Summary of Learning-to-cache: Accelerating Diffusion Transformer Via Layer Caching, by Xinyin Ma et al.
Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching
by Xinyin Ma, Gongfan Fang, Michael Bi Mi, Xinchao Wang
First submitted to arxiv on: 3 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 This study explores the applicability of diffusion transformers for various generative tasks, demonstrating impressive capabilities. However, these models come with a significant cost: slow inference due to the need for large-scale parameter computations. The authors propose a novel scheme called Learning-to-Cache (L2C), which learns to dynamically cache and remove redundant computations in diffusion transformer layers, achieving up to 93.68% computation reduction without compromising performance. L2C leverages the identical layer structure and sequential nature of diffusion transformers to identify redundant computations between timesteps. The authors also introduce a differentiable optimization objective to address the vast search space in deep models. Experimental results show that L2C outperforms existing samplers and cache-based methods at equivalent inference speeds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how to make big machines (computers) work faster by getting rid of some parts they don’t need. These “parts” are called layers, and they’re like building blocks for making predictions. The researchers found that most of these layers can be ignored without losing accuracy, which means the computer can do things faster! They developed a new way to figure out which layers to skip, using something called Learning-to-Cache (L2C). This helps the computer make decisions on its own about what it needs and doesn’t need. The results are impressive, showing that L2C is better than other methods at doing tasks quickly. |
Keywords
» Artificial intelligence » Diffusion » Inference » Optimization » Transformer
