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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.08685 |
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| _version_ | 1866909600628867072 |
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| author | Seawead, Team Yang, Ceyuan Lin, Zhijie Zhao, Yang Lin, Shanchuan Ma, Zhibei Guo, Haoyuan Chen, Hao Qi, Lu Wang, Sen Cheng, Feng Zuo, Feilong Zeng, Xuejiao Yang, Ziyan Kong, Fangyuan Wei, Meng Qing, Zhiwu Xiao, Fei Hoang, Tuyen Zhang, Siyu Zhu, Peihao Zhao, Qi Yan, Jiangqiao Gui, Liangke Bi, Sheng Li, Jiashi Ren, Yuxi Wang, Rui Li, Huixia Xiao, Xuefeng Liu, Shu Ling, Feng Zhang, Heng Wei, Houmin Kuang, Huafeng Duncan, Jerry Zhang, Junda Zheng, Junru Sun, Li Zhang, Manlin Sun, Renfei Zhuang, Xiaobin Li, Xiaojie Xia, Xin Chi, Xuyan Peng, Yanghua Wang, Yuping Wang, Yuxuan Zhao, Zhongkai Chen, Zhuo Song, Zuquan Yang, Zhenheng Feng, Jiashi Yang, Jianchao Jiang, Lu |
| author_facet | Seawead, Team Yang, Ceyuan Lin, Zhijie Zhao, Yang Lin, Shanchuan Ma, Zhibei Guo, Haoyuan Chen, Hao Qi, Lu Wang, Sen Cheng, Feng Zuo, Feilong Zeng, Xuejiao Yang, Ziyan Kong, Fangyuan Wei, Meng Qing, Zhiwu Xiao, Fei Hoang, Tuyen Zhang, Siyu Zhu, Peihao Zhao, Qi Yan, Jiangqiao Gui, Liangke Bi, Sheng Li, Jiashi Ren, Yuxi Wang, Rui Li, Huixia Xiao, Xuefeng Liu, Shu Ling, Feng Zhang, Heng Wei, Houmin Kuang, Huafeng Duncan, Jerry Zhang, Junda Zheng, Junru Sun, Li Zhang, Manlin Sun, Renfei Zhuang, Xiaobin Li, Xiaojie Xia, Xin Chi, Xuyan Peng, Yanghua Wang, Yuping Wang, Yuxuan Zhao, Zhongkai Chen, Zhuo Song, Zuquan Yang, Zhenheng Feng, Jiashi Yang, Jianchao Jiang, Lu |
| contents | This technical report presents a cost-efficient strategy for training a video generation foundation model. We present a mid-sized research model with approximately 7 billion parameters (7B) called Seaweed-7B trained from scratch using 665,000 H100 GPU hours. Despite being trained with moderate computational resources, Seaweed-7B demonstrates highly competitive performance compared to contemporary video generation models of much larger size. Design choices are especially crucial in a resource-constrained setting. This technical report highlights the key design decisions that enhance the performance of the medium-sized diffusion model. Empirically, we make two observations: (1) Seaweed-7B achieves performance comparable to, or even surpasses, larger models trained on substantially greater GPU resources, and (2) our model, which exhibits strong generalization ability, can be effectively adapted across a wide range of downstream applications either by lightweight fine-tuning or continue training. See the project page at https://seaweed.video/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08685 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model Seawead, Team Yang, Ceyuan Lin, Zhijie Zhao, Yang Lin, Shanchuan Ma, Zhibei Guo, Haoyuan Chen, Hao Qi, Lu Wang, Sen Cheng, Feng Zuo, Feilong Zeng, Xuejiao Yang, Ziyan Kong, Fangyuan Wei, Meng Qing, Zhiwu Xiao, Fei Hoang, Tuyen Zhang, Siyu Zhu, Peihao Zhao, Qi Yan, Jiangqiao Gui, Liangke Bi, Sheng Li, Jiashi Ren, Yuxi Wang, Rui Li, Huixia Xiao, Xuefeng Liu, Shu Ling, Feng Zhang, Heng Wei, Houmin Kuang, Huafeng Duncan, Jerry Zhang, Junda Zheng, Junru Sun, Li Zhang, Manlin Sun, Renfei Zhuang, Xiaobin Li, Xiaojie Xia, Xin Chi, Xuyan Peng, Yanghua Wang, Yuping Wang, Yuxuan Zhao, Zhongkai Chen, Zhuo Song, Zuquan Yang, Zhenheng Feng, Jiashi Yang, Jianchao Jiang, Lu Computer Vision and Pattern Recognition Artificial Intelligence This technical report presents a cost-efficient strategy for training a video generation foundation model. We present a mid-sized research model with approximately 7 billion parameters (7B) called Seaweed-7B trained from scratch using 665,000 H100 GPU hours. Despite being trained with moderate computational resources, Seaweed-7B demonstrates highly competitive performance compared to contemporary video generation models of much larger size. Design choices are especially crucial in a resource-constrained setting. This technical report highlights the key design decisions that enhance the performance of the medium-sized diffusion model. Empirically, we make two observations: (1) Seaweed-7B achieves performance comparable to, or even surpasses, larger models trained on substantially greater GPU resources, and (2) our model, which exhibits strong generalization ability, can be effectively adapted across a wide range of downstream applications either by lightweight fine-tuning or continue training. See the project page at https://seaweed.video/ |
| title | Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08685 |