Metadata-Version: 2.1
Name: gfpgan
Version: 1.3.8
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: <p align="center">
          <img src="assets/gfpgan_logo.png" height=130>
        </p>
        
        ## <div align="center"><b><a href="README.md">English</a> | <a href="README_CN.md">简体中文</a></b></div>
        
        <div align="center">
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        1. :boom: **Updated** online demo: [![Replicate](https://img.shields.io/static/v1?label=Demo&message=Replicate&color=blue)](https://replicate.com/tencentarc/gfpgan). Here is the [backup](https://replicate.com/xinntao/gfpgan).
        1. :boom: **Updated** online demo: [![Huggingface Gradio](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](https://huggingface.co/spaces/Xintao/GFPGAN)
        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)
        
        <!-- 3. Online demo: [Replicate.ai](https://replicate.com/xinntao/gfpgan) (may need to sign in, return the whole image)
        4. Online demo: [Baseten.co](https://app.baseten.co/applications/Q04Lz0d/operator_views/8qZG6Bg) (backed by GPU, returns the whole image)
        5. We provide a *clean* version of GFPGAN, which can run without CUDA extensions. So that it can run in **Windows** or on **CPU mode**. -->
        
        > :rocket: **Thanks for your interest in our work. You may also want to check our new updates on the *tiny models* for *anime images and videos* in [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN/blob/master/docs/anime_video_model.md)** :blush:
        
        GFPGAN aims at developing a **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.
        
        :question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md).
        
        :triangular_flag_on_post: **Updates**
        
        - :white_check_mark: Add [RestoreFormer](https://github.com/wzhouxiff/RestoreFormer) inference codes.
        - :white_check_mark: Add [V1.4 model](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth), which produces slightly more details and better identity than V1.3.
        - :white_check_mark: Add **[V1.3 model](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth)**, which produces **more natural** restoration results, and better results on *very low-quality* / *high-quality* inputs. See more in [Model zoo](#european_castle-model-zoo), [Comparisons.md](Comparisons.md)
        - :white_check_mark: Integrated to [Huggingface Spaces](https://huggingface.co/spaces) with [Gradio](https://github.com/gradio-app/gradio). See [Gradio Web Demo](https://huggingface.co/spaces/akhaliq/GFPGAN).
        - :white_check_mark: Support enhancing non-face regions (background) with [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN).
        - :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.
        
        ---
        
        If GFPGAN is helpful in your photos/projects, please help to :star: this repo or recommend it to your friends. Thanks:blush:
        Other recommended projects:<br>
        :arrow_forward: [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN): A practical algorithm for general image restoration<br>
        :arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): An open-source image and video restoration toolbox<br>
        :arrow_forward: [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions<br>
        :arrow_forward: [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison<br>
        
        ---
        
        ### :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
        
        ### Installation
        
        We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. <br>
        If you 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
        
            # If you want to enhance the background (non-face) regions with Real-ESRGAN,
            # you also need to install the realesrgan package
            pip install realesrgan
            ```
        
        ## :zap: Quick Inference
        
        We take the v1.3 version for an example. More models can be found [here](#european_castle-model-zoo).
        
        Download pre-trained models: [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth)
        
        ```bash
        wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models
        ```
        
        **Inference!**
        
        ```bash
        python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2
        ```
        
        ```console
        Usage: python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2 [options]...
        
          -h                   show this help
          -i input             Input image or folder. Default: inputs/whole_imgs
          -o output            Output folder. Default: results
          -v version           GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3
          -s upscale           The final upsampling scale of the image. Default: 2
          -bg_upsampler        background upsampler. Default: realesrgan
          -bg_tile             Tile size for background sampler, 0 for no tile during testing. Default: 400
          -suffix              Suffix of the restored faces
          -only_center_face    Only restore the center face
          -aligned             Input are aligned faces
          -ext                 Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto
        ```
        
        If you want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation and inference.
        
        ## :european_castle: Model Zoo
        
        | Version | Model Name  | Description |
        | :---: | :---:        |     :---:      |
        | V1.3 | [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) | Based on V1.2; **more natural** restoration results; better results on very low-quality / high-quality inputs. |
        | V1.2 | [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. Trained with more data with pre-processing. |
        | V1 | [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth) | The paper model, with colorization. |
        
        The comparisons are in [Comparisons.md](Comparisons.md).
        
        Note that V1.3 is not always better than V1.2. You may need to select different models based on your purpose and inputs.
        
        | Version | Strengths  | Weaknesses |
        | :---: | :---:        |     :---:      |
        |V1.3 |  ✓ natural outputs<br> ✓better results on very low-quality inputs <br> ✓ work on relatively high-quality inputs <br>✓ can have repeated (twice) restorations | ✗ not very sharp <br> ✗ have a slight change on identity |
        |V1.2 |  ✓ sharper output <br> ✓ with beauty makeup | ✗ some outputs are unnatural |
        
        You can find **more models (such as the discriminators)** here: [[Google Drive](https://drive.google.com/drive/folders/17rLiFzcUMoQuhLnptDsKolegHWwJOnHu?usp=sharing)], OR [[Tencent Cloud 腾讯微云](https://share.weiyun.com/ShYoCCoc)]
        
        ## :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. [Pre-trained 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
