Summary of Adaptive Non-uniform Timestep Sampling For Diffusion Model Training, by Myunsoo Kim et al.
Adaptive Non-Uniform Timestep Sampling for Diffusion Model Training
by Myunsoo Kim, Donghyeon Ki, Seong-Woong Shim, Byung-Jun Lee
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper proposes a novel approach to training diffusion models, which have shown great promise in image generation, natural language processing, and combinatorial optimization. However, as data distributions become more complex, traditional uniform timestep sampling can lead to slow convergence due to high-variance timesteps. To address this issue, the authors introduce a non-uniform timestep sampling method that prioritizes critical timesteps by tracking gradient updates’ impact on the objective function. Experimental results demonstrate accelerated training and improved performance at convergence across various datasets, scheduling strategies, and diffusion architectures. The proposed method outperforms previous heuristics in terms of robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers better at generating images, text, and other things by using a new way to train these models. Right now, it takes a long time for these models to get really good because some parts are more important than others. The authors figured out a way to focus on the most important parts first, which makes training faster and gets better results. |
Keywords
» Artificial intelligence » Diffusion » Image generation » Natural language processing » Objective function » Optimization » Tracking
