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.

翻译：盲人脸修复是计算机视觉中的一项重要任务，因其广泛的应用而备受关注。以往的工作主要利用面部先验来恢复人脸图像，并取得了高质量的结果。然而，由于从有限数据中获得的先验知识有限，生成逼真的面部细节仍然是一个具有挑战性的问题。在这项工作中，我们深入探索了利用预训练的Stable Diffusion进行盲人脸修复的潜力。我们提出了一种名为BFRffusion的方法，该方法经过精心设计，能够从低质量人脸图像中有效提取特征，并利用预训练Stable Diffusion的生成先验恢复出真实而逼真的面部细节。此外，我们构建了一个名为PFHQ的隐私保护人脸数据集，该数据集具有种族、性别和年龄等平衡属性。该数据集可作为训练盲人脸修复网络的可行替代方案，有效解决了真实人脸数据集中常见的隐私和偏见问题。通过一系列广泛的实验，我们证明了BFRffusion在用于盲人脸修复的合成和真实世界公开测试数据集上均达到了最先进的性能，并且我们的PFHQ数据集是训练盲人脸修复网络的可用资源。相关代码、预训练模型和数据集已在https://github.com/chenxx89/BFRffusion上发布。