Extreme Face Super-Resolution through Identity Fitting and Decoupling

In recent years, face super-resolution (FSR) methods haveachieved remarkable progress, generally maintaining high image fidelityand identity (ID) consistency under standard settings. However, underextreme degradation scenarios (e.g., scale ¿ > 8×), critical facial at-tributes and ID information are often severely lost in the input image,making it difficult for conventional models to reconstruct realistic andID-consistent faces. Existing methods tend to generate hallucinatedfaces under such conditions, producing restored images that lack faith-ful ID constraints. To address this challenge, we propose a novel FSRmethod with Identity Decoupling and Fitting (IDFSR), designed to en-hance ID restoration under large scaling factors while mitigating hallu-cination effects. Our approach involves three key designs: (1) maskingthe facial region in the low-resolution (LR) image to eliminate unre-liable ID cues; (2) warping a reference image to align with the LRinput, providing style guidance; and (3) leveraging ID embeddingsextracted from ground-truth (GT) images for fine-grained ID modelingand personalized adaptation. We first pretrain a diffusion-based modelto explicitly decouple style and ID by forcing it to reconstruct maskedLR facial regions using both style and ID embeddings. Subsequently, wefreeze most network parameters and perform lightweight fine-tuning ofthe ID embeddings using a small set of target ID images. This embeddingencodes fine-grained facial attributes and precise ID information, signifi-cantly improving both ID consistency and perceptual quality. Extensivequantitative evaluations and visual comparisons demonstrate that theproposed IDFSR substantially outperforms existing approaches underextreme degradation, particularly in terms of ID consistency.