Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model
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
2025-04-14
Summary
This paper talks about Seaweed-7B, a video generation AI model that is about medium-sized compared to others, but is trained in a way that saves a lot of money and computer resources. Even though it's not the biggest model out there, it still does a great job creating videos and can handle lots of different tasks.
What's the problem?
The problem is that most advanced video generation models are huge and require tons of expensive computer power to train, which makes them hard for most people or companies to use. This means only big tech companies can afford to create and use these powerful models, limiting who can work with cutting-edge video AI.
What's the solution?
The researchers built Seaweed-7B, which has 7 billion parameters—making it much smaller than the biggest models—and they trained it using methods that require fewer hours on powerful GPUs. This makes the model much cheaper and more efficient to train, while still keeping its ability to create high-quality videos and adapt to different situations.
Why it matters?
This work matters because it shows that you don't need a massive, super expensive model to get strong results in video generation. Seaweed-7B opens the door for more people, schools, and smaller companies to use advanced video AI, making the technology more accessible and encouraging more creativity and innovation.
Abstract
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.