Awesome
<img src="https://github.com/williamyang1991/TET-GAN/blob/master/imgs/teaser.png" width="80%" height="80%">TET-GAN
This is a pytorch implementation of the paper.
Shuai Yang, Jiaying Liu, Wenjing Wang and Zongming Guo. TET-GAN: Text Effects Transfer via Stylization and Destylization, Accepted by AAAI Conference on Artificial Intelligence (AAAI), 2019.
[Project] | [Paper] | [Dataset]
It is provided for educational/research purpose only. Please consider citing our paper if you find the software useful for your work.
Usage:
Prerequisites
- Python 2.7
- Pytorch 0.4.1
- matplotlib
- scipy
- opencv-python
- Pillow
Install
- Clone this repo:
git clone https://github.com/williamyang1991/TET-GAN.git
cd TET-GAN/src
Testing Example
- Download a pre-trained model from [Google Drive] or [Baidu Cloud] to
../save/
- Style Transfer with default parameters
- Results can be found in
../output/
- Results can be found in
python test.py
- Destylization with default parameters
python test.py --c2s 0
- Transfer the style of
26.jpg
onto the text image2.png
and save the result as26_2.png
python test.py --style_name ../data/style/26.png --content_name ../data/content/2.png --name 26_2.png
- For black and white text images, use option
--content_type 1
python test.py --style_name ../data/style/1.png --content_name ../data/content/4.png --content_type 1
Training Examples
Full Training
- Prepare dataset in
../data/dataset/
where the images are arranged in this way (See [Dataset] for one example)
dataset/style1/train/1.png
dataset/style1/train/2.png
...
dataset/style2/train/1.png
dataset/style2/train/2.png
...
- The training image consists of a text image and a style image as follows
- Train TET-GAN with default parameters
python train.py
Saved model can be found at ../save/
- Use
--help
to view more training options
python train.py --help
Oneshot Training
-
Download a pre-trained model from [Google Drive] or [Baidu Cloud] to
../save/
- Specify the pretrained model to load using the option
--load_model_name
- Specify the pretrained model to load using the option
-
Finetune TET-GAN on a new style/glyph image pair (supervised oneshot training)
python oneshotfinetune.py --style_name ../data/oneshotstyle/1-train.png
Saved model can be found at ../save/
- Finetune TET-GAN on a new style image without its glyph counterpart (unsupervised oneshot training)
python oneshotfinetune.py --style_name ../data/oneshotstyle/1-train.png --supervise 0
Saved model can be found at ../save/
- Use
--help
to view more finetuning options
python oneshotfinetune.py --help
Contact
Shuai Yang