Upload README.md

README.md

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+---
+license: mit
+pipeline_tag: image-to-3d
+base_model: stabilityai/TripoSR
+tags:
+- lux3d
+- rust
+- candle
+- safetensors
+- canonical-weights
+- 3d
+- image-to-3d
+- mesh-generation
+- triposr
+---
+
+# Lux3D TripoSR Canonical Weights
+
+This repository contains the canonicalized `safetensors` export of the TripoSR checkpoint used by the Lux3D runtime.
+
+## Model Details
+
+- Model family: `triposr`
+- Canonical file: `model.safetensors`
+- Tensor count: `549`
+- Primary runtime target: Lux3D runtime
+- Typical output: mesh export (`.obj`)
+
+## Included Files
+
+- `model.safetensors`
+- `resolved_config.json`
+- `manifest.json`
+- `checksums.json`
+
+## Usage With Lux3D
+
+The Lux3D CLI can validate and use the package once it is installed into your local model asset layout.
+
+```powershell
+cargo run -p lux3d-cli -- inspect --repo-root <runtime-root> triposr
+cargo run -p lux3d-cli -- run --repo-root <runtime-root> triposr --source <input-image> --mc-resolution 256 --mc-threshold 25.0 --output <output-file.obj>
+```
+
+## Provenance
+
+- Upstream source model: [`stabilityai/TripoSR`](https://huggingface.co/stabilityai/TripoSR)
+- Canonicalization flow: `tools/python_baseline/normalize_weights.py`
+
+The exact source checksum set is recorded in `manifest.json`. The integrity of the uploaded package is recorded in `checksums.json`.
+
+## Intended Use
+
+- single-image 3D reconstruction with Lux3D
+- mesh generation workflows using canonical weights
+- reproducible runtime validation
+
+## Limitations
+
+- Verified Lux3D runtime inference currently assumes CUDA.
+- This package includes canonical runtime artifacts only.
+- Texture baking and other extended upstream utilities are outside the scope of this package.
+
+## License
+
+- Upstream model page: [`stabilityai/TripoSR`](https://huggingface.co/stabilityai/TripoSR)
+- License file in this repository: [LICENSE](LICENSE)
+
+## Citation
+
+```bibtex
+@article{tripoSR2024,
+  title={TripoSR: Fast 3D Object Reconstruction from a Single Image},
+  author={Tochilkin, Dmitry and Pankratz, David and Liu, Zexiang and Huang, Zixuan and Letts, Adam and Li, Yangguang and Liang, Ding and Laforte, Christian and Jampani, Varun and Cao, Yan-Pei},
+  journal={arXiv preprint arXiv:2403.02151},
+  year={2024}
+}
+```