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
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 
*Warning : Due to slow computation speed of colab, it may take several minutes in colab environment
Before training, plase download DIV2K dataset LINK.
We recomment 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_path $DIV2K_DIR$ \
--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