https://github.com/habout632/fpgan

face generation
https://github.com/habout632/fpgan

Last synced: 8 days ago
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face generation

• Host: GitHub
• URL: https://github.com/habout632/fpgan
• Owner: habout632
• Created: 2019-11-15T08:18:05.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2019-11-17T14:01:14.000Z (about 5 years ago)
• Last Synced: 2024-11-30T10:32:09.005Z (2 months ago)
• Language: HTML
• Size: 408 KB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# GP-GAN: Gender-Preserving-GAN-for-Synthesizing-Faces-from-Landmarks

This repository is about out ICPR work, [GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks](https://arxiv.org/abs/1710.00962)

## Prerequisites

This code has tested on Ubuntu 16/18 with Pytorch 0.4 and cuda 9.0/8.0

## Getting Started

Clone this repo:  

* git clone https://github.com/DetionDX/GP-GAN-GenderPreserving-GAN-for-Synthesizing-Faces-from-Landmarks.git  

* cd GP-GAN-GenderPreserving-GAN-for-Synthesizing-Faces-from-Landmarks  

Download dataset

* cd datasets

* bash download_lfw_landmark_dataset.sh

## Training

change directory into the cloned folder

* cd ..

* python train.py --dataroot ./datasets/lfw/ --which_direction BtoA --fineSize 64 --loadSize 64 --no_flip  --name lfw_gpgan

To view training results and loss plots, run:  

* python -m visdom.server  

and click the URL http://localhost:8097

If you have prblem to download the pretrained vgg16.t7 file. You can download [here](https://www.dropbox.com/s/6nkmly7onpi5uug/vgg16.t7?dl=0): and put it into directory: ./models  

## Testing

python test.py --dataroot ./datasets/lfw/ --which_direction BtoA --fineSize 64 --loadSize 64  --name lfw_gpgan --which_epoch 10

## Reference

```

@INPROCEEDINGS{di2018gp, 

author={Xing Di and Vishwanath A. Sindagi and Vishal M. Patel}, 

booktitle={2018 24th International Conference on Pattern Recognition (ICPR)}, 

title={GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks}, 

year={2018},

pages={1079-1084}, 

month={Aug}

}

```

## Acknowledgments

This is work is highlg inspired by [pytorch-CycleGAN-and-pix2pix](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix#prerequisites)  

We highly thanks to [He Zhang](https://github.com/hezhangsprinter) for his discussing about DenseUnet part