OSDFace: One-Step Diffusion Model for Face Restoration (2411.17163v2)

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.

• The paper introduces OSDFace, a novel one-step diffusion model that efficiently restores degraded face images while preserving identity.
• It integrates a Visual Representation Embedder with a GAN to capture visual cues and align restored outputs with ground truth.
• Quantitative results show improved perceptual quality and reduced computational overhead, highlighting its potential for real-time image enhancement.

An Examination of OSDFace: One-Step Diffusion Model for Face Restoration

The paper "OSDFace: One-Step Diffusion Model for Face Restoration" presents a method aimed at addressing the challenges inherent in face restoration tasks using a novel approach known as the OSDFace model. This model employs a one-step diffusion process designed to improve the quality of face images suffering from degradation effects such as blur, noise, and compression. Traditional diffusion models, though effective, often suffer from computational inefficiencies due to their multi-step nature. OSDFace tackles these inefficiencies while maintaining high restoration quality and identity consistency.

Core Contributions

The primary contribution of the paper lies in its introduction of the OSDFace model, which leverages a one-step diffusion process for face restoration. The innovation here is the synthesis of high-quality images from low-quality inputs in a computationally efficient manner. The model is equipped with key components that include a visual representation embedder (VRE) and a generative adversarial network (GAN) for guiding the restoration process.

1. One-Step Diffusion Model: OSDFace methodology significantly reduces computational overhead by adopting a one-step diffusion approach. This is achieved without compromising the rich detail and natural appearance of the restored face images, which is often a trade-off in conventional systems.
2. Visual Representation Embedder (VRE): The VRE module is introduced to effectively capture prior information from low-quality face images. It uses a vector-quantized (VQ) dictionary that processes these images through a visual tokenizer, converting them into visual prompts.
3. Facial Identity Loss Integration: By incorporating facial identity loss, derived from face recognition models, the system ensures that the restored images maintain identity consistency with the original input, which is critical for practical applications where identity preservation is a must.
4. Adversarial Guidance: The use of GANs in guiding the diffusion process helps align the distribution between restored images and ground truth, leading to more realistic results.

Quantitative and Qualitative Performance

The OSDFace model demonstrates superior performance over existing state-of-the-art methods across various metrics and datasets. Notable quantitative outcomes include improved LPIPS, DISTS, and FID scores, reflecting enhanced perceptual quality and identity consistency. In particular, the model records substantial improvements with reduced multiplication-and-accumulate operations (MACs) and inference time, substantially lowering computational demands.

Implications and Future Directions

The introduction of a one-step diffusion mechanism into the face restoration domain is a notable advancement. It provides a viable pathway for deploying diffusion models in real-world applications where speed and fidelity are crucial, such as video call enhancement or automatic photo improvement software. The integration of VRE demonstrates potential for broader applications in domains where visual prior extraction is essential.

Future research could explore the extension of the OSDFace framework to other image domains beyond facial restoration. The adaptability of the VRE and its integration with diffusion models offers an intriguing avenue for venturing into generalized image restoration and enhancement tasks. Moreover, further optimizations in the GAN framework could yield even more realistic results, pushing the envelope of what is achievable with one-step diffusion processes.

In summary, the OSDFace model is a significant forward step in the field of computational photography and pattern recognition. Its methodological efficiencies and superior performance make it a valuable contribution to the advancement of AI in image restoration.