Summary of Adversarial Score Identity Distillation: Rapidly Surpassing the Teacher in One Step, by Mingyuan Zhou and Huangjie Zheng and Yi Gu and Zhendong Wang and Hai Huang
Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step
by Mingyuan Zhou, Huangjie Zheng, Yi Gu, Zhendong Wang, Hai Huang
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: 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 Identity Distillation (SiD) is a data-free method that achieves state-of-the-art performance in image generation using a pretrained diffusion model. However, its ultimate performance relies on the accuracy of the pretrained model capturing true data scores during the diffusion process. This paper introduces SiDA (SiD with Adversarial Loss), which enhances generation quality and distillation efficiency by incorporating real images and adversarial loss. SiDA utilizes an encoder from the generator’s score network as a discriminator, distinguishing between real images and those generated by SiD. The adversarial loss is combined with the original SiD loss, allowing SiDA to distill a single-step generator. SiDA converges faster than its predecessor when distilled from scratch and improves upon the original model’s performance during fine-tuning from a pre-distilled SiD generator. This one-step adversarial distillation method sets new benchmarks in generation performance for EDM diffusion models, achieving FID scores of 1.110 on ImageNet 64×64. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making an image generation model better by adding something called “adversarial loss”. The old model was good but not perfect, so they added this new thing to make it even better. It works by comparing the generated images with real ones and saying “hey, these are fake, these are real!” This helps the model learn how to create more realistic images. They tested this new method on different-sized models and found that it worked really well for all of them. This is important because making images look more real could be used for things like creating fake faces or animals. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Distillation » Encoder » Fine tuning » Image generation
