Summary of High Noise Scheduling Is a Must, by Mahmut S. Gokmen et al.
High Noise Scheduling is a Must
by Mahmut S. Gokmen, Cody Bumgardner, Jie Zhang, Ge Wang, Jin Chen
First submitted to arxiv on: 9 Apr 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 proposed consistency model employs advanced techniques to generate high-quality images, moving away from traditional sampling methods. The study builds upon recent advancements in consistency training, eliminating the limitations of distillation training. By introducing polynomial noise distribution and a predefined Karras noise algorithm, the model achieves better results than basic consistency models. Additionally, the implementation of a sinusoidal-based curriculum enhances denoising performance. The paper compares its proposed technique to state-of-the-art models using the same hyperparameters, demonstrating improved FID scores (33.54) after 100,000 training steps with constant discretization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research focuses on improving image generation capabilities by developing a new consistency model. This model uses advanced techniques and noise distribution methods to produce better results than previous approaches. The study also explores the use of a sinusoidal-based curriculum to enhance denoising performance. The goal is to create a more robust and effective image generation system. |
Keywords
* Artificial intelligence * Distillation * Image generation
