Summary of Constant Rate Schedule: Constant-rate Distributional Change For Efficient Training and Sampling in Diffusion Models, by Shuntaro Okada et al.
Constant Rate Schedule: Constant-Rate Distributional Change for Efficient Training and Sampling in Diffusion Models
by Shuntaro Okada, Kenji Doi, Ryota Yoshihashi, Hirokatsu Kataoka, Tomohiro Tanaka
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The proposed noise schedule ensures a constant rate of change in the probability distribution of diffused data throughout the diffusion process. The schedule is determined by measuring the probability-distributional change of diffused data through simulation and using it to inform training of diffusion models. The functional form of the noise schedule is automatically tailored to each dataset and type of diffusion model, such as pixel space or latent space. Experiments on unconditional and class-conditional image generation tasks demonstrate that the proposed noise schedule improves performance of both pixel-space and latent-space diffusion models across various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to create random noise for computer vision tasks. It wants to make sure the noise is changing in a consistent way throughout the process. To do this, it simulates how the data changes and uses that information to decide what kind of noise to use. This approach helps different types of models (like ones that work with images or hidden codes) by automatically adjusting the noise schedule for each task. The paper tests its idea on several datasets and shows that it improves results. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model » Image generation » Latent space » Probability
