Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restoration

Figure 1. Comparison of SUPIR [91], DreamClear [3], and our proposed method. Our training dataset is constructed entirely from synthetic images. Trained with such data, our method achieves the most realistic restoration results with the lowest training cost.