The field of image and face restoration is rapidly evolving, with a focus on personalized and efficient methods. Recent developments have seen the integration of diffusion models, reference-guided approaches, and identity-preserving techniques to improve the quality and consistency of restored images. These innovations enable the adaptation of models to specific features and identities, while minimizing memory consumption and preserving user privacy. Notable papers include:

• Memory-Efficient Personalization of Text-to-Image Diffusion Models via Selective Optimization Strategies, which proposes a framework for efficient personalization of text-to-image diffusion models.
• Show and Polish: Reference-Guided Identity Preservation in Face Video Restoration, which introduces a novel method for face video restoration using a high-quality reference face image as a visual prompt.
• ID-EA: Identity-driven Text Enhancement and Adaptation with Textual Inversion for Personalized Text-to-Image Generation, which guides text embeddings to align with visual identity embeddings, improving identity preservation in personalized generation. These advancements have significant implications for various applications, including biometric security systems, age verification, and face restoration.