2022-AdaIN-pytorch
This is an unofficial Pytorch implementation of the paper, Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization, ICCV 2017 arxiv. I referred to the official implementation in Torch. I used pretrained weights of vgg19 and decoder from naoto0804.
Requirements
Install requirements by $ pip install -r requirement.txt
- Python 3.7+
- PyTorch 1.10
- Pillow
- TorchVision
- Numpy
- imageio
- tqdm
Usage
Training
The encoder uses pretrained vgg19 network. Download the vgg19 weight. The decoder is trained on MSCOCO and wikiart dataset. Run the script train.py
$ python train.py --content_dir $CONTENT_DIR --style_dir STYLE_DIR --cuda
usage: train.py [-h] [--content_dir CONTENT_DIR] [--style_dir STYLE_DIR]
[--epochs EPOCHS] [--batch_size BATCH_SIZE] [--resume RESUME] [--cuda]
optional arguments:
-h, --help show this help message and exit
--content_dir CONTENT_DIR
content images folder path
--style_dir STYLE_DIR
style images folder path
--epochs EPOCHS Number of epoch
--batch_size BATCH_SIZE
Batch size
--resume RESUME Continue training from epoch
--cuda Use CUDA
Test Image Style Transfer
Download vgg19 weight, decoder weight under the main directory.
To test basic style transfer, run the script test_image.py. Specify --content_image, --style_img to the image path, or specify --content_dir, --style_dir to iterate all images under this directory. All outputs are saved in ./results/. Specify --grid_pth to collect all outputs in a grid image.
$ python test.py --content_image $IMG --style_image $STYLE --cuda
optional arguments:
-h, --help show this help message and exit
--content_image CONTENT_IMAGE
single content image file
--content_dir CONTENT_DIR
content image directory, iterate all images under this directory
--style_image STYLE_IMAGE
single style image
--style_dir STYLE_DIR
style image directory, iterate all images under this directory
--decoder_weight DECODER_WEIGHT decoder weight file (default='decoder.pth')
--alpha {Alpha Range}
Alpha [0.0, 1.0] controls style transfer level
--cuda Use CUDA
--grid_pth GRID_PTH
Specify a grid image path (default=None) if generate a grid image
that contains all style transferred images
Test Image Interpolation Style Transfer
To test style transfer interpolation, run the script test_interpolate.py. Specify --style_image with multiple paths separated by comma. Specify --interpolation_weights to interpolate once. All outputs are saved in ./results_interpolate/. Specify --grid_pth to interpolate with different built-in weights and provide 4 style images.
$ python test_interpolation.py --content_image $IMG --style_image $STYLE $WEIGHT --cuda
optional arguments:
-h, --help show this help message and exit
--content_image CONTENT_IMAGE
single content image file
--style_image STYLE_IMAGE
multiple style images file separated by comma
--decoder_weight DECODER_WEIGHT decoder weight file (default='decoder.pth')
--alpha {Alpha Range}
Alpha [0.0, 1.0] (default=1.0) controls style transfer level
--interpolation_weights INTERPOLATION_WEIGHTS
Interpolation weight of each style image, separated by comma.
Do not specify if input grid_pth.
--cuda Use CUDA
--grid_pth GRID_PTH
Specify a grid image path (default=None) to perform interpolation style
transfer multiple times with different built-in weights and generate a
grid image that contains all style transferred images. Provide 4 style
images. Do not specify if input interpolation_weights.
Test Video Style Transfer
To test video style transfer, run the script test_video.py. All outputs are saved in ./results_video/.
$ python test_video.py --content_video $VID --style_image $STYLE --cuda
optional arguments:
-h, --help show this help message and exit
--content_video CONTENT_IMAGE
single content video file
--style_image STYLE_IMAGE
single style image
--decoder_weight DECODER_WEIGHT decoder weight file (default='decoder.pth')
--alpha {Alpha Range}
Alpha [0.0, 1.0] controls style transfer level
--cuda Use CUDA
Examples
Basic Style Transfer
Different levels of style transfer
Interpolation Style Transfer
Video Style Transfer
Original Video
Style Image
Style Transfer Video
References
- X. Huang and S. Belongie. "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.", in ICCV, 2017. arxiv
- Original implementation in Torch
- Pretrained weights
- List of all source URLs of images collected from the internet. Image_sources.txt