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

• URL: http://arxiv.org/abs/2412.06753v1
• Date: Mon, 09 Dec 2024 18:43:46 GMT
• Title: InstantRestore: Single-Step Personalized Face Restoration with Shared-Image Attention
• Authors: Howard Zhang, Yuval Alaluf, Sizhuo Ma, Achuta Kadambi, Jian Wang, Kfir Aberman,
• Abstract summary: 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.<n>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.<n>In experiments, InstantRestore outperforms existing methods in quality and speed, making it an appealing choice for identity-preserving face restoration.
• Score: 24.635713719257225
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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