Personalized Restoration via Dual-Pivot Tuning

Generative diffusion models can serve as priors, ensuring that image restoration solutions adhere to natural image manifolds. For facial images, however, personalized priors are essential to accurately reconstruct individual-specific facial features. We propose Dual-Pivot Tuning - a simple yet effective two-stage approach to personalize blind restoration systems while preserving general prior integrity. Our key observation is that for efficient personalization, the diffusion model should be tuned around a fixed textual pivot in the first step, while in the second step a guiding network should be tuned in a generic (non-personalized) manner, using the personalized diffusion model as a fixed "pivot". This approach ensures that personalization does not interfere with the restoration process, producing results with a natural appearance that show high fidelity to both identity and degraded image attributes. We conducted extensive experiments with images of widely recognized individuals, evaluating our approach both qualitatively and quantitatively against relevant baselines. Notably, our personalized prior not only achieves superior identity fidelity, but also outperforms state-of-the-art generic priors in terms of overall image quality. Project webpage is https://personalized-restoration.github.io/ and code is available at https://github.com/personalized-restoration/personalized-restoration.