  While deep learning-based methods for blind face restoration have achieved
unprecedented success, they still suffer from two major limitations. First,
most of them deteriorate when facing complex degradations out of their training
data. Second, these methods require multiple constraints, e.g., fidelity,
perceptual, and adversarial losses, which require laborious hyper-parameter
tuning to stabilize and balance their influences. In this work, we propose a
novel method named DifFace that is capable of coping with unseen and complex
degradations more gracefully without complicated loss designs. The key of our
method is to establish a posterior distribution from the observed low-quality
(LQ) image to its high-quality (HQ) counterpart. In particular, we design a
transition distribution from the LQ image to the intermediate state of a
pre-trained diffusion model and then gradually transmit from this intermediate
state to the HQ target by recursively applying a pre-trained diffusion model.
The transition distribution only relies on a restoration backbone that is
trained with $L_2$ loss on some synthetic data, which favorably avoids the
cumbersome training process in existing methods. Moreover, the transition
distribution can contract the error of the restoration backbone and thus makes
our method more robust to unknown degradations. Comprehensive experiments show
that DifFace is superior to current state-of-the-art methods, especially in
cases with severe degradations. Code and model are available at
https://github.com/zsyOAOA/DifFace.
