arXiv: cs.AI preprintsInternational5 November 2026
PEEK: One-Step-Look-Ahead Exposure Bias Correction for Diffusion Sampling
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arXiv:2611.05407v1 Announce Type: cross Abstract: Diffusion image generators are successful due to accurate denoising networks and strong numerical samplers. Yet the two components are not fully matched. During sampling, the denoiser predicts for the next step based on flawed input that differ much from the real data. This train-test gap, or exposure bias, caps image generation quality (i.e., blurry, lacking in details or awkward in shape). To mitigate the problem, we propose PEEK, a novel training-free deterministic sampler plugin. At each eligible denoising step, PEEK first performs a provis
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