OSDFace: One-Step Diffusion Model for Face Restoration

TL;DR

OSDFace introduces a one-step diffusion model for face restoration that improves efficiency and maintains high identity consistency, surpassing current methods in quality and metrics.

Contribution

The paper proposes a novel one-step diffusion approach with a visual representation embedder and identity loss, enhancing face restoration speed and accuracy.

Findings

OSDFace outperforms SOTA methods in visual quality.

It achieves higher identity consistency in restored faces.

Experimental results confirm improved quantitative metrics.

Abstract

Diffusion models have demonstrated impressive performance in face restoration. Yet, their multi-step inference process remains computationally intensive, limiting their applicability in real-world scenarios. Moreover, existing methods often struggle to generate face images that are harmonious, realistic, and consistent with the subject's identity. In this work, we propose OSDFace, a novel one-step diffusion model for face restoration. Specifically, we propose a visual representation embedder (VRE) to better capture prior information and understand the input face. In VRE, low-quality faces are processed by a visual tokenizer and subsequently embedded with a vector-quantized dictionary to generate visual prompts. Additionally, we incorporate a facial identity loss derived from face recognition to further ensure identity consistency. We further employ a generative adversarial network (GAN) as a guidance model to encourage distribution alignment between the restored face and the ground truth. Experimental results demonstrate that OSDFace surpasses current state-of-the-art (SOTA) methods in both visual quality and quantitative metrics, generating high-fidelity, natural face images with high identity consistency. The code and model will be released at https://github.com/jkwang28/OSDFace.