Summary of Efficient Integrators For Diffusion Generative Models, by Kushagra Pandey et al.
Efficient Integrators for Diffusion Generative Models
by Kushagra Pandey, Maja Rudolph, Stephan Mandt
First submitted to arxiv on: 11 Oct 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); 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 paper proposes a principled framework for accelerating sample generation in pre-trained diffusion models. Two complementary frameworks, Conjugate Integrators and Splitting Integrators, are introduced to improve the efficiency of deterministic and stochastic sampling methods. The proposed hybrid method achieves state-of-the-art performance on the Phase Space Langevin Diffusion model on CIFAR-10, with FID scores of 2.11 for deterministic and 2.36 for stochastic samplers, outperforming baselines in just 100 network function evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make diffusion models faster by finding new ways to generate samples quickly. It introduces two new methods: Conjugate Integrators and Splitting Integrators. These methods work together to make it possible to generate high-quality images fast, which is important for applications like generating realistic pictures of objects. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model
