  Restoring facial details from low-quality (LQ) images has remained a
challenging problem due to its ill-posedness induced by various degradations in
the wild. The existing codebook prior mitigates the ill-posedness by leveraging
an autoencoder and learned codebook of high-quality (HQ) features, achieving
remarkable quality. However, existing approaches in this paradigm frequently
depend on a single encoder pre-trained on HQ data for restoring HQ images,
disregarding the domain gap between LQ and HQ images. As a result, the encoding
of LQ inputs may be insufficient, resulting in suboptimal performance. To
tackle this problem, we propose a novel dual-branch framework named DAEFR. Our
method introduces an auxiliary LQ branch that extracts crucial information from
the LQ inputs. Additionally, we incorporate association training to promote
effective synergy between the two branches, enhancing code prediction and
output quality. We evaluate the effectiveness of DAEFR on both synthetic and
real-world datasets, demonstrating its superior performance in restoring facial
details. Project page: https://liagm.github.io/DAEFR/
