Create README.md

README.md

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+---
+license: apache-2.0
+---
+---
+license: apache-2.0
+---
+
+# FastVideo Wan2.1-VSA-T2V-14B-720P-Diffusers 
+
+<div>
+  <div align="center">
+    <a href="https://github.com/hao-ai-lab/FastVideo" target="_blank">FastVideo Team</a>&emsp;
+  </div>
+  <div align="center">
+    <a href="https://arxiv.org/pdf/2505.13389">Paper</a> | 
+    <a href="https://github.com/hao-ai-lab/FastVideo">Code</a>
+  </div>
+</div>
+
+
+## Model Overview
+- This model is finetuned with [VSA](https://arxiv.org/pdf/2505.13389), based on [Wan-AI/Wan2.1-T2V-14B-Diffusers](https://huggingface.co/Wan-AI/Wan2.1-T2V-1.3B-Diffusers).  
+- It achieves up to 2.1x speed up on a single **H100** GPU.  
+- Both [finetuning](https://github.com/hao-ai-lab/FastVideo/blob/main/scripts/finetune/finetune_v1_VSA.sh) and [inference](https://github.com/hao-ai-lab/FastVideo/blob/main/scripts/inference/v1_inference_wan_VSA.sh) scripts are available in the [FastVideo](https://github.com/hao-ai-lab/FastVideo) repository.  
+- Try it out on **FastVideo** — we support a wide range of GPUs from **H100** to **4090**
+- We use [FastVideo 720P Synthetic Wan dataset](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x768x1280_250k) for training.
+
+
+
+If you use Wan2.1-VSA-T2V-14B-720P-Diffusers model for your research, please cite our paper:
+```
+@article{zhang2025vsa,
+  title={VSA: Faster Video Diffusion with Trainable Sparse Attention},
+  author={Zhang, Peiyuan and Huang, Haofeng and Chen, Yongqi and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao},
+  journal={arXiv preprint arXiv:2505.13389},
+  year={2025}
+}
+@article{zhang2025fast,
+  title={Fast video generation with sliding tile attention},
+  author={Zhang, Peiyuan and Chen, Yongqi and Su, Runlong and Ding, Hangliang and Stoica, Ion and Liu, Zhengzhong and Zhang, Hao},
+  journal={arXiv preprint arXiv:2502.04507},
+  year={2025}
+}
+```