Summary of Accelerating Convergence Of Score-based Diffusion Models, Provably, by Gen Li et al.
Accelerating Convergence of Score-Based Diffusion Models, Provably
by Gen Li, Yu Huang, Timofey Efimov, Yuting Wei, Yuejie Chi, Yuxin Chen
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Theory (cs.IT); Optimization and Control (math.OC); Machine Learning (stat.ML)
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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 presents novel training-free algorithms to accelerate deterministic (DDIM) and stochastic (DDPM) samplers in diffusion generative modeling. The authors design accelerated versions of these samplers that converge at faster rates than existing methods, with rates of O(1/T^2) for the deterministic sampler and O(1/T) for the stochastic sampler. These algorithms leverage insights from higher-order approximation and are applicable to target distributions without log-concavity or smoothness requirements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this paper speeds up popular generative models by designing new acceleration techniques that work with existing samplers. This is important because these models can be slow and computationally expensive. The authors show how their methods improve the convergence rates of these models, making them more efficient for use in various applications. |
Keywords
* Artificial intelligence * Diffusion
