replicate/GFPGAN

replicate/GFPGAN

Description: Patches for GFPGAN

Language: Python

License: NOASSERTION

Stars: 13

Forks: 25

Open issues: 1

Created: 2024-03-14T00:33:02Z

Pushed: 2024-04-02T16:39:32Z

Default branch: master

Fork: yes

Parent repository: TencentARC/GFPGAN

Archived: no

README:

##

1. Colab Demo for GFPGAN ; (Another Colab Demo for the original paper model)

> :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** :blush:

GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration.

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

• :fire::fire::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 with Gradio. See Gradio Web Demo.
• :white_check_mark: Support enhancing non-face regions (background) with 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:

:arrow_forward: Real-ESRGAN: A practical algorithm for general image restoration

:arrow_forward: BasicSR: An open-source image and video restoration toolbox

:arrow_forward: facexlib: A collection that provides useful face-relation functions

:arrow_forward: HandyView: A PyQt5-based image viewer that is handy for view and comparison

---

:book: GFP-GAN: Towards Real-World Blind Face Restoration with Generative Facial Prior

> [Paper]   [Project Page]   [Demo]

> Xintao Wang, Yu Li, Honglun Zhang, Ying Shan

> Applied Research Center (ARC), Tencent PCG

---

:wrench: Dependencies and Installation

• Python >= 3.7 (Recommend to use Anaconda or Miniconda)
• PyTorch >= 1.7
• Option: NVIDIA GPU + CUDA
• Option: Linux

Installation

We now provide a *clean* version of GFPGAN, which does not require customized CUDA extensions.

If you want to use the original model in our paper, please see [PaperModel.md](PaperModel.md) for installation.

1. Clone repo

git clone https://github.com/TencentARC/GFPGAN.git
cd GFPGAN

1. Install dependent packages

# 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

wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P experiments/pretrained_models

Inference!

python inference_gfpgan.py -i inputs/whole_imgs -o results -v 1.3 -s 2

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 | Based on V1.2; more natural restoration results; better results on very low-quality / high-quality inputs. | | V1.2 | GFPGANCleanv1-NoCE-C2.pth | No colorization; no CUDA extensions are required. Trained with more data with pre-processing. | | V1 | 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...