InstantRestore: Single-Step Personalized Face Restoration with Shared-Image Attention
This addresses the challenge of scalable, identity-preserving face restoration for applications requiring real-time processing, though it appears incremental as it builds on diffusion models with novel attention mechanisms.
The paper tackles the problem of fast and personalized face image restoration by introducing InstantRestore, a framework that uses a single-step diffusion model and attention-sharing to achieve near real-time performance while preserving identity-specific features, outperforming existing methods in quality and speed.
Face image restoration aims to enhance degraded facial images while addressing challenges such as diverse degradation types, real-time processing demands, and, most crucially, the preservation of identity-specific features. Existing methods often struggle with slow processing times and suboptimal restoration, especially under severe degradation, failing to accurately reconstruct finer-level identity details. To address these issues, we introduce InstantRestore, a novel framework that leverages a single-step image diffusion model and an attention-sharing mechanism for fast and personalized face restoration. Additionally, InstantRestore incorporates a novel landmark attention loss, aligning key facial landmarks to refine the attention maps, enhancing identity preservation. At inference time, given a degraded input and a small (~4) set of reference images, InstantRestore performs a single forward pass through the network to achieve near real-time performance. Unlike prior approaches that rely on full diffusion processes or per-identity model tuning, InstantRestore offers a scalable solution suitable for large-scale applications. Extensive experiments demonstrate that InstantRestore outperforms existing methods in quality and speed, making it an appealing choice for identity-preserving face restoration.