OSDFace: One-Step Diffusion Model for Face Restoration
This work addresses the challenge of real-time face restoration for applications requiring efficient processing, though it is incremental as it builds on existing diffusion and GAN methods.
The paper tackles the problem of computationally intensive multi-step inference in diffusion models for face restoration by proposing OSDFace, a one-step diffusion model that achieves state-of-the-art results in visual quality and quantitative metrics, generating high-fidelity, natural face images with high identity consistency.
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.