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
Published: 2025-04-11 16:46:20

Abstract: 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/