Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic
  Image Restoration In the Wild
Authors: name: Fanghua Yu, name: Jinjin Gu, name: Zheyuan Li, name: Jinfan Hu, name: Xiangtao Kong, name: Xintao Wang, name: Jingwen He, name: Yu Qiao, name: Chao Dong

We introduce SUPIR (Scaling-UP Image Restoration), a groundbreaking image
restoration method that harnesses generative prior and the power of model
scaling up. Leveraging multi-modal techniques and advanced generative prior,
SUPIR marks a significant advance in intelligent and realistic image
restoration. As a pivotal catalyst within SUPIR, model scaling dramatically
enhances its capabilities and demonstrates new potential for image restoration.
We collect a dataset comprising 20 million high-resolution, high-quality images
for model training, each enriched with descriptive text annotations. SUPIR
provides the capability to restore images guided by textual prompts, broadening
its application scope and potential. Moreover, we introduce negative-quality
prompts to further improve perceptual quality. We also develop a
restoration-guided sampling method to suppress the fidelity issue encountered
in generative-based restoration. Experiments demonstrate SUPIR's exceptional
restoration effects and its novel capacity to manipulate restoration through
textual prompts.