LiTo Runtime 8-bit Affine for mlx-spatial
A self-contained LiTo inference bundle with selective affine INT8 weights for Apple Silicon. Accuracy-sensitive boundaries remain in FP32. This is an unofficial derivative for non-commercial research use, not an Apple release.
Use
pip install \
"mlx-spatial @ git+https://github.com/appautomaton/mlx-spatial.git@afa6b6512567dc9964294a1cec8601e1b505802e"
hf download appautomaton/lito-research-mlx-8bit \
--local-dir weights/lito-research-mlx-8bit
mlx-spatial-lito validate weights/lito-research-mlx-8bit
mlx-spatial-lito generate inputs/lito/object-rgba.png \
--weights-root weights/lito-research-mlx-8bit \
--output outputs/lito/object-8bit.ply \
--format ply \
--num-steps 20 \
--cfg-scale 3.0 \
--print-metrics
The output is a 3D Gaussian Splat PLY, not a triangle mesh. A clean RGBA foreground matte is strongly recommended.
Bundle
| File | Logical tensors | Quantized matrices | Bytes |
|---|---|---|---|
image_to_3d/lito_dit_rgba.safetensors | 1,016 | 224 | 2,004,347,727 |
tokenizer/lito_new.safetensors | 467 | 124 | 168,537,103 |
dependencies/trellis/ckpts/ss_dec_conv3d_16l8_fp16.safetensors | 74 | 0 | 147,591,972 |
| Runtime weights | 1,557 | 348 | 2,320,476,802 |
The bundle also includes the decoder config, Microsoft MIT license, and an
immutable source manifest under dependencies/trellis/.
The LiTo quantization scheme is affine 8-bit with group size 64. Packed weights
are stored as uint32 with FP32 scales and biases and execute directly through
mx.quantized_matmul. Internal attention and MLP matrices in the EMA DiT,
Gaussian decoder, and voxel decoder are quantized. The image conditioner,
convolutions, embeddings, normalizations, boundary projections, and output
heads remain FP32.
Embedded TRELLIS Decoder
LiTo uses the TRELLIS sparse-structure decoder to convert voxel latents into
Gaussian initialization coordinates. This bundle embeds the exact checkpoint
from microsoft/TRELLIS-image-large revision
25e0d31ffbebe4b5a97464dd851910efc3002d96:
dependencies/trellis/LICENSE
dependencies/trellis/SOURCE.json
dependencies/trellis/ckpts/ss_dec_conv3d_16l8_fp16.json
dependencies/trellis/ckpts/ss_dec_conv3d_16l8_fp16.safetensors
The decoder safetensors SHA-256 is
1c76d4a40519aa2d711cc263a8404105231ac26db31d946bed48b84fee79009a.
The runtime does not search a separate TRELLIS checkout.
Verification
- Both checkpoints pass
mlx-spatial-lito validate. - Architecture inspection recovers 28 DiT blocks, 6 Gaussian Perceiver blocks, and 4 voxel decoder blocks.
- Real-weight Linear probes measured
0.51%–0.61%relative RMSE and cosine similarity above0.99998against FP32. - An uncapped 20-step run produced 557,568 finite Gaussians in 2 minutes 39.76 seconds, with 11.60 GiB peak active MLX memory.
- A separate bundle-local 20-step validation produced 8,192 finite Gaussians after explicitly capping occupied cells for packaging verification.
These figures are one local Apple Silicon observation, not a general benchmark or an official quality-equivalence claim.
Limitations and Licenses
- Inference only; training and mesh-specific modules are intentionally absent.
- Quantization can change generation details relative to FP32.
- Single-view reconstruction cannot determine unseen geometry with certainty.
- Commercial use is not permitted by Apple's model license.
LiTo weights are covered by the bundled LICENSE_MODEL. The embedded TRELLIS
decoder is covered by the MIT License under dependencies/trellis/LICENSE.
Apple Machine Learning Research Model is licensed under the Apple Machine Learning Research Model License Agreement.