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README.md

@@ -7,7 +7,7 @@ sdk: gradio
 sdk_version: 6.1.0
 app_file: app.py
 pinned: false
-license: mit
+license: other
 short_description: Multi-view image to 3D generation
 ---
 
@@ -18,69 +18,6 @@ This fork extends [TRELLIS.2](https://github.com/microsoft/TRELLIS.2) with multi
 ## What's New
 
 - **Multi-image conditioning**: Use multiple views for better 3D reconstruction
-- **Windows support**: Runs on Windows with automatic `sdpa` attention fallback
+- **Windows support**: Runs on Windows with automatic sdpa attention fallback
 
 > For the interactive visualization tool, see the [viser_view branch](https://github.com/OpsiClear/Trellis2_multi_image_conditioning/tree/viser_view).
-
-## Installation
-
-```sh
-git clone https://github.com/OpsiClear/Trellis2_multi_image_conditioning.git --recursive
-cd Trellis2_multi_image_conditioning
-. ./setup.sh --new-env --basic --flash-attn --nvdiffrast --nvdiffrec --cumesh --o-voxel --flexgemm
-```
-
-> On Windows, `flash-attn` is unavailable. The code automatically falls back to PyTorch's native `sdpa` backend.
-
-## Usage
-
-### Multi-Image Generation
-
-```python
-from trellis2.pipelines import Trellis2ImageTo3DPipeline
-from PIL import Image
-
-pipeline = Trellis2ImageTo3DPipeline.from_pretrained("microsoft/TRELLIS.2-4B")
-pipeline.cuda()
-
-# Load multiple views
-images = [Image.open(f"view_{i}.png") for i in range(4)]
-
-# Generate with multi-image conditioning
-mesh = pipeline.run_multi_image(images)[0]
-```
-
-Or run the example:
-```sh
-python example_multi_image.py
-```
-
-### Other Examples
-
-```sh
-python example.py           # Single image generation
-python app.py               # Gradio web demo
-python example_texturing.py # PBR texture generation
-python app_texturing.py     # Texture generation web demo
-```
-
-## Attribution
-
-This project is a fork of [TRELLIS.2](https://github.com/microsoft/TRELLIS.2) by Microsoft Corporation, originally released under the MIT License.
-
-For full documentation, training instructions, and model details, see the original repository.
-
-If you use this code, please cite the original paper:
-
-```bibtex
-@article{xiang2025trellis2,
-    title={Native and Compact Structured Latents for 3D Generation},
-    author={Xiang, Jianfeng and Chen, Xiaoxue and Xu, Sicheng and Wang, Ruicheng and Lv, Zelong and Deng, Yu and Zhu, Hongyuan and Dong, Yue and Zhao, Hao and Yuan, Nicholas Jing and Yang, Jiaolong},
-    journal={Tech report},
-    year={2025}
-}
-```
-
-## License
-
-The original TRELLIS.2 code is MIT licensed by Microsoft Corporation. New additions in this fork (multi-image conditioning) are licensed under [AGPL-3.0](LICENSE).