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README.md

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 [Code Repository](https://github.com/Yaofang-Liu/Pusa-VidGen) | [Project Page](https://yaofang-liu.github.io/Pusa_Web/) | [Dataset](https://huggingface.co/datasets/RaphaelLiu/PusaV1_training) | [Wan2.1 Model](https://huggingface.co/RaphaelLiu/PusaV1) | [Paper (Pusa V1.0)](https://arxiv.org/abs/2507.16116) | [Paper (FVDM)](https://arxiv.org/abs/2410.03160) | [Follow on X](https://x.com/stephenajason) | [Xiaohongshu](https://www.xiaohongshu.com/user/profile/5c6f928f0000000010015ca1?xsec_token=YBEf_x-s5bOBQIMJuNQvJ6H23Anwey1nnDgC9wiLyDHPU=&xsec_source=app_share&xhsshare=CopyLink&appuid=5c6f928f0000000010015ca1&apptime=1752622393&share_id=60f9a8041f974cb7ac5e3f0f161bf748)
 
-### Overview
 **Pusa Wan2.2 V1.0** extends the groundbreaking Pusa paradigm to the advanced **Wan2.2-T2V-A14B** architecture, featuring a **MoE DiT design** with separate high-noise and low-noise models. This architecture provides enhanced quality control and generation capabilities while maintaining the revolutionary **vectorized timestep adaptation (VTA)** approach.
 
-### ✨ Key Features
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 **Various tasks in one model, all support 4-step inference with LightX2V**:
   Image-to-Video, Start-End Frames, Video Completion, Video Extension, Text-to-Video, Video Transition, and more...