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Main Authors: 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
Format: Preprint
Published: 2025
Subjects:
Computer Vision and Pattern Recognition
Artificial Intelligence
Online Access:https://arxiv.org/abs/2504.08685
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Table of 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/

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