Summary of Towards Precise Scaling Laws For Video Diffusion Transformers, by Yuanyang Yin et al.
Towards Precise Scaling Laws for Video Diffusion Transformers
by Yuanyang Yin, Yaqi Zhao, Mingwu Zheng, Ke Lin, Jiarong Ou, Rui Chen, Victor Shea-Jay Huang, Jiahao Wang, Xin Tao, Pengfei Wan, Di Zhang, Baoqun Yin, Wentao Zhang, Kun Gai
First submitted to arxiv on: 25 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel paper investigates the optimal performance of video diffusion transformers within limited data and compute budgets, crucial due to their high training costs. The authors systematically analyze scaling laws for video diffusion transformers and confirm their presence, discovering that these models are more sensitive to learning rate and batch size than language models. To address this, they propose a new scaling law that predicts optimal hyperparameters for any model size and compute budget. Under these optimal settings, the authors achieve comparable performance while reducing inference costs by 40.1% compared to conventional scaling methods within a compute budget of 1e10 TFlops. The study establishes a more generalized and precise relationship among validation loss, any model size, and compute budget, enabling performance prediction for non-optimal model sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Video diffusion transformers are powerful tools for generating videos, but they can be expensive to train. This paper helps us understand how to make the most of these models by finding the right balance between data, compute power, and performance. The authors show that video diffusion models need to be trained with special care because they’re more sensitive to certain settings than language models. By using a new scaling law, they can predict the best settings for any model size and amount of computing power, which is really useful for practical applications. |
Keywords
» Artificial intelligence » Diffusion » Inference » Scaling laws
