  We present AutoDIR, an innovative all-in-one image restoration system
incorporating latent diffusion. AutoDIR excels in its ability to automatically
identify and restore images suffering from a range of unknown degradations.
AutoDIR offers intuitive open-vocabulary image editing, empowering users to
customize and enhance images according to their preferences. Specifically,
AutoDIR consists of two key stages: a Blind Image Quality Assessment (BIQA)
stage based on a semantic-agnostic vision-language model which automatically
detects unknown image degradations for input images, an All-in-One Image
Restoration (AIR) stage utilizes structural-corrected latent diffusion which
handles multiple types of image degradations. Extensive experimental evaluation
demonstrates that AutoDIR outperforms state-of-the-art approaches for a wider
range of image restoration tasks. The design of AutoDIR also enables flexible
user control (via text prompt) and generalization to new tasks as a foundation
model of image restoration. Project is available at:
\url{https://jiangyitong.github.io/AutoDIR_webpage/}.
