DG-Font: Deformable Generative Networks for Unsupervised Font Generation

The source code for 'DG-Font: Deformable Generative Networks for Unsupervised Font Generation', by Yangchen Xie, Xinyuan Chen, Li sun and Yue lu. The paper was accepted by CVPR2021.

Arvix version

Note

Note that the current repo only works with 80x80 resolution images. An improved version of DGFont is coming soon, which will also fit 128x128 and 256x256 resolution images.

Gallery

Dependencies

Libarary

pytorch (>=1.0)
tqdm  
numpy
opencv-python  
scipy  
sklearn
matplotlib  
pillow  
tensorboardX

DCN

please refer to https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch/tree/pytorch_1.0.0 to install the dependencies of deformable convolution.

Dataset

方正字库 provides free font download for non-commercial users.

Example directory hierarchy

Project
|--- DG-Font
|          |--- font2img.py    
|          |--- main.py
|          |--- train
|                 |--- train.py
|
|--- data
       |--- font1
       |--- font2
             |--- 0000.png
             |--- 0001.png
             |--- ...
       |--- ...

How to run

prepare dataset

python font2img.py --ttf_path ttf_folder --chara character.txt --save_path save_folder --img_size 80 --chara_size CHARACTERSIZE
python font2img.py --ttf_path ../gwfonts-200 --chara roman_character.txt --save_path ../gwfonts-images-DG-Font --img_size 80 --chara_size 60
python font2img.py --ttf_path ../gwfonts-200 --chara roman_character.txt --save_path ../gwfonts-images-DG-Font --img_size 80 --chara_size 60 --image_base_path ../attributeData/grayscale_images/ --image_save_path ../grayscale_images_200

train

python main.py --gpu GPU_ID --img_size 80 --data_path /path/to --output_k CLASS_NUM --batch_size BATCHSIZE --val_num TEST_IMGS_NUM_FOR_EACH_CLASS

test

python main.py --gpu GPU_ID --img_size 80 --data_path /path/to --output_k CLASS_NUM --batch_size BATCHSIZE --validation --load_model $DIR_TO_LOAD

Acknowledgements

We would like to thank Johnson yue and 上海驿创信息技术有限公司 for their advices in code. Our code is based on TUNIT.

Bibtex

@inproceedings{DG-Font,
    title={DG-Font: Deformable Generative Networks for Unsupervised Font Generation},
    author={Yangchen Xie, Xinyuan Chen, Li sun, Yue lu},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    year={2021}
}

yukistavailable/DG-Font