Add model card and conversion metadata

LICENSE_MODEL

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+Disclaimer: IMPORTANT: This Apple Machine Learning Research Model is
+specifically developed and released by Apple Inc. ("Apple") for the sole purpose
+of scientific research of artificial intelligence and machine-learning
+technology. “Apple Machine Learning Research Model” means the model, including
+but not limited to algorithms, formulas, trained model weights, parameters,
+configurations, checkpoints, and any related materials (including
+documentation).
+
+This Apple Machine Learning Research Model is provided to You by
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+Machine Learning Research Model constitutes acceptance of this Agreement. If You
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+
+* License Scope: In consideration of your agreement to abide by the following
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+
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+
+* Disclaimer and Limitation of Liability: This Apple Machine Learning Research
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+
+Copyright (C) 2026 Apple Inc. All Rights Reserved.

README.md

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+---
+license: other
+license_name: apple-machine-learning-research-model-license-agreement
+license_link: https://github.com/apple/ml-lito/blob/main/LICENSE_MODEL
+library_name: mlx
+pipeline_tag: image-to-3d
+tags:
+  - mlx
+  - safetensors
+  - image-to-3d
+  - gaussian-splatting
+  - 3dgs
+  - research-only
+  - non-commercial
+base_model:
+  - apple/ml-lito
+---
+
+# LiTo Research MLX Safetensors
+
+Unofficial MLX-compatible safetensors conversion of Apple's LiTo research checkpoints for non-commercial research use with `mlx-spatial`.
+
+This repository is not an Apple release and is not endorsed by Apple. The converted files preserve the released checkpoint tensors and change only the storage format and local layout needed for MLX loading in `mlx-spatial`.
+
+## License
+
+Apple's LiTo model weights are released under the Apple Machine Learning Research Model License Agreement:
+
+https://github.com/apple/ml-lito/blob/main/LICENSE_MODEL
+
+Use is limited to non-commercial scientific research and academic development activities. Commercial product use is not permitted. Redistribution of this converted bundle must keep Apple's license terms, attribution notice, and modification disclosure.
+
+Attribution notice required by the Apple license:
+
+> Apple Machine Learning Research Model is licensed under the Apple Machine Learning Research Model License Agreement.
+
+## Source
+
+- Source project: https://apple.github.io/ml-lito/
+- Source code: https://github.com/apple/ml-lito
+- Source checkpoints: Apple's LiTo checkpoint downloads referenced by the upstream project.
+- Runtime package: `mlx-spatial`
+- `mlx-spatial` source: https://github.com/appautomaton/mlx-spatial
+- `mlx-spatial` PyPI package: https://pypi.org/project/mlx-spatial/
+
+## Conversion
+
+The files in this repository were converted from Apple's original `.ckpt` files to safetensors for local MLX loading. No training, fine-tuning, quantization, pruning, or tensor-value modification was applied.
+
+Expected converted layout:
+
+```text
+tokenizer/lito_new.safetensors
+image_to_3d/lito_dit_rgba.safetensors
+```
+
+Optional conversion metadata may be included under:
+
+```text
+tokenizer/conversion_metadata/lito_new.yaml
+image_to_3d/conversion_metadata/lito_dit_rgba.yaml
+```
+
+These two safetensors are the LiTo-specific checkpoint files required by `mlx-spatial-lito validate`. End-to-end generation in `mlx-spatial` also needs the TRELLIS sparse-structure decoder weights from the separate TRELLIS.2 setup; those are not included in this LiTo bundle.
+
+## Use With mlx-spatial
+
+Install `mlx-spatial`:
+
+```bash
+pip install mlx-spatial
+```
+
+Download this model bundle:
+
+```bash
+hf download appautomaton/lito-research-mlx \
+  --local-dir weights/lito-research-mlx
+```
+
+Validate the local layout:
+
+```bash
+mlx-spatial-lito validate weights/lito-research-mlx
+mlx-spatial-lito inspect weights/lito-research-mlx --limit 10
+```
+
+Generate a Gaussian-splat PLY:
+
+```bash
+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.
+
+## Known Limitations
+
+- Research-only, non-commercial license boundary from Apple.
+- This is a 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.

image_to_3d/conversion_metadata/lito_dit_rgba.yaml

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+source: weights/lito-raw/lito_dit_rgba.ckpt
+converter: torch.load-map-location-cpu
+pt_safe_loader_error: RuntimeError: invalid checkpoint structure: BUILD with non-empty state is not supported
+tensor_count: 2793

tokenizer/conversion_metadata/lito_new.yaml

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+source: weights/lito-raw/lito_new.ckpt
+converter: torch.load-map-location-cpu
+pt_safe_loader_error: RuntimeError: invalid checkpoint structure: BUILD with non-empty state is not supported
+tensor_count: 1108