Wan: Open and Advanced Large-Scale Video Generative Models

WanTeam, Ang Wang, Baole Ai, Bin Wen, chaojie mao, Chenwei Xie, Di Chen, Feiwu Yu, Haiming Zhao, Jianxiao Yang, jianyuan.zengjy, Jiayu Wang, Jingfeng Zhang, Zhou, Jinkai Wang, Jixuan Chen, Kai Zhu, Kang Zhao, Keyu Yan, Lianghua Huang, Mengyang Feng, Zhang Ningyi, Pandeng Li, Wupingyu, Ruihang Chu, Ruili, Shiwei Zhang, Siyang Sun, Tao Fang, Tianxing Wang, Tianyi Gui, Tingyu Weng, Tong Shen, Wei Lin, Wei Wang, Wei Wang, wenmeng zhou, wente.wang, Wenting Shen, Wenyuan Yu, Xianzhong Shi, xiaominghuang, Xin Xu, Yan Kou, Yangyu Lv, Yifei Li, yijing liu, Yiming Wang, Yingya Zhang, Yitong Huang, Yong Li, You Wu, Yu Liu, Yulin Pan, Yun Zheng, Yuntao Hong, Yupeng Shi, Yutong Feng, jiangzeyinzi, Zhen Han, Zhi-Fan Wu, Ziyu Liu

Wan: Open and Advanced Large-Scale Video Generative Models: 72 upvotes on Hugging Face Daily Papers, #3 of 27 papers on 2025-03-27. Day-by-day upvote history.

This report presents Wan, a comprehensive and open suite of video foundation models designed to push the boundaries of video generation. Built upon the mainstream diffusion transformer paradigm, Wan achieves significant advancements in generative capabilities through a series of innovations, including our novel VAE, scalable pre-training strategies, large-scale data curation, and automated evaluation metrics. These contributions collectively enhance the model's performance and versatility. Specifically, Wan is characterized by four key features: Leading Performance: The 14B model of Wan, trained on a vast dataset comprising billions of images and videos, demonstrates the scaling laws of video generation with respect to both data and model size. It consistently outperforms the existing open-source models as well as state-of-the-art commercial solutions across multiple internal and external benchmarks, demonstrating a clear and significant performance superiority. Comprehensiveness: Wan offers two capable models, i.e., 1.3B and 14B parameters, for efficiency and effectiveness respectively. It also covers multiple downstream applications, including image-to-video, instruction-guided video editing, and personal video generation, encompassing up to eight tasks. Consumer-Grade Efficiency: The 1.3B model demonstrates exceptional resource efficiency, requiring only 8.19 GB VRAM, making it compatible with a wide range of consumer-grade GPUs. Openness: We open-source the entire series of Wan, including source code and all models, with the goal of fostering the growth of the video generation community. This openness seeks to significantly expand the creative possibilities of video production in the industry and provide academia with high-quality video foundation models. All the code and models are available at https://github.com/Wan-Video/Wan2.1.

Paper page on Hugging Face · arXiv

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