Summary of Towards Fast Stochastic Sampling in Diffusion Generative Models, by Kushagra Pandey et al.
Towards Fast Stochastic Sampling in Diffusion Generative Models
by Kushagra Pandey, Maja Rudolph, Stephan Mandt
First submitted to arxiv on: 11 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 In this paper, researchers tackle the issue of slow sample generation in diffusion models by proposing a new approach called Splitting Integrators. These integrators aim to improve sampling efficiency by cleverly alternating between numerical updates involving different variables. However, the authors show that a naive application of these integrators is sub-optimal and propose several principled modifications to achieve better results. The resulting Reduced Splitting Integrators are tested on Phase Space Langevin Diffusion (PSLD) with CIFAR-10 data and demonstrate an FID score of 2.36 in just 100 network function evaluations, outperforming existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making a special type of computer program called diffusion models run faster. These programs are used to generate new images or other data that looks similar to some training data. The problem is that they take a long time to do this. The researchers came up with an idea to make it go faster by using something called Splitting Integrators. They experimented and found that the first version wasn’t good enough, so they made some changes and got better results. Now, their new way of doing things can generate images much faster than before. |
Keywords
* Artificial intelligence * Diffusion
