Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model

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 atthis https URL

@article{seawead2025_2504.08685,
  title={ Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model },
  author={ Team Seawead and Ceyuan Yang and Zhijie Lin and Yang Zhao and Shanchuan Lin and Zhibei Ma and Haoyuan Guo and Hao Chen and Lu Qi and Sen Wang and Feng Cheng and Feilong Zuo and Xuejiao Zeng and Ziyan Yang and Fangyuan Kong and Meng Wei and Zhiwu Qing and Fei Xiao and Tuyen Hoang and Siyu Zhang and Peihao Zhu and Qi Zhao and Jiangqiao Yan and Liangke Gui and Sheng Bi and Jiashi Li and Yuxi Ren and Rui Wang and Huixia Li and Xuefeng Xiao and Shu Liu and Feng Ling and Heng Zhang and Houmin Wei and Huafeng Kuang and Jerry Duncan and Junda Zhang and Junru Zheng and Li Sun and Manlin Zhang and Renfei Sun and Xiaobin Zhuang and Xiaojie Li and Xin Xia and Xuyan Chi and Yanghua Peng and Yuping Wang and Yuxuan Wang and Zhongkai Zhao and Zhuo Chen and Zuquan Song and Zhenheng Yang and Jiashi Feng and Jianchao Yang and Lu Jiang },
  journal={arXiv preprint arXiv:2504.08685},
  year={ 2025 }
}