迈向卓越：在野外实践模型扩展以实现照片逼真图像修复

摘要

我们介绍了SUPIR（Scaling-UP Image Restoration），这是一种突破性的图像恢复方法，利用生成先验和模型扩展的能力。利用多模态技术和先进的生成先验，SUPIR标志着智能和逼真图像恢复方面的重大进展。作为SUPIR内的一个关键推动因素，模型扩展显著增强了其能力，并展示了图像恢复的新潜力。我们收集了一个包含2000万高分辨率、高质量图像的数据集用于模型训练，每个图像都附带描述性文本注释。SUPIR具有根据文本提示恢复图像的能力，拓宽了其应用范围和潜力。此外，我们引入了负质量提示以进一步提高感知质量。我们还开发了一种恢复引导抽样方法，以抑制生成式恢复中遇到的保真度问题。实验表明了SUPIR出色的恢复效果以及通过文本提示操纵恢复的新能力。

English

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.

迈向卓越：在野外实践模型扩展以实现照片逼真图像修复

Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild

摘要

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