Metadata-Version: 2.1
Name: gfpgan
Version: 0.2.1
Summary: GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration
Home-page: https://github.com/TencentARC/GFPGAN
Author: Xintao Wang
Author-email: xintao.wang@outlook.com
License: Apache License Version 2.0
Description: # GFPGAN (CVPR 2021)
        
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        1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) for GFPGAN <a href="https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="google colab logo"></a>; (Another [Colab Demo](https://colab.research.google.com/drive/1Oa1WwKB4M4l1GmR7CtswDVgOCOeSLChA?usp=sharing) for the original paper model)
        1. We provide a *clean* version of GFPGAN, which can run without CUDA extensions. So that it can run in **Windows** or on **CPU mode**.
        
        GFPGAN aims at developing **Practical Algorithm for Real-world Face Restoration**.<br>
        It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g.*, StyleGAN2) for blind face restoration.
        
        :triangular_flag_on_post: **Updates**
        
        - :white_check_mark: We provide a *clean* version of GFPGAN, which does not require CUDA extensions.
        - :white_check_mark: We provide an updated model without colorizing faces.
        
        ### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior
        
        > [[Paper](https://arxiv.org/abs/2101.04061)] &emsp; [[Project Page](https://xinntao.github.io/projects/gfpgan)] &emsp; [Demo] <br>
        > [Xintao Wang](https://xinntao.github.io/), [Yu Li](https://yu-li.github.io/), [Honglun Zhang](https://scholar.google.com/citations?hl=en&user=KjQLROoAAAAJ), [Ying Shan](https://scholar.google.com/citations?user=4oXBp9UAAAAJ&hl=en) <br>
        > Applied Research Center (ARC), Tencent PCG
        
        <p align="center">
          <img src="https://xinntao.github.io/projects/GFPGAN_src/gfpgan_teaser.jpg">
        </p>
        
        ---
        
        ## :wrench: Dependencies and Installation
        
        - Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/#linux) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html))
        - [PyTorch >= 1.7](https://pytorch.org/)
        - Option: NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
        - Option: Linux (We have not tested on Windows)
        
        ### Installation
        
        We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. <br>
        If you want want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation.
        
        1. Clone repo
        
            ```bash
            git clone https://github.com/TencentARC/GFPGAN.git
            cd GFPGAN
            ```
        
        1. Install dependent packages
        
            ```bash
            # Install basicsr - https://github.com/xinntao/BasicSR
            # We use BasicSR for both training and inference
            pip install basicsr
        
            # Install facexlib - https://github.com/xinntao/facexlib
            # We use face detection and face restoration helper in the facexlib package
            pip install facexlib
        
            pip install -r requirements.txt
            python setup.py develop
            ```
        
        ## :zap: Quick Inference
        
        Download pre-trained models: [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth)
        
        ```bash
        wget https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth -P experiments/pretrained_models
        ```
        
        **Inference!**
        
        ```bash
        python inference_gfpgan.py --upscale_factor 2 --test_path inputs/whole_imgs --save_root results
        ```
        
        ## :european_castle: Model Zoo
        
        - [GFPGANCleanv1-NoCE-C2.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.2.0/GFPGANCleanv1-NoCE-C2.pth): No colorization; no CUDA extensions are required. It is still in training. Trained with more data with pre-processing.
        - [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth): The paper model, with colorization.
        
        ## :computer: Training
        
        We provide the training codes for GFPGAN (used in our paper). <br>
        You could improve it according to your own needs.
        
        **Tips**
        
        1. More high quality faces can improve the restoration quality.
        2. You may need to perform some pre-processing, such as beauty makeup.
        
        **Procedures**
        
        (You can try a simple version ( `options/train_gfpgan_v1_simple.yml`) that does not require face component landmarks.)
        
        1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset)
        
        1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder.
            1. [Pretrained StyleGAN2 model: StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/StyleGAN2_512_Cmul1_FFHQ_B12G4_scratch_800k.pth)
            1. [Component locations of FFHQ: FFHQ_eye_mouth_landmarks_512.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/FFHQ_eye_mouth_landmarks_512.pth)
            1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth)
        
        1. Modify the configuration file `options/train_gfpgan_v1.yml` accordingly.
        
        1. Training
        
        > python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 gfpgan/train.py -opt options/train_gfpgan_v1.yml --launcher pytorch
        
        ## :scroll: License and Acknowledgement
        
        GFPGAN is released under Apache License Version 2.0.
        
        ## BibTeX
        
            @InProceedings{wang2021gfpgan,
                author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan},
                title = {Towards Real-World Blind Face Restoration with Generative Facial Prior},
                booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
                year = {2021}
            }
        
        ## :e-mail: Contact
        
        If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`.
        
Keywords: computer vision,pytorch,image restoration,super-resolution,face restoration,gan,gfpgan
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
