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license: apache-2.0 |
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license: apache-2.0 |
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--- |
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# FastVideo FastWan2.1-T2V-14B-480P-Diffusers |
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<p align="center"> |
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<img src="https://raw.githubusercontent.com/hao-ai-lab/FastVideo/main/assets/logo.jpg" width="200"/> |
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</p> |
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<div> |
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<div align="center"> |
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<a href="https://github.com/hao-ai-lab/FastVideo" target="_blank">FastVideo Team</a>  |
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</div> |
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<div align="center"> |
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<a href="https://arxiv.org/pdf/2505.13389">Paper</a> | |
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<a href="https://github.com/hao-ai-lab/FastVideo">Code</a> |
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</div> |
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</div> |
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## Model Overview |
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- This model is jointly finetuned with [DMD](https://arxiv.org/pdf/2405.14867) and [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). |
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- It supports 3-step inference and achieves up to 50x speed up. |
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- Both [finetuning](https://github.com/hao-ai-lab/FastVideo/blob/main/scripts/distill/v1_distill_dmd_wan_VSA.sh) and [inference](https://github.com/hao-ai-lab/FastVideo/blob/main/scripts/inference/v1_inference_wan_dmd.sh) scripts are available in the [FastVideo](https://github.com/hao-ai-lab/FastVideo) repository. |
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- Try it out on **FastVideo** — we support a wide range of GPUs from **H100** to **4090**, and even support **Mac** users! |
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- We use [FastVideo 480P Synthetic Wan dataset](https://huggingface.co/datasets/FastVideo/Wan-Syn_77x448x832_600k) for training. |
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If you use FastWan2.1-T2V-14B-480P-Diffusers model for your research, please cite our paper: |
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``` |
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@article{zhang2025vsa, |
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title={VSA: Faster Video Diffusion with Trainable Sparse Attention}, |
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author={Zhang, Peiyuan and Huang, Haofeng and Chen, Yongqi and Lin, Will and Liu, Zhengzhong and Stoica, Ion and Xing, Eric and Zhang, Hao}, |
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journal={arXiv preprint arXiv:2505.13389}, |
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year={2025} |
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} |
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@article{zhang2025fast, |
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title={Fast video generation with sliding tile attention}, |
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author={Zhang, Peiyuan and Chen, Yongqi and Su, Runlong and Ding, Hangliang and Stoica, Ion and Liu, Zhengzhong and Zhang, Hao}, |
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journal={arXiv preprint arXiv:2502.04507}, |
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year={2025} |
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} |
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``` |
