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.

翻译：人脸图像复原旨在增强退化的人脸图像，同时应对多样化退化类型、实时处理需求等挑战，其中最关键的是保持身份特异性特征。现有方法常面临处理速度慢、复原效果欠佳的问题，尤其在严重退化条件下难以准确重建细粒度身份细节。为解决这些问题，本文提出InstantRestore——一种基于单步图像扩散模型与注意力共享机制的新型快速个性化人脸复原框架。该框架创新性地引入地标注意力损失函数，通过对齐关键人脸特征点来优化注意力图，从而增强身份保持能力。在推理阶段，给定退化输入图像及少量（约4张）参考图像，InstantRestore仅需单次前向传播即可实现近实时处理。相较于依赖完整扩散过程或需针对每个身份进行模型调优的现有方法，本框架提供了适用于大规模应用的可扩展解决方案。大量实验表明，InstantRestore在复原质量与处理速度上均优于现有方法，成为身份保持型人脸复原的理想选择。