InstantRestore: Single-Step Personalized Face Restoration with Shared-Image Attention

TL;DR

InstantRestore is a fast, scalable face restoration framework that uses a single-step diffusion model and attention-sharing to enhance degraded images while preserving identity features, achieving near real-time performance.

Contribution

It introduces a novel single-step diffusion-based framework with shared attention and landmark loss for efficient, personalized face restoration without per-identity tuning.

Findings

Outperforms existing methods in quality and speed

Achieves near real-time restoration with small reference sets

Effectively preserves identity details during restoration

Abstract

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.