SelfVerify: Typicality-Guided Sampling with Diffusion Models

Diffusion models are trained to predict noise under a prescribed Gaussian corruption process, suggesting that their own noise predictions may provide an internal signal for candidate selection. We introduce SelfVerify, which compares patchwise moments of predicted noise with a Gaussian reference and retains the lowest-scoring trajectory. SelfVerify requires neither a separately trained external reward or preference model nor additional denoiser evaluations, avoiding reliance on external-verifier training and preference data for selection. We interpret the resulting score as a noise-statistical typicality proxy, with preference and alignment gains treated only as downstream empirical outcomes. Controlled noise-process interventions show that predicted-noise statistics respond to selected departures from the training process, including variance changes missed by a patch-mean statistic. On SANA-base, SelfVerify improves several preference metrics under standard classifier-free guidance. Motivated by the possibility that guidance-induced off-manifold behavior may weaken this signal, we additionally evaluate SelfVerify under CFG++, an independently proposed manifold-constrained guidance method, and observe larger relative selection gains. A scored-branch control further shows that the prediction used for ranking materially affects performance. Consistent gains on GenEval2 and AudioLDM2 further suggest that the signal transfers across tasks and modalities. Overall, predicted-noise typicality provides a useful internal sampling signal without establishing formal data typicality, manifold proximity, or a causal link from typicality to preference.