| --- |
| license: mit |
| base_model: |
| - Wan-AI/Wan2.1-T2V-14B-Diffusers |
| - hunyuanvideo-community/HunyuanVideo |
| pipeline_tag: text-to-video |
| library_name: diffusers |
| --- |
| |
| # VORTA: Efficient Video Diffusion via Routing Sparse Attention |
|
|
| > TL;DR - VORTA accelerates video diffusion transformers by sparse attention and dynamic routing, achieving speedup with negligible quality loss. |
|
|
|
|
| ## Quick Start |
|
|
| 1. Download the checkpoints into the `./results` directory under the VORTA GitHub code repository. |
| ```bash |
| git lfs install |
| git clone git@hf.co:anonymous728/VORTA |
| # mv VORTA/<model_name> results/, <model_name>: wan-14B, hunyuan; e.g. |
| mv VORTA/wan-14B results/ |
| ``` |
| _Other alternative methods to download the models can be found [here](https://huggingface.co/docs/hub/models-downloading#using-git)._ |
|
|
|
|
| 2. Follow the `README.md` instructions to run the sampling with speedup. 🤗 |
