PEEK: One-Step Look-Ahead Exposure Bias Correction for Diffusion Sampling

Diffusion image generators owe much of their practical success to accurate denoising networks and strong numerical samplers. Yet the two components are not fully matched. During sampling, the denoiser is asked to make predictions based on imperfect noisy states formed by model predictions that differ from the training noisy states formed by real data. This train-test gap, known otherwise as exposure bias in literature, caps image generation quality (i.e., blurry, lacking in details or unnaturally-shaped). To mitigate the problem, we propose PEEK, a training-free deterministic sampler plugin. At each eligible denoising step, PEEK first performs a provisional one-step update, evaluates the denoiser once at the look-ahead state, and either keeps the current clean prediction or replaces it with this one-step-later prediction to form the actual noisy state. By taking the additional step, we generally obtain a higher-signal, more-image-like prediction for the noisy state, mitigating the exposure bias. To optimize the replacement positions, PEEK searches for a binary replacement schedule with a greedy metric-driven search made efficient by the PEEK prefix and baseline noise-schedule-rebasing caches; for a 9-step search, the caches reduce the one-candidate worst-case vanilla denoiser-evaluation count by 69.3\%. Across pixel-space and latent-space diffusion models with unconditional, label-guided, and text-guided settings on CIFAR-10, CelebA-HQ, LSUN Bedroom, ImageNet, and MS-COCO datasets, PEEK improves over NFE-matched DPM-Solver++ and DEIS baselines, with five-run mean FID reductions up to 39.3\% under the same deployment cost. Code will be released.