RestorerID: Towards Tuning-Free Face Restoration with ID Preservation

TL;DR

RestorerID is a tuning-free, diffusion model-based face restoration method that effectively preserves ID information across various degradation levels using a single reference image.

Contribution

It introduces a novel framework combining ID injection with blind face restoration and a Face ID Rebalancing Adapter to improve ID preservation without tuning.

Findings

Outperforms test-tuning and other reference-guided methods in ID preservation.

Effectively restores high-quality faces across different degradation levels.

Demonstrates superior results on Celeb-Ref and real-world datasets.

Abstract

Blind face restoration has made great progress in producing high-quality and lifelike images. Yet it remains challenging to preserve the ID information especially when the degradation is heavy. Current reference-guided face restoration approaches either require face alignment or personalized test-tuning, which are unfaithful or time-consuming. In this paper, we propose a tuning-free method named RestorerID that incorporates ID preservation during face restoration. RestorerID is a diffusion model-based method that restores low-quality images with varying levels of degradation by using a single reference image. To achieve this, we propose a unified framework to combine the ID injection with the base blind face restoration model. In addition, we design a novel Face ID Rebalancing Adapter (FIR-Adapter) to tackle the problems of content unconsistency and contours misalignment that are caused by information conflicts between the low-quality input and reference image. Furthermore, by employing an Adaptive ID-Scale Adjusting strategy, RestorerID can produce superior restored images across various levels of degradation. Experimental results on the Celeb-Ref dataset and real-world scenarios demonstrate that RestorerID effectively delivers high-quality face restoration with ID preservation, achieving a superior performance compared to the test-tuning approaches and other reference-guided ones. The code of RestorerID is available at \url{https://github.com/YingJiacheng/RestorerID}.