  Although diffusion models are rising as a powerful solution for blind face
restoration, they are criticized for two problems: 1) slow training and
inference speed, and 2) failure in preserving identity and recovering
fine-grained facial details. In this work, we propose WaveFace to solve the
problems in the frequency domain, where low- and high-frequency components
decomposed by wavelet transformation are considered individually to maximize
authenticity as well as efficiency. The diffusion model is applied to recover
the low-frequency component only, which presents general information of the
original image but 1/16 in size. To preserve the original identity, the
generation is conditioned on the low-frequency component of low-quality images
at each denoising step. Meanwhile, high-frequency components at multiple
decomposition levels are handled by a unified network, which recovers complex
facial details in a single step. Evaluations on four benchmark datasets show
that: 1) WaveFace outperforms state-of-the-art methods in authenticity,
especially in terms of identity preservation, and 2) authentic images are
restored with the efficiency 10x faster than existing diffusion model-based BFR
methods.
