Summary of Lazydit: Lazy Learning For the Acceleration Of Diffusion Transformers, by Xuan Shen et al.
LazyDiT: Lazy Learning for the Acceleration of Diffusion Transformers
by Xuan Shen, Zhao Song, Yufa Zhou, Bo Chen, Yanyu Li, Yifan Gong, Kai Zhang, Hao Tan, Jason Kuen, Henghui Ding, Zhihao Shu, Wei Niu, Pu Zhao, Yanzhi Wang, Jiuxiang Gu
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper introduces a new framework for efficiently training and deploying Diffusion Transformers, which have shown impressive results in generative tasks. The authors propose LazyDiT, a lazy learning approach that reuses cached results from previous steps to skip redundant computations. This method leverages linear approximations of the similarity between consecutive outputs to reduce computational costs. Experimental results demonstrate that LazyDiT outperforms DDIM samplers across various transformer models and resolutions. Furthermore, the authors successfully implement their method on mobile devices, achieving better performance with similar latency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making machines learn faster and more efficiently. It’s like when you’re doing a big math problem and you remember that you did something similar earlier, so you can just copy it instead of redoing everything. The authors call this “lazy learning” and they use it to make a special kind of computer model called a Diffusion Transformer work better. They tested their idea and found that it really works! It even works well on small devices like smartphones. |
Keywords
» Artificial intelligence » Diffusion » Transformer
