Diffusion image generators rely on denoising networks and numerical samplers, yet the two components are not perfectly aligned. During sampling the denoiser predicts the next step from a noisy input that deviates substantially from real data, creating an exposure bias that leads to blurry, detail‑poor, or oddly shaped images. PEEK is a training‑free deterministic sampler plugin. At each eligible denoising step, PEEK first performs a provisional one‑step update to obtain a look‑ahead state, evaluates the denoiser again at that state, and then interpolates between this look‑ahead prediction and the original one‑step‑later prediction to produce the next noisy state. The extra look‑ahead step provides a higher‑signal noisy state, yielding a more image‑like prediction and mitigating exposure bias. Interpolation positions are found by a search that is accelerated using prefix and baseline noise‑schedule‑rebasing caches. Experiments on eight pixel‑space and latent‑space diffusion models—including unconditional, label‑guided, and text‑guided settings across various resolutions—show consistent improvements over NFE‑matched baselines, with FID gains up to 49.3% and minimal extra cost (model‑wise calibration takes at most 2.2 minutes). Project page: https://dachengruoque.github.io/PEEK-Video/\n\nReview: PEEK’s look‑ahead evaluation and interpolation strategy effectively reduces sampling exposure bias without heavy computational overhead, offering a practical plug‑in for higher‑quality diffusion generation.