Improved denoising diffusion probabilistic models with efficient non-diagonal covariance modeling
The sampling process of Denoising Diffusion Probabilistic Models (DDPMs) can be accelerated by leveraging second-order information in the form of approximations to the denoising posterior covariance -- allowing samples of acceptable quality to be produced in fewer but larger sampling steps. Previous attempts at using such information have used drastic (e.g.\ diagonal) simplifications of the covariance. These do not do justice to the peculiar statistical structure of natural images, which exhibit strong non-diagonal correlations between pixels and color channels, and a slow-decaying power-law frequency spectrum. Here, we develop a novel covariance model that captures these features. Our Kr...
| Authors | Rui Xia, Ayan Das, Artem Artemev et al. |
|---|---|
| Primary category | cs.CV |
| Published | 22 August 2026 |
| Last updated at source | 22 August 2026 |
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