title: Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model
summary: 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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paper_authors.name: Xuyan Chi
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paper_authors.name: Yanghua Peng
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paper_authors.name: Jiashi Feng
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paper_title: Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model
paper_summary: 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/
paper_ai_summary: 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.
paper_ai_keywords: video generation diffusion model lightweight fine-tuning