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Blind face restoration is an important task in computer vision and has gainedsignificant attention due to its wide-range applications. In this work, wedelve into the potential of leveraging the pretrained Stable Diffusion forblind face restoration. We propose BFRffusion which is thoughtfully designed toeffectively extract features from low-quality face images and could restorerealistic and faithful facial details with the generative prior of thepretrained Stable Diffusion. In addition, we build a privacy-preserving facedataset called PFHQ with balanced attributes like race, gender, and age. Thisdataset can serve as a viable alternative for training blind face restorationmethods, effectively addressing privacy and bias concerns usually associatedwith the real face datasets. Through an extensive series of experiments, wedemonstrate that our BFRffusion achieves state-of-the-art performance on bothsynthetic and real-world public testing datasets for blind face restoration andour PFHQ dataset is an available resource for training blind face restorationnetworks. The codes, pretrained models, and dataset are released athttps://github.com/chenxx89/BFRffusion.

Towards Real-World Blind Face Restoration with Generative Diffusion Prior