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CLIPstyler

Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition" (CVPR 2022 Accepted)

Gihyun Kwon, Jong Chul Ye

LINK : https://arxiv.org/abs/2112.00374

MAIN3_e2-min

Cite

@article{kwon2021clipstyler,
  title={Clipstyler: Image style transfer with a single text condition},
  author={Kwon, Gihyun and Ye, Jong Chul},
  journal={arXiv preprint arXiv:2112.00374},
  year={2021}
}

Environment

Pytorch 1.7.1, Python 3.6

$ conda create -n CLIPstyler python=3.6
$ conda install --yes -c pytorch pytorch=1.7.1 torchvision cudatoolkit=11.0
$ pip install ftfy regex tqdm
$ conda install -c anaconda git
$ pip install git+https://github.com/openai/CLIP.git

Style Transfer with Single-image

We provide demo with replicate.ai

To train the model and obtain the image, run

python train_CLIPstyler.py --content_path ./test_set/face.jpg \
--content_name face --exp_name exp1 \
--text "Sketch with black pencil"

To change the style of custom image, please change the --content_path argument

edit the text condition with --text argument

For easy demo, we provide Google Colab Open In Colab.

*Warning : Due to slow computation speed of colab, it may take several minutes in colab environment

Fast Style Transfer

Before training, plase download DIV2K dataset LINK.

We recommend to use Training data of High-Resolution(HR) images.

To train the model, please download the pre-trained vgg encoder & decoder models in LINK.

Please save the downloaded models in ./models directory

Then, run the command

python train_fast.py --content_dir $DIV2K_DIR$ \
--name exp1 \
--text "Sketch with black pencil" --test_dir ./test_set

Please set the $DIV2K_DIR$ as the directory in which DIV2K images are saved.

To test the fast style transfer model,

python test_fast.py --test_dir ./test_set --decoder ./model_fast/clip_decoder_iter_200.pth.tar

Change the argument --decoder to other trained models for testing on different text conditions.

We provide several fine-tuned decoders for several text conditions. LINK

To use high-resolution image, please add --hr_dir ./hr_set to test command.

We provide colab notebook for testing fast transfer model Open In Colab

Style interpolation on Fast Style Transfer

Style interpolation results with interpolating weight parameters of two fine-tuned decoder models

To interpolate the fast style transfer model,

python test_intp.py --decoder_src $SOURCE_DECODER --decoder_trg $TARGET_DECODER

Put source and target decoder model paths in $SOURCE_DECODER and $TARGET_DECODER

Style interpolation example with interpolating two styles "Stone wall" and "Desert sand"

interp_style

Video style transfer with Fast model

For video style transfer, first install video io package

$ pip install imageio-ffmpeg
$ conda install -c conda-forge/label/cf202003 opencv

Then run the following command,

python test_video.py --content_path $VIDEO_PATH$ --decoder $DECODER_PATH$