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.

翻译：我们提出SUPIR（规模化图像恢复），一种开创性的图像恢复方法，该方法利用生成先验与模型规模化扩展的力量。通过融合多模态技术与先进的生成先验，SUPIR标志着智能逼真图像恢复领域的重大进展。作为SUPIR的核心催化剂，模型规模化显著增强了其能力，并为图像恢复展现了全新潜力。我们构建了一个包含2000万张高分辨率、高质量图像的数据集用于模型训练，每张图像均配有描述性文本注释。SUPIR支持通过文本提示引导图像恢复，拓展了其应用范围与潜力。此外，我们引入负质量提示以进一步提升感知质量，并开发了一种恢复导向的采样方法，以抑制生成式恢复中遇到的保真度问题。实验证明，SUPIR具备卓越的恢复效果，以及通过文本提示操控恢复过程的全新能力。