Plug-in Image Quality Control for Few-Step Posterior Diffusion Super-Resolution

Diffusion-based super-resolution (SR) has made remarkable progress, mainly through diffusion-prior approaches that rely on assumed degradation likelihoods or generative priors. Posterior diffusion SR instead learns directly from LR–HR pairs, but plug-in mechanisms for controlling the image quality of pretrained models remain less explored. To the best of our knowledge, we propose the first plug-in module for few-step posterior diffusion SR that enables image quality control without retraining the core model. Motivated by high-order diffusion solvers, we formulate a derivative-based correction targeting the per-step posterior-mean discrepancy, which may include the effects of accumulated discretization error. This discrepancy is related to a covariance-weighted KL gradient, motivating an LR-based image-guidance surrogate whose effective scale is calibrated for each denoising step. The sign of the guidance selects between a fidelity-oriented mode and a sharpness-oriented mode. In blind SR, the fidelity-oriented mode improves fidelity metrics, while the sharpness-oriented mode achieves higher scores on non-reference perceptual metrics such as MANIQA and CLIPIQA surpassing Stable Diffusion-based methods on RealSR. Our module provides continuous access to a range of image-quality operating points within a single pretrained posterior model.