從卓越擴展：在野外實踐模型擴展以進行照片逼真影像修復

摘要

我們介紹了 SUPIR（Scaling-UP Image Restoration），這是一種開創性的影像修復方法，利用生成先驗和模型擴展的威力。通過利用多模態技術和先進的生成先驗，SUPIR標誌著智能和逼真影像修復方面的重大進步。作為 SUPIR 內的關鍵催化劑，模型擴展顯著增強了其能力，展示了影像修復的新潛力。我們收集了一個包含 2 千萬高分辨率、高質量影像的數據集進行模型訓練，每個影像都附帶有描述性文本註釋。SUPIR 具備根據文本提示修復影像的能力，擴大了應用範圍和潛力。此外，我們引入了負質量提示以進一步提高感知質量。我們還開發了一種修復引導取樣方法，以抑制生成式修復中遇到的保真度問題。實驗證明了 SUPIR 卓越的修復效果，以及通過文本提示操控修復的新能力。

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.

從卓越擴展：在野外實踐模型擴展以進行照片逼真影像修復

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

摘要

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