Blind face restoration is an important task in computer vision and has gained significant attention due to its wide-range applications. 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 methods, 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，因其广泛的应用而受到显著关注。在本文中，我们深入探索了利用预训练Stable Diffusion进行盲人脸修复的潜力。我们提出了BFRffusion，该模型经过精心设计，能够从低质量人脸图像中有效提取特征，并借助预训练Stable Diffusion的生成先验，恢复真实且忠实的人脸细节。此外，我们构建了一个名为PFHQ的隐私保护人脸数据集，该数据集在种族、性别和年龄等属性上保持均衡。该数据集可作为训练盲人脸修复方法的可行替代方案，有效解决真实人脸数据集中常见的隐私和偏差问题。通过一系列广泛的实验，我们证明了BFRffusion在合成和真实世界的公共测试数据集上均取得了盲人脸修复的最优性能，同时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。