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 at https://seaweed.video/
67fc6ffc59b22e7c34d64c2e
Team Seawead
67fc6ffc59b22e7c34d64c2f
Ceyuan Yang
67fc6ffc59b22e7c34d64c30
Zhijie Lin
admin_assigned
2025-04-14T13:17:03.433Z
64415957bd0c9726529802f6
/avatars/1132d1ee68fb58ec635d57c8175caacd.svg
Zhijie Lin
user
Ikuinen
67fc6ffc59b22e7c34d64c31
Yang Zhao
67fc6ffc59b22e7c34d64c32
Shanchuan Lin
67fc6ffc59b22e7c34d64c33
Zhibei Ma
admin_assigned
2025-04-14T13:19:16.342Z
645d5a44680734460f9e7742
/avatars/66687e2c413acce55436d967071f8786.svg
Zhibei Ma
user
Brightmzb
67fc6ffc59b22e7c34d64c34
Haoyuan Guo
67fc6ffc59b22e7c34d64c35
Hao Chen
67fc6ffc59b22e7c34d64c36
Lu Qi
67fc6ffc59b22e7c34d64c37
Sen Wang
67fc6ffc59b22e7c34d64c38
Feng Cheng
claimed_verified
2025-04-15T07:55:33.922Z
6617eb10c1167c8d87ca818d
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/n1Mr3l8btaMM18Hao99o0.png
Feng Cheng
user
klauscc
67fc6ffc59b22e7c34d64c39
Feilong Zuo Xuejiao Zeng
67fc6ffc59b22e7c34d64c3a
Ziyan Yang
admin_assigned
2025-04-14T13:20:01.373Z
63c98de85fdc575773c52098
/avatars/b66675e781695daed7c00a7c802c91f5.svg
Ziyan Yang
user
ziyany
67fc6ffc59b22e7c34d64c3b
Fangyuan Kong
67fc6ffc59b22e7c34d64c3c
Zhiwu Qing
67fc6ffc59b22e7c34d64c3d
Fei Xiao
67fc6ffc59b22e7c34d64c3e
Meng Wei
67fc6ffc59b22e7c34d64c3f
Tuyen Hoang
67fc6ffc59b22e7c34d64c40
Siyu Zhang
67fc6ffc59b22e7c34d64c41
Peihao Zhu
67fc6ffc59b22e7c34d64c42
Qi Zhao
67fc6ffc59b22e7c34d64c43
Jiangqiao Yan
67fc6ffc59b22e7c34d64c44
Liangke Gui
67fc6ffc59b22e7c34d64c45
Sheng Bi
67fc6ffc59b22e7c34d64c46
Jiashi Li
67fc6ffc59b22e7c34d64c47
Yuxi Ren
67fc6ffc59b22e7c34d64c48
Rui Wang
67fc6ffc59b22e7c34d64c49
Huixia Li
claimed_verified
2025-05-26T08:41:11.037Z
6368b457e943416fc80619fb
https://cdn-avatars.huggingface.co/v1/production/uploads/6368b457e943416fc80619fb/RjAJKMwl6aIFKAZs1vgvK.jpeg
HXLee
user
HXLee
67fc6ffc59b22e7c34d64c4a
Xuefeng Xiao
67fc6ffc59b22e7c34d64c4b
Shu Liu
67fc6ffc59b22e7c34d64c4c
Feng Ling
claimed_verified
2025-04-14T09:46:31.964Z
636a4e4fa55bbbdf8c877667
/avatars/efdb68c56a4a44fdac52750c07a6cc35.svg
Ling Feng
user
lingff
67fc6ffc59b22e7c34d64c4d
Heng Zhang
67fc6ffc59b22e7c34d64c4e
Houmin Wei
67fc6ffc59b22e7c34d64c4f
Huafeng Kuang
67fc6ffc59b22e7c34d64c50
Jerry Duncan
67fc6ffc59b22e7c34d64c51
Junda Zhang
67fc6ffc59b22e7c34d64c52
Junru Zheng
67fc6ffc59b22e7c34d64c53
Li Sun
67fc6ffc59b22e7c34d64c54
Manlin Zhang
67fc6ffc59b22e7c34d64c55
Renfei Sun
67fc6ffc59b22e7c34d64c56
Xiaobin Zhuang
claimed_verified
2025-04-20T15:03:53.471Z
63774ca43a63a2983ffc12f9
https://cdn-avatars.huggingface.co/v1/production/uploads/63774ca43a63a2983ffc12f9/jUJasGf5_2rwiEun_eYl7.png
xiaobin zhuang
user
xiaobinzhuang
67fc6ffc59b22e7c34d64c57
Xiaojie Li
67fc6ffc59b22e7c34d64c58
Xin Xia
67fc6ffc59b22e7c34d64c59
Xuyan Chi
67fc6ffc59b22e7c34d64c5a
Yanghua Peng
67fc6ffc59b22e7c34d64c5b
Yuping Wang
67fc6ffc59b22e7c34d64c5c
Yuxuan Wang
67fc6ffc59b22e7c34d64c5d
Zhongkai Zhao
67fc6ffc59b22e7c34d64c5e
Zhuo Chen
67fc6ffc59b22e7c34d64c5f
Zuquan Song
67fc6ffc59b22e7c34d64c60
Zhenheng Yang
claimed_verified
2025-04-14T09:46:34.053Z
6421183b69a2c2933882d652
/avatars/66813a8fa22915087cccd4dbfb945ca7.svg
Zhenheng Yang
user
zhenheny
67fc6ffc59b22e7c34d64c61
Jiashi Feng
67fc6ffc59b22e7c34d64c62
Jianchao Yang
67fc6ffc59b22e7c34d64c63
Lu Jiang
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 at https://seaweed.video/
A mid-sized video generation model with 7 billion parameters, trained efficiently with substantial GPU hours, achieves competitive performance and strong generalization across various applications.
video generation diffusion model lightweight fine-tuning