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

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/