Summary of Score-optimal Diffusion Schedules, by Christopher Williams et al.
Score-Optimal Diffusion Schedules
by Christopher Williams, Andrew Campbell, Arnaud Doucet, Saifuddin Syed
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- 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 This paper proposes a novel algorithm for adaptively selecting an optimal discretisation schedule in denoising diffusion models (DDMs). DDMs generate samples from high-dimensional data distributions by incrementally injecting noise into the data, and the chosen schedule affects the quality of these samples. The authors introduce a cost that measures the work done by the simulation procedure to transport samples between points in the diffusion path, allowing for adaptivity without hyperparameter tuning. This approach leverages the estimated Stein score, making it scalable to existing pre-trained models during training or inference. The algorithm obtains competitive FID scores on image datasets and recovers performant schedules previously discovered through manual search. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us make better pictures by figuring out how to improve a type of computer model called denoising diffusion models (DDMs). These models are good at creating new pictures that look like real ones. To do this, they add noise to the picture and then try to remove it. The problem is that there’s no easy way to know when to stop adding or removing noise. The authors of this paper come up with a new method for deciding when to stop, which helps them make better pictures. |
Keywords
» Artificial intelligence » Diffusion » Hyperparameter » Inference
