Awesome
ASMA-GAN
Anisotropic Stroke Control for Multiple Artists Style Transfer
Proceedings of the 28th ACM International Conference on Multimedia
The official repository with Pytorch
Top News <img width=8% src="./doc/img/new.gif"/>
2021-09-25: Training related code has been released.
Methodology
Dependencies
- python3.6+
- pytorch1.5+
- torchvision
- pyyaml
- paramiko
- pandas
- requests
- tensorboard
- tensorboardX
- tqdm
Installation
We highly recommend you to use Anaconda for installation
conda create -n ASMA python=3.6
conda activate ASMA
conda install pytorch==1.5.0 torchvision==0.6.0 cudatoolkit=10.1 -c pytorch
pip install pyyaml paramiko pandas requests tensorboard tensorboardX tqdm
Preparation
- Training dataset
- Download the content images from Places365 .
- Download the artist images data_art_backup.zip from Github Releases. Thanks for the data in StyleAware.
- pre-trained model
- Download the model from Github Releases, and unzip the files to ./train_logs/
Usage
To test with pretrained model
The command line below will generate 1088*1920 HD style migration pictures of 11 painters for each picture of testImgRoot (11 painters include: Berthe Moriso , Edvard Munch, Ernst Ludwig Kirchner, Jackson Pollock, Wassily Kandinsky, Oscar-Claude Monet, Nicholas Roerich, Paul Cézanne, Pablo Picasso ,Samuel Colman, Vincent Willem van Gogh. The output image(s) can be found in ./test_logs/ASMAfinal/
-
Example of style transfer with all 11 artists style
python main.py --mode test --cuda 0 --version ASMAfinal --dataloader_workers 8 --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle -1 -
Example of style transfer with Pablo Picasso style
python main.py --mode test --cuda 0 --version ASMAfinal --dataloader_workers 8 --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle 8 -
Example of style transfer with Wassily Kandinsky style
python main.py --mode test --cuda 0 --version ASMAfinal --dataloader_workers 8 --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle 4
--version refers to the ASMAGAN training logs name.
--testImgRoot can be a folder with images or the path of a single picture.You can assign the image(s) you want to perform style transfer to this argument.
--specify_sytle is used to specify which painter's style is used for style transfer. When the value is -1, 11 painters' styles are used for image(s) respectively for style transfer. The values corresponding to each painter's style are as follows [0: Berthe Moriso, 1: Edvard Munch, 2: Ernst Ludwig Kirchner, 3: Jackson Pollock, 4: Wassily Kandinsky, 5: Oscar-Claude Monet, 6: Nicholas Roerich, 7: Paul Cézanne, 8: Pablo Picasso, 9 : Samuel Colman, 10: Vincent Willem van Gogh]
Training
To train your own model, first change the dataset path in ./env/config.json.
Then use:
python main.py --mode train --cuda 0 --dataloader_workers 12 --version $(your experiment name) --trainYaml train.yaml
Change the training parameters in ./train_configs/train.yaml.
To cite our paper
@inproceedings{DBLP:conf/mm/ChenYLQN20,
author = {Xuanhong Chen and
Xirui Yan and
Naiyuan Liu and
Ting Qiu and
Bingbing Ni},
title = {Anisotropic Stroke Control for Multiple Artists Style Transfer},
booktitle = {{MM} '20: The 28th {ACM} International Conference on Multimedia, 2020},
publisher = {{ACM}},
year = {2020},
url = {https://doi.org/10.1145/3394171.3413770},
doi = {10.1145/3394171.3413770},
timestamp = {Thu, 15 Oct 2020 16:32:08 +0200},
biburl = {https://dblp.org/rec/conf/mm/ChenYLQN20.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Some Results
Related Projects
Learn about our other projects [RainNet], [Sketch Generation], [CooGAN], [Knowledge Style Transfer], [SimSwap],[ASMA-GAN],[Pretrained_VGG19].