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[CVPR 2022] RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs
| Date | Stars |
|---|---|
| 2026-07-24 | 363 |
| 2026-07-25 | 363 |
| 2026-07-28 | 363 |
| 2026-07-30 | 363 |
| 2026-08-06 | 363 |
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# We have merged the code of RestoreFormer into our journal version, RestoreFormer++. Please feel free to access the resources from [https://github.com/wzhouxiff/RestoreFormerPlusPlus](https://github.com/wzhouxiff/RestoreFormerPlusPlus) # Updating - **20230915** Update an online demo [](https://huggingface.co/spaces/wzhouxiff/RestoreFormerPlusPlus) - **20230915** A more user-friendly and comprehensive inference method refer to our [RestoreFormer++](https://github.com/wzhouxiff/RestoreFormerPlusPlus) - **20230116** For convenience, we further upload the [test datasets](#testset), including CelebA (both HQ and LQ data), LFW-Test, CelebChild-Test, and Webphoto-Test, to OneDrive and BaiduYun. - **20221003** We provide the link of the [test datasets](#testset). - **20220924** We add the code for [**metrics**](#metrics) in scripts/metrics. <!-- # RestoreFormer This repo includes the source code of the paper: "[RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs](https://openaccess.thecvf.com/content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf)" (CVPR 2022) by Zhouxia Wang, Jiawei Zhang, Runjian Chen, Wenping Wang, and Ping Luo.  **RestoreFormer** tends to explore fully-spatial attentions to model contextual information and surpasses existing works that use local operators. It has several benefits compared to prior arts. First, it incorporates a multi-head coross-attention layer to learn fully-spatial interations between corrupted queries and high-quality key-value pairs. Second, the key-value pairs in RestoreFormer are sampled from a reconstruction-oriented high-quality dictionary, whose elements are rich in high-quality facial features specifically aimed for face reconstruction. --> <!-- --> <!-- ## Environment - python>=3.7 - pytorch>=1.7.1 - pytorch-lightning==1.0.8 - omegaconf==2.0.0 - basicsr==1.3.3.4 **Warning** Different versions of pytorch-lightning and omegaconf may lead to errors or different results. ## Preparations of dataset and models **Dataset**: - Training data: Both **HQ Dictionary** and **RestoreFormer** in our work are trained with **FFHQ** which attained from [FFHQ repository](https://github.com/NVlabs/ffhq-dataset). The original size of the images in FFHQ are 1024x1024. We resize them to 512x512 with bilinear interpolation in our work. Link this dataset to ./data/FFHQ/image512x512. - <a id="testset">Test data</a>: * CelebA-Test-HQ: [OneDrive](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wzhoux_connect_hku_hk/EY7P-MReZUZOngy3UGa5abUBJKel1IH5uYZLdwp2e2KvUw?e=rK0VWh); [BaiduYun](https://pan.baidu.com/s/1tMpxz8lIW50U8h00047GIw?pwd=mp9t)(code mp9t) * CelebA-Test-LQ: [OneDrive](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wzhoux_connect_hku_hk/EXULDOtX3qdKg9_--k-hbr4BumxOUAi19iQjZNz75S6pKA?e=Kghqri); [BaiduYun](https://pan.baidu.com/s/1y6ZcQPCLyggj9VB5MgoWyg?pwd=7s6h)(code 7s6h) * LFW-Test: [OneDrive](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wzhoux_connect_hku_hk/EZ7ibkhUuRxBjdd-MesczpgBfpLVfv-9uYVskLuZiYpBsg?e=xPNH26); [BaiduYun](https://pan.baidu.com/s/1UkfYLTViL8XVdZ-Ej-2G9g?pwd=7fhr)(code 7fhr). Note that it was align with dlib. * CelebChild: [OneDrive](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wzhoux_connect_hku_hk/ESK6vjLzDuJAsd-cfWrfl20BTeSD_w4uRNJREGfl3zGzJg?e=Tou7ft); [BaiduYun](https://pan.baidu.com/s/1pGCD4TkhtDsmp8emZd8smA?pwd=rq65)(code rq65) * WepPhoto-Test: [OneDrive](https://connecthkuhk-my.sharepoint.com/:u:/g/personal/wzhoux_connect_hku_hk/ER1-0eYKGkZIs-YEDhNW0xIBohCI5IEZyAS2PAvI81Stcg?e=TFJFGh); [BaiduYun](https://pan.baidu.com/s/1SjBfinSL1F-bbOpXiD0nlw?pwd=nren)(code nren) **Model**: Both pretrained models used for training and the trained model of our
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