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Metadata-Version: 2.1 |
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Name: gfpgan |
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Version: 1.3.8 |
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Summary: GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration |
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Home-page: https://github.com/TencentARC/GFPGAN |
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Author: Xintao Wang |
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Author-email: xintao.wang@outlook.com |
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License: Apache License Version 2.0 |
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Keywords: computer vision,pytorch,image restoration,super-resolution,face restoration,gan,gfpgan |
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Classifier: Development Status :: 4 - Beta |
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Classifier: License :: OSI Approved :: Apache Software License |
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Classifier: Operating System :: OS Independent |
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Classifier: Programming Language :: Python :: 3 |
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Classifier: Programming Language :: Python :: 3.7 |
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Classifier: Programming Language :: Python :: 3.8 |
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Description-Content-Type: text/markdown |
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License-File: LICENSE |
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<p align="center"> |
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<img src="assets/gfpgan_logo.png" height=130> |
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</p> |
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<div align="center"> |
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<!-- <a href="https://twitter.com/_Xintao_" style="text-decoration:none;"> |
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<img src="https://user-images.githubusercontent.com/17445847/187162058-c764ced6-952f-404b-ac85-ba95cce18e7b.png" width="4%" alt="" /> |
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</a> --> |
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[](https://github.com/TencentARC/GFPGAN/releases) |
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[](https://pypi.org/project/gfpgan/) |
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[](https://github.com/TencentARC/GFPGAN/issues) |
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[](https://github.com/TencentARC/GFPGAN/issues) |
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[](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE) |
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[](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/pylint.yml) |
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[](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/publish-pip.yml) |
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</div> |
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1. :boom: **Updated** online demo: [](https://replicate.com/tencentarc/gfpgan). Here is the [backup](https://replicate.com/xinntao/gfpgan). |
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1. :boom: **Updated** online demo: [](https://huggingface.co/spaces/Xintao/GFPGAN) |
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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) |
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<!-- 3. Online demo: [Replicate.ai](https://replicate.com/xinntao/gfpgan) (may need to sign in, return the whole image) |
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4. Online demo: [Baseten.co](https://app.baseten.co/applications/Q04Lz0d/operator_views/8qZG6Bg) (backed by GPU, returns the whole image) |
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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**. --> |
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> :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: |
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GFPGAN aims at developing a **Practical Algorithm for Real-world Face Restoration**.<br> |
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It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g.*, StyleGAN2) for blind face restoration. |
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:question: Frequently Asked Questions can be found in [FAQ.md](FAQ.md). |
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:triangular_flag_on_post: **Updates** |
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- :white_check_mark: Add [RestoreFormer](https://github.com/wzhouxiff/RestoreFormer) inference codes. |
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- :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. |
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- :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]( |
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- :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). |
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- :white_check_mark: Support enhancing non-face regions (background) with [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN). |
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- :white_check_mark: We provide a *clean* version of GFPGAN, which does not require CUDA extensions. |
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- :white_check_mark: We provide an updated model without colorizing faces. |
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--- |
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If GFPGAN is helpful in your photos/projects, please help to :star: this repo or recommend it to your friends. Thanks:blush: |
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Other recommended projects:<br> |
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:arrow_forward: [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN): A practical algorithm for general image restoration<br> |
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:arrow_forward: [BasicSR](https://github.com/xinntao/BasicSR): An open-source image and video restoration toolbox<br> |
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:arrow_forward: [facexlib](https://github.com/xinntao/facexlib): A collection that provides useful face-relation functions<br> |
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:arrow_forward: [HandyView](https://github.com/xinntao/HandyView): A PyQt5-based image viewer that is handy for view and comparison<br> |
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--- |
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> [[Paper](https://arxiv.org/abs/2101.04061)]   [[Project Page](https://xinntao.github.io/projects/gfpgan)]   [Demo] <br> |
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> [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> |
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> Applied Research Center (ARC), Tencent PCG |
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<p align="center"> |
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<img src="https://xinntao.github.io/projects/GFPGAN_src/gfpgan_teaser.jpg"> |
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</p> |
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--- |
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- Python >= 3.7 (Recommend to use [Anaconda](https://www.anaconda.com/download/ |
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- [PyTorch >= 1.7](https://pytorch.org/) |
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- Option: NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads) |
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- Option: Linux |
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We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions. <br> |
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If you want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation. |
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1. Clone repo |
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```bash |
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git clone https://github.com/TencentARC/GFPGAN.git |
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cd GFPGAN |
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``` |
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1. Install dependent packages |
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```bash |
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pip install basicsr |
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pip install facexlib |
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pip install -r requirements.txt |
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python setup.py develop |
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pip install realesrgan |
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``` |
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We take the v1.3 version for an example. More models can be found [here]( |
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Download pre-trained models: [GFPGANv1.3.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth) |
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```bash |
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wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models |
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``` |
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**Inference!** |
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```bash |
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python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2 |
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``` |
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```console |
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Usage: python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2 [options]... |
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-h show this help |
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-i input Input image or folder. Default: inputs/whole_imgs |
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-o output Output folder. Default: results |
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-v version GFPGAN model version. Option: 1 | 1.2 | 1.3. Default: 1.3 |
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-s upscale The final upsampling scale of the image. Default: 2 |
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-bg_upsampler background upsampler. Default: realesrgan |
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-bg_tile Tile size for background sampler, 0 for no tile during testing. Default: 400 |
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-suffix Suffix of the restored faces |
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-only_center_face Only restore the center face |
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-aligned Input are aligned faces |
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-ext Image extension. Options: auto | jpg | png, auto means using the same extension as inputs. Default: auto |
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``` |
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If you want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation and inference. |
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| Version | Model Name | Description | |
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| :---: | :---: | :---: | |
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| 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. | |
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| 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. | |
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| V1 | [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth) | The paper model, with colorization. | |
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The comparisons are in [Comparisons.md](Comparisons.md). |
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Note that V1.3 is not always better than V1.2. You may need to select different models based on your purpose and inputs. |
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| Version | Strengths | Weaknesses | |
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| :---: | :---: | :---: | |
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|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 | |
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|V1.2 | ✓ sharper output <br> ✓ with beauty makeup | ✗ some outputs are unnatural | |
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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)] |
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We provide the training codes for GFPGAN (used in our paper). <br> |
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You could improve it according to your own needs. |
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**Tips** |
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1. More high quality faces can improve the restoration quality. |
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2. You may need to perform some pre-processing, such as beauty makeup. |
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**Procedures** |
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(You can try a simple version ( `options/train_gfpgan_v1_simple.yml`) that does not require face component landmarks.) |
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1. Dataset preparation: [FFHQ](https://github.com/NVlabs/ffhq-dataset) |
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1. Download pre-trained models and other data. Put them in the `experiments/pretrained_models` folder. |
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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) |
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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) |
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1. [A simple ArcFace model: arcface_resnet18.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/arcface_resnet18.pth) |
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1. Modify the configuration file `options/train_gfpgan_v1.yml` accordingly. |
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1. Training |
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> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 gfpgan/train.py -opt options/train_gfpgan_v1.yml --launcher pytorch |
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GFPGAN is released under Apache License Version 2.0. |
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@InProceedings{wang2021gfpgan, |
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author = {Xintao Wang and Yu Li and Honglun Zhang and Ying Shan}, |
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title = {Towards Real-World Blind Face Restoration with Generative Facial Prior}, |
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booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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year = {2021} |
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} |
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If you have any question, please email `xintao.wang@outlook.com` or `xintaowang@tencent.com`. |
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