Learned Degradation Guidance: Turning Frozen Image Editors into Controllable Restorers

Instruction-based image editors have a strong image prior, and classifier-free guidance allows them to trade fidelity for perceptual quality with a single scale. Guidance pushes the output away from a second prediction of the model. In restoration, the model usually makes this second prediction under a generic low-quality prompt. Such a prompt names a degradation but cannot show the one in the image, so the model produces a degradation of its own. We introduce Learned Degradation Guidance (LDG), in which a trained adapter, not a prompt, makes the second prediction. On a frozen editor, a degradation adapter makes an image's own degradation stronger, and a restoration adapter removes it. The two adapters are trained separately, only on images that the editor generates itself, and are combined by guidance at inference. Guidance then pushes the output away from the image's own degradation, and one scale takes the editor from faithful to sharp restoration. With this second prediction, the editor reaches a higher perceptual quality than with one prompted to degrade. In real-world super-resolution tests, LDG with LoRA adapters is closer to the ground truth than the state of the art at its most faithful setting, reaches the highest perceptual quality at larger scales, and at equal or higher quality stays closer to the ground truth in almost every comparison. On real photographs with nine kinds of degradation, such as haze, rain and reflections, it achieves the best average score among the compared methods on a recent benchmark, with one adapter pair per degradation, each trained on about a hundred generated images. Source-attention calibration, an adapter that only reweights the editor's attention with a few thousand parameters, is almost as faithful as LoRA at the faithful end of the scale.