Diffusion models have impressive image generation capability, but low-quality generations still exist, and their identification remains challenging due to the lack of a proper sample-wise metric. To address this, we propose BayesDiff, a pixel-wise uncertainty estimator for generations from diffusion models based on Bayesian inference. In particular, we derive a novel uncertainty iteration principle to characterize the uncertainty dynamics in diffusion, and leverage the last-layer Laplace approximation for efficient Bayesian inference. The estimated pixel-wise uncertainty can not only be aggregated into a sample-wise metric to filter out low-fidelity images but also aids in augmenting successful generations and rectifying artifacts in failed generations in text-to-image tasks. Extensive experiments demonstrate the efficacy of BayesDiff and its promise for practical applications.
@article{arxiv.2310.11142,
title = {BayesDiff: Estimating Pixel-wise Uncertainty in Diffusion via Bayesian Inference},
author = {Siqi Kou and Lei Gan and Dequan Wang and Chongxuan Li and Zhijie Deng},
journal= {arXiv preprint arXiv:2310.11142},
year = {2024}
}