

Personalized Restoration Via Dual Pivot Tuning
Overview :
This paper proposes a simple and effective personalized image restoration method, named Dual-Pivot Tuning. The method consists of two steps: 1) Fine-tuning a conditional generative model to leverage the conditional information in the encoder for personalization; 2) Fixing the generative model and adjusting the parameters of the encoder to adapt to the strengthened personalized prior. This can generate natural images that preserve both personalized facial features and image degradation attributes. Experiments demonstrate that compared to non-personalized methods, this method can generate higher-fidelity facial images.
Target Users :
Suitable for image restoration tasks that require preserving specific facial features.
Use Cases
Personalized diffusion model for portrait image restoration using several reference photos of a particular celebrity.
Achieving face-swapping effects using different personalized models.
Combining text prompts for text-directed editing of personalized models.
Features
Leverage reference images for personalized diffusion prior
Preserve facial identity information in the denoising framework
Retain visual attributes of degraded images
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