The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

Related tags

Deep LearningASMAGAN
Overview

ASMA-GAN

Anisotropic Stroke Control for Multiple Artists Style Transfer

Proceedings of the 28th ACM International Conference on Multimedia

The official repository with Pytorch

[Arxiv paper]

logo

title

Methodology

Framework

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

  • Traning dataset
    • Coming soon
  • 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

Coming soon

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

Results1

Related Projects

Learn about our other projects [RainNet], [Sketch Generation], [CooGAN], [Knowledge Style Transfer], [SimSwap],[ASMA-GAN],[Pretrained_VGG19].

High Resolution Results

Comments
  • Can't download pre-trained model

    Can't download pre-trained model

    Hi! Could you please check your pre-trained model. The follow links is no found. Thank you https://github.com/neuralchen/ASMAGAN/releases/download/v.1.0/ASMAfinal.zip

    opened by namdn 5
  • Thank you for your great project. When will the training code be released

    Thank you for your great project. When will the training code be released

    Thank you for your great project.

    1. When will the training code be released.
    2. I want to get more painters how do I do that, how do I make the training datasets, how much data do I need
    3. Looking forward to your reply
    opened by zhanghongyong123456 5
  • Fine Tuning for single class

    Fine Tuning for single class

    Hello team, I would like to finetune your pretrained model for just five new class (total output will be five), how should I use the finetune? Thank you!

    opened by minhtcai 0
  • KeyError 1920

    KeyError 1920

    using the official command: python main.py --mode test --cuda 0 --version ASMAfinal --dataloader_workers 8 --testImgRoot ./bench/ --nodeName localhost --checkpoint 350000 --testScriptsName common_useage --specify_sytle 8

    then error happened Generator Script Name: Conditional_Generator_asm 11 classes Finished preprocessing the test dataset, total image number: 25... /home/ama/anaconda3/envs/ASMA/lib/python3.9/site-packages/torchvision/transforms/transforms.py:332: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum. warnings.warn( Traceback (most recent call last): File "/home/ama/ASMAGAN/main.py", line 266, in tester.test() File "/home/ama/ASMAGAN/test_scripts/tester_common_useage.py", line 50, in test test_data = TestDataset(test_img,batch_size) File "/home/ama/ASMAGAN/data_tools/test_data_loader_resize.py", line 36, in init transform.append(T.Resize(1088,1920)) File "/home/ama/anaconda3/envs/ASMA/lib/python3.9/site-packages/torchvision/transforms/transforms.py", line 336, in init interpolation = _interpolation_modes_from_int(interpolation) File "/home/ama/anaconda3/envs/ASMA/lib/python3.9/site-packages/torchvision/transforms/functional.py", line 47, in _interpolation_modes_from_int return inverse_modes_mapping[i] KeyError: 1920

    opened by Kayce001 1
  • Change aspect ratio of images

    Change aspect ratio of images

    test code change aspect ratio of input images so output images are deformed to fix this i make some correction at "test_data_loader_resize.py"

    image

    opened by birolkuyumcu 0
  • RuntimeError: cuDNN

    RuntimeError: cuDNN

    Hi I get the following error when running the code:

    RuntimeError: cuDNN error: CUDNN_STATUS_EXECUTION_FAILED when calling backward()

    I would appreciate your help on how to resolve this.

    Thank you!

    Gero

    opened by Limbicnation 8
Releases(v.1.1)
Owner
Six_God
Six_God
GitHub Repository
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