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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.09642 |
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| _version_ | 1866911477064007680 |
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| author | Zheng, Zangwei Peng, Xiangyu Lou, Yuxuan Shen, Chenhui Young, Tom Guo, Xinying Wang, Binluo Xu, Hang Liu, Hongxin Jiang, Mingyan Li, Wenjun Wang, Yuhui Ye, Anbang Ren, Gang Ma, Qianran Liang, Wanying Lian, Xiang Wu, Xiwen Zhong, Yuting Li, Zhuangyan Gong, Chaoyu Lei, Guojun Cheng, Leijun Zhang, Limin Li, Minghao Zhang, Ruijie Hu, Silan Huang, Shijie Wang, Xiaokang Zhao, Yuanheng Wang, Yuqi Wei, Ziang You, Yang |
| author_facet | Zheng, Zangwei Peng, Xiangyu Lou, Yuxuan Shen, Chenhui Young, Tom Guo, Xinying Wang, Binluo Xu, Hang Liu, Hongxin Jiang, Mingyan Li, Wenjun Wang, Yuhui Ye, Anbang Ren, Gang Ma, Qianran Liang, Wanying Lian, Xiang Wu, Xiwen Zhong, Yuting Li, Zhuangyan Gong, Chaoyu Lei, Guojun Cheng, Leijun Zhang, Limin Li, Minghao Zhang, Ruijie Hu, Silan Huang, Shijie Wang, Xiaokang Zhao, Yuanheng Wang, Yuqi Wei, Ziang You, Yang |
| contents | Video generation models have achieved remarkable progress in the past year. The quality of AI video continues to improve, but at the cost of larger model size, increased data quantity, and greater demand for training compute. In this report, we present Open-Sora 2.0, a commercial-level video generation model trained for only $200k. With this model, we demonstrate that the cost of training a top-performing video generation model is highly controllable. We detail all techniques that contribute to this efficiency breakthrough, including data curation, model architecture, training strategy, and system optimization. According to human evaluation results and VBench scores, Open-Sora 2.0 is comparable to global leading video generation models including the open-source HunyuanVideo and the closed-source Runway Gen-3 Alpha. By making Open-Sora 2.0 fully open-source, we aim to democratize access to advanced video generation technology, fostering broader innovation and creativity in content creation. All resources are publicly available at: https://github.com/hpcaitech/Open-Sora. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_09642 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k Zheng, Zangwei Peng, Xiangyu Lou, Yuxuan Shen, Chenhui Young, Tom Guo, Xinying Wang, Binluo Xu, Hang Liu, Hongxin Jiang, Mingyan Li, Wenjun Wang, Yuhui Ye, Anbang Ren, Gang Ma, Qianran Liang, Wanying Lian, Xiang Wu, Xiwen Zhong, Yuting Li, Zhuangyan Gong, Chaoyu Lei, Guojun Cheng, Leijun Zhang, Limin Li, Minghao Zhang, Ruijie Hu, Silan Huang, Shijie Wang, Xiaokang Zhao, Yuanheng Wang, Yuqi Wei, Ziang You, Yang Graphics Artificial Intelligence Video generation models have achieved remarkable progress in the past year. The quality of AI video continues to improve, but at the cost of larger model size, increased data quantity, and greater demand for training compute. In this report, we present Open-Sora 2.0, a commercial-level video generation model trained for only $200k. With this model, we demonstrate that the cost of training a top-performing video generation model is highly controllable. We detail all techniques that contribute to this efficiency breakthrough, including data curation, model architecture, training strategy, and system optimization. According to human evaluation results and VBench scores, Open-Sora 2.0 is comparable to global leading video generation models including the open-source HunyuanVideo and the closed-source Runway Gen-3 Alpha. By making Open-Sora 2.0 fully open-source, we aim to democratize access to advanced video generation technology, fostering broader innovation and creativity in content creation. All resources are publicly available at: https://github.com/hpcaitech/Open-Sora. |
| title | Open-Sora 2.0: Training a Commercial-Level Video Generation Model in $200k |
| topic | Graphics Artificial Intelligence |
| url | https://arxiv.org/abs/2503.09642 |