  Blind face restoration is an important task in computer vision and has gained
significant attention due to its wide-range applications. Previous works mainly
exploit facial priors to restore face images and have demonstrated high-quality
results. However, generating faithful facial details remains a challenging
problem due to the limited prior knowledge obtained from finite data. In this
work, we delve into the potential of leveraging the pretrained Stable Diffusion
for blind face restoration. We propose BFRffusion which is thoughtfully
designed to effectively extract features from low-quality face images and could
restore realistic and faithful facial details with the generative prior of the
pretrained Stable Diffusion. In addition, we build a privacy-preserving face
dataset called PFHQ with balanced attributes like race, gender, and age. This
dataset can serve as a viable alternative for training blind face restoration
networks, effectively addressing privacy and bias concerns usually associated
with the real face datasets. Through an extensive series of experiments, we
demonstrate that our BFRffusion achieves state-of-the-art performance on both
synthetic and real-world public testing datasets for blind face restoration and
our PFHQ dataset is an available resource for training blind face restoration
networks. The codes, pretrained models, and dataset are released at
https://github.com/chenxx89/BFRffusion.
