Summary of One-step Diffusion Distillation Through Score Implicit Matching, by Weijian Luo and Zemin Huang and Zhengyang Geng and J. Zico Kolter and Guo-jun Qi
One-Step Diffusion Distillation through Score Implicit Matching
by Weijian Luo, Zemin Huang, Zhengyang Geng, J. Zico Kolter, Guo-jun Qi
First submitted to arxiv on: 22 Oct 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 Score Implicit Matching (SIM) is a novel approach to distilling pre-trained diffusion models into single-step generator models, achieving almost the same sample generation ability as the original model without requiring training samples. This method relies on computing gradients for score-based divergences between diffusion and generator models under certain conditions. SIM demonstrates strong empirical performances on CIFAR10, achieving FID scores of 2.06 for unconditional generation and 1.96 for class-conditional generation. Additionally, SIM distills a single-step transformer-based text-to-image (T2I) generator that outperforms other one-step generators, including SDXL-TURBO, SDXL-LIGHTNING, and HYPER-SDXL, with an aesthetic score of 6.42. This single-step T2I generator will be released alongside this paper. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion models are powerful tools for generating realistic images, but they often require many steps to produce high-quality results. To make them more efficient, researchers have developed methods to “distill” pre-trained diffusion models into smaller, faster versions that still generate great pictures. But these methods usually need a lot of computation or sacrifice some quality. A new approach called Score Implicit Matching (SIM) can distill diffusion models into single-step generators that are just as good without needing any extra training data. This method works by finding a way to efficiently calculate the differences between the original model and the new, smaller generator. The results show that SIM can generate great-looking images with just one step, beating other methods in some cases. |
Keywords
* Artificial intelligence * Diffusion * Transformer
