Adaptive image restoration models can restore images with differentdegradation levels at inference time without the need to retrain the model. Wepresent an approach that is highly accurate and allows a significant reductionin the number of parameters. In contrast to existing methods, our approach canrestore images using a single fixed-size model, regardless of the number ofdegradation levels. On popular datasets, our approach yields state-of-the-artresults in terms of size and accuracy for a variety of image restoration tasks,including denoising, deJPEG, and super-resolution.