LiTo Runtime FP32 for mlx-spatial
Run Apple's LiTo image-to-3D Gaussian Splat model on Apple Silicon through mlx-spatial, using MLX-ready safetensors instead of local .ckpt conversion.
This runtime-pruned bundle is for researchers who want a practical Mac-native
LiTo inference path: download one repository and generate a 3D Gaussian Splat
PLY from an input image. No CUDA is required.
Quick Start: Image to 3DGS on Apple Silicon
Install mlx-spatial:
1pip install \
2 "mlx-spatial @ git+https://github.com/appautomaton/mlx-spatial.git@afa6b6512567dc9964294a1cec8601e1b505802e"
This model card requires the bundle-local LiTo dependency layout introduced by
the immutable runtime commit above.
Download this model bundle:
1hf download appautomaton/lito-research-mlx \
2 --local-dir weights/lito-research-mlx
Validate the local layout:
1mlx-spatial-lito validate weights/lito-research-mlx
2mlx-spatial-lito inspect weights/lito-research-mlx --limit 10
Generate a Gaussian-splat PLY:
1mlx-spatial-lito generate inputs/lito/sample.png \
2 --weights-root weights/lito-research-mlx \
3 --output outputs/lito/sample.ply \
4 --memory-profile balanced \
5 --print-metrics
The output is a 3D Gaussian Splat PLY, not a mesh. Use a 3DGS-aware viewer such as KIRI Engine's 3DGS Render Blender add-on. Blender's native PLY importer can read the container but does not render LiTo Gaussian splat fields correctly.
What This Model Bundle Provides
This Hugging Face repository is a self-contained LiTo runtime bundle for
mlx-spatial:
1tokenizer/lito_new.safetensors
2image_to_3d/lito_dit_rgba.safetensors
3dependencies/trellis/LICENSE
4dependencies/trellis/SOURCE.json
5dependencies/trellis/ckpts/ss_dec_conv3d_16l8_fp16.json
6dependencies/trellis/ckpts/ss_dec_conv3d_16l8_fp16.safetensors
The embedded decoder is the exact 147,591,972-byte checkpoint from
microsoft/TRELLIS-image-large revision
25e0d31ffbebe4b5a97464dd851910efc3002d96, with SHA-256
1c76d4a40519aa2d711cc263a8404105231ac26db31d946bed48b84fee79009a.
It converts LiTo voxel latents into Gaussian initialization coordinates.
mlx-spatial-lito validate requires this bundle-local decoder and does not
search a separate TRELLIS checkout.
| File | Logical tensors | Bytes |
|---|
image_to_3d/lito_dit_rgba.safetensors | 1,016 | 3,713,563,945 |
tokenizer/lito_new.safetensors | 467 | 521,201,761 |
dependencies/trellis/ckpts/ss_dec_conv3d_16l8_fp16.safetensors | 74 | 147,591,972 |
| Runtime weights | 1,557 | 4,382,357,678 |
Best For
- Apple Silicon MLX inference experiments.
- Image-to-3D Gaussian Splat generation with
mlx-spatial.
- Research workflows that need LiTo weights in safetensors format.
- Local 3DGS inspection in KIRI, Gaussian-splat-aware Blender add-ons, or compatible 3DGS viewers.
Current Limitations
- Research-only, non-commercial license boundary from Apple.
- This is an unofficial converted derivative bundle, not an Apple-hosted official MLX package.
- Current
mlx-spatial LiTo support targets image-to-3D Gaussian Splat inference; it does not provide LiTo training, fine-tuning, mesh extraction, multi-image conditioning, or video conditioning.
- Visual quality depends strongly on input matting and alpha quality. Inputs with broad or noisy alpha masks can produce weaker holes, handles, and fine structures.
- CUDA is not required and is not used by
mlx-spatial LiTo inference.
Conversion Details
The LiTo files were converted from Apple's original .ckpt checkpoints to
safetensors and pruned to the modules read by the main inference path. The
bundle retains the EMA velocity estimator, DINO/RGBA image conditioner,
Gaussian decoder, and voxel decoder. It removes the non-EMA training copy,
duplicate tokenizer, mesh/fpoint/LPIPS modules, and training-only decoders.
Retained tensor values, shapes, and dtypes are unchanged; no retained LiTo
tensor is quantized. The embedded TRELLIS decoder is redistributed unchanged
from the immutable Microsoft source revision recorded above.
Verification
- Both checkpoints and the embedded decoder pass
mlx-spatial-lito validate.
- Architecture inspection recovers 28 DiT blocks, 6 Gaussian Perceiver blocks,
and 4 voxel decoder blocks.
- The bundle exposes 1,483 LiTo logical inference tensors.
- A bundle-local 20-step runtime validation produced 8,192 finite Gaussians
after explicitly capping occupied cells for packaging verification. This is
not an uncapped quality benchmark.
Project Links
Apple LiTo Source and License
This bundle is based on Apple's LiTo research release:
Apple's LiTo model weights are released under the Apple Machine Learning Research Model License Agreement. Use is limited to non-commercial scientific research and academic development activities. Commercial product use is not permitted.
License and source access last checked: 2026-08-06.
This repository is not an Apple release and is not endorsed by Apple. Redistribution of this converted bundle must keep Apple's license terms, attribution notice, and modification disclosure.
Required attribution notice:
Apple Machine Learning Research Model is licensed under the Apple Machine Learning Research Model License Agreement.
The embedded TRELLIS dependency is licensed under the MIT License. Its license
copy and immutable source metadata are included under dependencies/trellis/.
