InstantRestore: Single-Step Personalized Face Restoration with Shared-Image Attention
| Autor: | Zhang, Howard, Alaluf, Yuval, Ma, Sizhuo, Kadambi, Achuta, Wang, Jian, Aberman, Kfir |
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| Rok vydání: | 2024 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Project page: https://snap-research.github.io/InstantRestore/ |
| Databáze: | arXiv |
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