appautomaton/lito-research-mlx

LiTo Research MLX 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 bundle is for researchers who want a practical Mac-native LiTo inference path: download the weights, point mlx-spatial-lito at them, 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:

pip install mlx-spatial

Download this model bundle:

hf download appautomaton/lito-research-mlx \
  --local-dir weights/lito-research-mlx

Validate the local layout:

mlx-spatial-lito validate weights/lito-research-mlx
mlx-spatial-lito inspect weights/lito-research-mlx --limit 10

Generate a Gaussian-splat PLY:

mlx-spatial-lito generate inputs/lito/sample.png \
  --weights-root weights/lito-research-mlx \
  --output outputs/lito/sample.ply \
  --memory-profile safe \
  --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 contains the LiTo-specific safetensors expected by mlx-spatial:

tokenizer/lito_new.safetensors
image_to_3d/lito_dit_rgba.safetensors

It also includes lightweight conversion metadata:

tokenizer/conversion_metadata/lito_new.yaml
image_to_3d/conversion_metadata/lito_dit_rgba.yaml

End-to-end LiTo generation in mlx-spatial also needs the TRELLIS sparse-structure decoder weights from the separate TRELLIS.2 setup. Those TRELLIS.2 weights are not included in this LiTo bundle.

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 files in this repository were converted from Apple's original .ckpt checkpoints to safetensors for local MLX loading. The conversion changes storage format and local layout only.

No training, fine-tuning, quantization, pruning, or tensor-value modification was applied.

Project Links

• Runtime package: mlx-spatial
• mlx-spatial PyPI package: https://pypi.org/project/mlx-spatial/
• mlx-spatial source: https://github.com/appautomaton/mlx-spatial
• This model repo: https://huggingface.co/appautomaton/lito-research-mlx

Apple LiTo Source and License

This bundle is based on Apple's LiTo research release:

• Apple LiTo project: https://apple.github.io/ml-lito/
• Apple LiTo source code: https://github.com/apple/ml-lito
• Apple model license: https://github.com/apple/ml-lito/blob/main/LICENSE_MODEL

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.

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.