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/

本研究针对视频生成模型的高成本训练问题，提出了一种具有成本效益的训练策略，开发了一个名为海藻-7B的中型模型，拥有约70亿个参数。尽管计算资源有限，海藻-7B的性能与更大规模的模型相媲美，表明其在资源受限环境下的设计选择至关重要。

海藻-7B：视频生成基础模型的成本有效训练