updated Aug 14, 2026

Repo size

11.2 GB

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Public

Hugging Face

Repo makeup

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.safetensors11.2 GB · 100%

At a glance

Task: Image-to-3D
Library: mlx
License: other
Access: Public
Created: Aug 14, 2026
Updated: Aug 14, 2026
SHA: a1cc385e

Base models

Model card

From the Hugging Face model README

TRELLIS.2 MLX 8-bit Affine for `mlx-spatial`

A self-contained TRELLIS.2 image-to-3D bundle with selective 8-bit affine weights for direct execution by mlx-spatial on Apple Silicon. The bundle includes the TRELLIS.2 checkpoints, DINOv3 image conditioner, and RMBG-2.0 foreground extractor required by the local inference pipeline.

Large transformer and block-internal MLP matrices execute through MLX's packed quantized matrix multiplication. Convolutions and accuracy-sensitive boundary tensors remain in their source precision.

This is an unofficial quantized derivative. It is not a Microsoft, Meta, or BRIA release. Because the bundle contains RMBG-2.0, it is restricted to non-commercial use unless the user obtains separate commercial rights from BRIA.

Variants

Variant	Precision	Complete bundle size	Model
Source dependency set	BF16, FP16, and FP32	18.483 GB	TRELLIS.2-4B + DINOv3 ViT-L/16 + RMBG-2.0
MLX 8-bit bundle	Selective affine INT8 with retained BF16, FP16, and FP32	11.248 GB	This model

The complete 8-bit bundle is 39.1% smaller and does not require duplicate full-precision checkpoints. Both forms expose the same 5,587 logical tensors with identical names, shapes, and declared source dtypes.

Compatibility

This format requires an mlx-spatial build that includes TRELLIS.2 affine checkpoint support. Until that support is available in a tagged PyPI release, install the current project revision:

pip install \
  "mlx-spatial @ git+https://github.com/appautomaton/mlx-spatial.git@main"

The runtime targets Apple Silicon, Python 3.13, and MLX 0.32.x. It does not use Torch, CUDA, or a dequantized full-precision checkpoint.

Use

Download the complete bundle:

hf download appautomaton/trellis2-mlx-8bit \
  --local-dir weights/trellis2-mlx-8bit

Validate its three runtime roots:

mlx-spatial-trellis2 validate \
  --root weights/trellis2-mlx-8bit

mlx-spatial-trellis2 dinov3-validate \
  --root weights/trellis2-mlx-8bit/dinov3

mlx-spatial-trellis2 rmbg-validate \
  --root weights/trellis2-mlx-8bit/rmbg

Generate a textured GLB:

mlx-spatial-trellis2 generate-textured \
  weights/trellis2-mlx-8bit \
  inputs/trellis2/object.png \
  --dino-root weights/trellis2-mlx-8bit/dinov3 \
  --rmbg-root weights/trellis2-mlx-8bit/rmbg \
  --output outputs/trellis2/object-8bit/model.glb \
  --pipeline-type 1024_cascade \
  --seed 42

1024_cascade is the recommended quality tier; use 512 when lower memory use or faster iteration matters more.

Do not pass --slat-steps for a quality run; the model configuration uses 12 steps. --slat-steps 1 is intended only for a quick runtime smoke test.

RGBA inputs use their alpha channel directly. RGB inputs are passed through the bundled RMBG-2.0 model. A clean single-object foreground and an uncropped silhouette generally produce the most useful reconstruction.

Bundle

Component	Logical tensors	INT8 matrices	Safetensors bytes
TRELLIS.2 flow, VAE, and decoder checkpoints	4,418	1,306	10,560,255,296
`dinov3/model.safetensors`	415	144	344,386,453
`rmbg/model.safetensors`	754	96	343,122,500
Total	5,587	1,546	11,247,764,249

Configuration, model card, license, and RMBG support files account for the small difference between checkpoint bytes and the complete directory size.

The repository preserves the TRELLIS.2 checkpoint names and layout used by the source model. DINOv3 and RMBG-2.0 are bundled under dinov3/ and rmbg/ so a single download contains every runtime weight.

Quantization

The quantization scheme is affine 8-bit with group size 64. Packed weights are stored as uint32 with FP32 scales and biases, then executed directly through mx.quantized_matmul.

The following block-internal two-dimensional weights are quantized:

self-attention QKV and output projections in all five TRELLIS.2 flow DiTs;
cross-attention query, key/value, and output projections in those DiTs;
DiT MLP input and output matrices;
internal ConvNeXt MLP matrices in the shape and texture encoders and decoders;
DINOv3 attention Q/K/V/output and MLP up/down projections;
RMBG-2.0 Swin attention QKV/output and MLP FC matrices.

The following tensors remain in their source precision:

all dense and sparse convolutions, including the sparse-structure decoder;
input, output, patch, latent, subdivision, and final prediction boundaries;
timestep and condition projections, embeddings, learned tokens, and positional tensors;
normalization parameters, RMSNorm parameters, modulation tensors, and biases;
RMBG stage-transition reductions;
matrices whose input dimension is incompatible with group size 64.

Physical packed arrays use internal qweight, scale, and bias suffixes. The runtime reconstructs the original logical names from safetensors metadata, so the existing TRELLIS.2 pipeline and configuration continue to use the same checkpoint contract. Format details are embedded under mlx_spatial.trellis2.quantization.

Reproducing the Bundle

Starting from the three source weight roots:

mlx-spatial-trellis2-quantize \
  weights/trellis2 \
  weights/trellis2-mlx-8bit \
  --dinov3-root weights/dinov3-vitl16-pretrain-lvd1689m \
  --rmbg-root weights/rmbg2 \
  --bits 8 \
  --group-size 64

The command creates a complete runtime root. Do not add full-precision copies of the quantized checkpoints to the 8-bit repository.

Verification

Logical checkpoint inspection matched all 5,587 source tensor names, shapes, and dtypes exactly.
The quantization inventory contains 1,546 packed matrices: 1,050 DiT matrices, 256 encoder/decoder MLP matrices, 144 DINOv3 matrices, and 96 RMBG-2.0 matrices.
The complete mlx-spatial repository suite passed 1,175 tests; 10 tests were skipped, 50 were deselected, and 3 expected failures remained.
A 512-resolution, 12-step run completed background handling, DINOv3 conditioning, sparse-structure sampling, shape and texture SLat sampling, both decoders, and textured GLB export with no blocker.
That run produced 4,022 shape tokens, 1,041,541 decoded shape/texture points, and a 12,083,344-byte GLB. The rendered asset retained coherent geometry and recognizable appearance.
The same run completed in 152.23 seconds, observed 3.516 GB peak MLX allocator use, and recorded zero swap growth.
A separate 1024_cascade, 12-step run completed the same end-to-end path and produced a Blender-readable 12,670,448-byte GLB with 199,884 faces and embedded 1024 x 1024 PBR textures.

The 512 runtime and memory figures are one local Apple Silicon observation, not a general benchmark. The 1024_cascade run overlapped another MLX workload, so it establishes compatibility and artifact health rather than performance. Neither run is a formal claim of visual equivalence to the source weights.

Limitations

Quantization changes the sampling trajectory. Geometry, pose, topology, texture placement, material values, and unseen surfaces can differ from the source-precision model even with the same seed.
End-to-end validation covers 512 and 1024_cascade. The standalone 1024 and 1536_cascade routes have not received an equivalent quality evaluation.
Single-view reconstruction cannot determine unseen geometry with certainty.
Fine detail depends on foreground extraction, cropping, occlusion, reflections, transparency, and thin structures.
Generated meshes can contain small holes, non-manifold regions, or other topology requiring downstream cleanup.
The packed checkpoint format requires mlx-spatial; generic safetensors readers expose the physical packed arrays rather than the logical matrices.
This bundle supports inference, not training or fine-tuning.
Commercial use of the bundled RMBG-2.0 weights requires separate rights from BRIA.

License and Attribution

This is a combined derivative; no single permissive license covers every included component. Read LICENSES.md and all referenced terms before downloading, using, or redistributing the bundle.

Microsoft TRELLIS.2 files are provided under the MIT License. A copy is included as LICENSE_TRELLIS2.
Meta DINOv3 files and derivatives are governed by the DINOv3 License. The complete agreement is included as LICENSE_DINOV3.md, as required for redistribution.
BRIA RMBG-2.0 is provided for non-commercial use under CC BY-NC 4.0. Its attribution and change notice are included in LICENSE_RMBG2.md. Commercial use requires separate authorization from BRIA.

The combined bundle must therefore be treated as non-commercial unless the user has obtained all additional rights that their use requires. This repository is not affiliated with or endorsed by Microsoft, Meta, BRIA, or the original authors.