Seaweed-7B：视频生成基础模型的高效低成本训练

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

April 11, 2025

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

cs.AI

摘要

本技术报告提出了一种经济高效的视频生成基础模型训练策略。我们介绍了一个中等规模的研究模型，名为Seaweed-7B，拥有约70亿参数（7B），从零开始训练共消耗了665,000小时的H100 GPU算力。尽管在计算资源有限的情况下进行训练，Seaweed-7B相较于规模大得多的当代视频生成模型，展现出了极具竞争力的性能。在资源受限的环境中，设计选择尤为关键。本报告着重阐述了提升中等规模扩散模型性能的关键设计决策。通过实证研究，我们得出两点观察：(1) Seaweed-7B在性能上可与甚至超越那些消耗了更多GPU资源训练的大型模型相媲美；(2) 我们的模型展现出强大的泛化能力，能够通过轻量级微调或持续训练，有效适应广泛的下游应用场景。更多详情请访问项目页面：https://seaweed.video/。

English

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/