v/GFPGAN
1
0
Fork 0
mirror of https://github.com/TencentARC/GFPGAN.git synced 2025-05-18 08:10:16 -07:00
Code Issues Projects Releases Wiki Activity

add paste back and update README

Browse Source
This commit is contained in:
Xintao 2021-06-15 19:54:58 +08:00
parent 4f50996feb
commit 8041b69fbc
3 changed files with 67 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

29
README.md
View File

@ -8,13 +8,13 @@ GFPGAN is a blind face restoration algorithm towards real-world face images.
[Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo)
### :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>
> [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
#### Abstract
Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages **rich and diverse priors encapsulated in a pretrained face GAN** for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
#### BibTeX
@ -43,18 +43,39 @@ Blind face restoration usually relies on facial priors, such as facial geometry
```bash
git clone https://github.com/xinntao/GFPGAN.git
cd GFPGAN
```
1. Install dependent packages
```bash
cd GFPGAN
# Install basicsr - https://github.com/xinntao/BasicSR
# We use BasicSR for both training and inference
# Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient
BASICSR_EXT=True 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
```
## :zap: Quick Inference
> python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs
Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
> wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
> python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
> python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
## :computer: Training
We provide a complete training for GFPGAN. <br>
You could improve it according to your own needs.
> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
## :scroll: License and Acknowledgement

29
README_CN.md
View File

@ -8,13 +8,13 @@ GFPGAN is a blind face restoration algorithm towards real-world face images.
[Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo)
### :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>
> [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
#### Abstract
Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages rich and diverse priors encapsulated in a pretrained face GAN for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
Blind face restoration usually relies on facial priors, such as facial geometry prior or reference prior, to restore realistic and faithful details. However, very low-quality inputs cannot offer accurate geometric prior while high-quality references are inaccessible, limiting the applicability in real-world scenarios. In this work, we propose GFP-GAN that leverages **rich and diverse priors encapsulated in a pretrained face GAN** for blind face restoration. This Generative Facial Prior (GFP) is incorporated into the face restoration process via novel channel-split spatial feature transform layers, which allow our method to achieve a good balance of realness and fidelity. Thanks to the powerful generative facial prior and delicate designs, our GFP-GAN could jointly restore facial details and enhance colors with just a single forward pass, while GAN inversion methods require expensive image-specific optimization at inference. Extensive experiments show that our method achieves superior performance to prior art on both synthetic and real-world datasets.
#### BibTeX
@ -43,18 +43,39 @@ Blind face restoration usually relies on facial priors, such as facial geometry
```bash
git clone https://github.com/xinntao/GFPGAN.git
cd GFPGAN
```
1. Install dependent packages
```bash
cd GFPGAN
# Install basicsr - https://github.com/xinntao/BasicSR
# We use BasicSR for both training and inference
# Set BASICSR_EXT=True to compile the cuda extensions in the BasicSR - It may take several minutes to compile, please be patient
BASICSR_EXT=True 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
```
## :zap: Quick Inference
> python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs
Download pre-trained models: [GFPGANv1.pth](https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth)
> wget https://github.com/TencentARC/GFPGAN/releases/download/v0.1.0/GFPGANv1.pth -P experiments/pretrained_models
> python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/whole_imgs
> python inference_gfpgan_full.py --model_path experiments/pretrained_models/GFPGANv1.pth --test_path inputs/cropped_faces --aligned
## :computer: Training
We provide a complete training for GFPGAN. <br>
You could improve it according to your own needs.
> python -m torch.distributed.launch --nproc_per_node=4 --master_port=22021 train.py -opt train_gfpgan_v1.yml --launcher pytorch
## :scroll: License and Acknowledgement

19
inference_gfpgan_full.py
View File

@ -11,7 +11,14 @@ from archs.gfpganv1_arch import GFPGANv1
from basicsr.utils import img2tensor, imwrite, tensor2img
def restoration(gfpgan, face_helper, img_path, save_root, has_aligned=False, only_center_face=True, suffix=None):
def restoration(gfpgan,
face_helper,
img_path,
save_root,
has_aligned=False,
only_center_face=True,
suffix=None,
paste_back=False):
# read image
img_name = os.path.basename(img_path)
print(f'Processing {img_name} ...')
@ -60,6 +67,12 @@ def restoration(gfpgan, face_helper, img_path, save_root, has_aligned=False, onl
cmp_img = np.concatenate((cropped_face, restored_face), axis=1)
imwrite(cmp_img, os.path.join(save_root, 'cmp', f'{basename}_{idx:02d}.png'))
if not has_aligned and paste_back:
face_helper.get_inverse_affine(None)
save_restore_path = os.path.join(save_root, 'restored_imgs', img_name)
# paste each restored face to the input image
face_helper.paste_faces_to_input_image(save_restore_path)
if __name__ == '__main__':
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
@ -71,6 +84,7 @@ if __name__ == '__main__':
parser.add_argument('--suffix', type=str, default=None, help='Suffix of the restored faces')
parser.add_argument('--only_center_face', action='store_true')
parser.add_argument('--aligned', action='store_true')
parser.add_argument('--paste_back', action='store_true')
args = parser.parse_args()
if args.test_path.endswith('/'):
@ -110,6 +124,7 @@ if __name__ == '__main__':
save_root,
has_aligned=args.aligned,
only_center_face=args.only_center_face,
suffix=args.suffix)
suffix=args.suffix,
paste_back=args.paste_back)
print('Results are in the <results> folder.')