  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.
