Summary of Multi-student Diffusion Distillation For Better One-step Generators, by Yanke Song et al.
Multi-student Diffusion Distillation for Better One-step Generators
by Yanke Song, Jonathan Lorraine, Weili Nie, Karsten Kreis, James Lucas
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces Multi-Student Distillation (MSD), a framework to distill conditional teacher diffusion models into multiple single-step generators, each responsible for a subset of the conditioning data. This approach allows for higher generation quality at smaller sizes and faster inference, making it suitable for computationally heavy applications. MSD trains multiple distilled students using distribution matching and adversarial distillation techniques, achieving competitive results with faster inference for single-step generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion models can create high-quality images, but they need many steps to do so. To make them work faster, scientists created a way to “distill” the knowledge of one big model into smaller ones. These small models can then generate images quickly. The problem is that these small models aren’t as good at generating images as the bigger one. In this paper, researchers developed a new way to distill the knowledge of a teacher model into many small students. Each student is responsible for a part of the data, making it better at generating images. This approach allows smaller and faster image generation while still producing high-quality results. |
Keywords
» Artificial intelligence » Diffusion » Distillation » Image generation » Inference » Teacher model
