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@ -15,9 +15,15 @@ It leverages rich and diverse priors encapsulated in a pretrained face GAN (*e.g
:triangular_flag_on_post: **Updates** :triangular_flag_on_post: **Updates**
- :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 a *clean* version of GFPGAN, which does not require CUDA extensions.
- :white_check_mark: We provide an updated model without colorizing faces. - :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. Thanks:blush: <br>
Other recommended projects: [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN) &emsp; [BasicSR](https://github.com/xinntao/BasicSR) &emsp; [facexlib](https://github.com/xinntao/facexlib)
---
### :book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior ### :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> > [[Paper](https://arxiv.org/abs/2101.04061)] &emsp; [[Project Page](https://xinntao.github.io/projects/gfpgan)] &emsp; [Demo] <br>
@ -62,6 +68,10 @@ If you want want to use the original model in our paper, please see [PaperModel.
pip install -r requirements.txt pip install -r requirements.txt
python setup.py develop 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 ## :zap: Quick Inference