Summary of Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-linear Time Complexity, by Haoxuan Chen et al.
Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity
by Haoxuan Chen, Yinuo Ren, Lexing Ying, Grant M. Rotskoff
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC); Numerical Analysis (math.NA); 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 The proposed algorithm aims to accelerate diffusion models by dividing the sampling process into blocks with parallelizable Picard iterations, achieving a provable sub-linear complexity w.r.t. the data dimension. This is achieved through rigorous theoretical analysis based on a generalized version of Girsanov’s theorem, compatible with both SDE and probability flow ODE implementations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion models are used to generate images and scientific data. To make them faster, researchers have come up with an idea to split the process into blocks that can be done in parallel. This helps reduce the time it takes to complete the task. The new method is mathematically proven to work efficiently for large datasets. |
Keywords
» Artificial intelligence » Diffusion » Probability
