  Blind face restoration (BFR) is a highly challenging problem due to the
uncertainty of degradation patterns. Current methods have low generalization
across photorealistic and heterogeneous domains. In this paper, we propose a
Diffusion-Information-Diffusion (DID) framework to tackle diffusion manifold
hallucination correction (DiffMAC), which achieves high-generalization face
restoration in diverse degraded scenes and heterogeneous domains. Specifically,
the first diffusion stage aligns the restored face with spatial feature
embedding of the low-quality face based on AdaIN, which synthesizes
degradation-removal results but with uncontrollable artifacts for some hard
cases. Based on Stage I, Stage II considers information compression using
manifold information bottleneck (MIB) and finetunes the first diffusion model
to improve facial fidelity. DiffMAC effectively fights against blind
degradation patterns and synthesizes high-quality faces with attribute and
identity consistencies. Experimental results demonstrate the superiority of
DiffMAC over state-of-the-art methods, with a high degree of generalization in
real-world and heterogeneous settings. The source code and models will be
public.
