Jingkai Wang, Jue Gong, Lin Zhang, Zheng Chen, Xing Liu, Hong Gu, Yutong Liu, Yulun Zhang, and Xiaokang Yang, "One-Step Diffusion Model for Face Restoration", CVPR, 2025

• 2025-12-23: Inference code and pretrained models are released.
• 2025-04-23: Results are released. (Synthetic dataset: CelebA-Test; Real-world datasets: Wider-Test, LFW-Test, and WebPhoto-Test)
• 2025-02-27: Congratulations! OSDFace is accepted to CVPR 2025.
• 2024-11-25: This repo is released.

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.

• Release inference code and pretrained models
• Release training code

• Environment
• Datasets
• Models
• Inference
• Training
• Results
• Citation
• Acknowledgements

1. Create conda environment with Python 3.10. (conda create -n osdface python=3.10)
2. Install PyTorch 2.4.0. (pip install torch==2.4.0 torchvision==0.19.0 torchaudio==2.4.0 --index-url https://mirror.sjtu.edu.cn/pytorch-wheels/cu121)
3. Install requirements.txt. (pip install -r requirements.txt)

Training Dataset:

We use FFHQ and FFHQR for training. All of them are resized to 512x512.

Testing Dataset:

We use CelebA-Test as the synthetic dataset. The download link can refer to DAEFR.

We use Wider-Test, LFW-Test, and WebPhoto-Test as the real-world datasets. The download links can refer to DAEFR and VQFR.

Thanks a lot for their contribution to Face Restoration.

1. Download the pretrained model from Google Drive. Put models in pretrained/, and put the test data in data/
2. Run the inference bash test.sh.

Since the offcial Stable Diffusion 2.1 is not avaliable, you could visit ModelScope to download it and use it locally.

If you meet an error from diffusers:

File "... lib/python3.10/site-packages/diffusers/utils/dynamic_modules_utils.py", line 28, in <module>
    from huggingface_hub import cached_download, hf_hub_download, model_info

you can edit the dynamic_modules_utils.py like:

1. Replace line 24 from from huggingface_hub import cached_download, hf_hub_download, model_info to from huggingface_hub import hf_hub_download, model_info.
2. Replace line 287 from resolved_module_file = cached_download( to resolved_module_file = hf_hub_download(.

We achieved state-of-the-art performance on synthetic and real-world datasets. Detailed results can be found in the paper, and the full results could be downloaded here: Google Drive or OneDrive.

If you find the code helpful in your research or work, please cite the following paper(s).

@InProceedings{wang2025osdface,
    author    = {Wang, Jingkai and Gong, Jue and Zhang, Lin and Chen, Zheng and Liu, Xing and Gu, Hong and Liu, Yutong and Zhang, Yulun and Yang, Xiaokang},
    title     = {{OSDFace}: One-Step Diffusion Model for Face Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12626-12636}
}

[TBD]