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https://github.com/williamyang1991/tet-gan
[AAAI 2019] TET-GAN: Text Effects Transfer via Stylization and Destylization
https://github.com/williamyang1991/tet-gan
aaai2019 gan style-transfer text-effect text-effects
Last synced: 4 days ago
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[AAAI 2019] TET-GAN: Text Effects Transfer via Stylization and Destylization
- Host: GitHub
- URL: https://github.com/williamyang1991/tet-gan
- Owner: williamyang1991
- License: mit
- Created: 2019-01-02T01:57:17.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2024-07-07T03:16:14.000Z (6 months ago)
- Last Synced: 2024-12-23T15:14:24.209Z (11 days ago)
- Topics: aaai2019, gan, style-transfer, text-effect, text-effects
- Language: Python
- Homepage:
- Size: 2.53 MB
- Stars: 95
- Watchers: 8
- Forks: 25
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# 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]](http://www.icst.pku.edu.cn/struct/Projects/TETGAN.html) | [[Paper]](https://arxiv.org/abs/1812.06384) | [[Dataset]](https://williamyang1991.github.io/projects/AAAI2019/TETGAN.html)
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]](https://drive.google.com/file/d/1pNOE4COeoXp_-N4IogNS-GavCBbZJtw1/view?usp=sharing) or [[Baidu Cloud]](https://pan.baidu.com/s/1yK6wM0famUwu25s1v92Emw) to `../save/`
- Style Transfer with default parameters
- Results can be found in `../output/`
```
python test.py
```
- Destylization with default parameters
```
python test.py --c2s 0
```
- Transfer the style of `26.jpg` onto the text image `2.png` and save the result as `26_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]](https://williamyang1991.github.io/projects/AAAI2019/TETGAN.html) 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]](https://drive.google.com/file/d/1pNOE4COeoXp_-N4IogNS-GavCBbZJtw1/view?usp=sharing) or [[Baidu Cloud]](https://pan.baidu.com/s/1yK6wM0famUwu25s1v92Emw) to `../save/`
- Specify the pretrained model to load using the option `--load_model_name`
- 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