Summary of Accelerating Diffusion Models with One-to-many Knowledge Distillation, by Linfeng Zhang et al.
Accelerating Diffusion Models with One-to-Many Knowledge Distillation
by Linfeng Zhang, Kaisheng Ma
First submitted to arxiv on: 5 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 This paper addresses the computational overhead challenge faced by diffusion models, which are renowned for generating high-quality images. Despite their advancements, these models struggle with real-time generation due to the substantial computational costs. To alleviate this issue, researchers have proposed various methods to accelerate diffusion models, such as improved sampling techniques or step distillation. However, there remains a knowledge gap in reducing the computational cost for each timestep. The authors introduce one-to-many knowledge distillation (O2MKD), a novel approach that distills a single teacher diffusion model into multiple student diffusion models, each trained to learn the teacher’s knowledge for a subset of continuous timesteps. Experimental results on various datasets demonstrate that O2MKD can be applied to previous knowledge distillation and fast sampling methods to achieve significant acceleration. The authors’ contribution has far-reaching implications for real-time image generation applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper talks about a type of computer program called “diffusion models” that creates realistic images. These programs are good at making pictures, but they use a lot of computer power and can’t make new images quickly enough. To solve this problem, the researchers came up with a new way to teach these programs how to work faster. They call it one-to-many knowledge distillation (O2MKD). It works by teaching many small programs to learn from a single good program. This helps the small programs learn faster and make images more quickly. The authors tested their idea on some big datasets and showed that it can really speed up image generation. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Distillation » Image generation » Knowledge distillation
