Awesome
pytorch-AdaIN
This is an unofficial pytorch implementation of a paper, Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization [Huang+, ICCV2017]. I'm really grateful to the original implementation in Torch by the authors, which is very useful.
Requirements
Please install requirements by pip install -r requirements.txt
- Python 3.5+
- PyTorch 0.4+
- TorchVision
- Pillow
(optional, for training)
- tqdm
- TensorboardX
Usage
Download models
Download decoder.pth / vgg_normalized.pth from release and put them under models/.
Test
Use --content and --style to provide the respective path to the content and style image.
CUDA_VISIBLE_DEVICES=<gpu_id> python test.py --content input/content/cornell.jpg --style input/style/woman_with_hat_matisse.jpg
You can also run the code on directories of content and style images using --content_dir and --style_dir. It will save every possible combination of content and styles to the output directory.
CUDA_VISIBLE_DEVICES=<gpu_id> python test.py --content_dir input/content --style_dir input/style
This is an example of mixing four styles by specifying --style and --style_interpolation_weights option.
CUDA_VISIBLE_DEVICES=<gpu_id> python test.py --content input/content/avril.jpg --style input/style/picasso_self_portrait.jpg,input/style/impronte_d_artista.jpg,input/style/trial.jpg,input/style/antimonocromatismo.jpg --style_interpolation_weights 1,1,1,1 --content_size 512 --style_size 512 --crop
Some other options:
--content_size: New (minimum) size for the content image. Keeping the original size if set to 0.--style_size: New (minimum) size for the content image. Keeping the original size if set to 0.--alpha: Adjust the degree of stylization. It should be a value between 0.0 and 1.0 (default).--preserve_color: Preserve the color of the content image.
Train
Use --content_dir and --style_dir to provide the respective directory to the content and style images.
CUDA_VISIBLE_DEVICES=<gpu_id> python train.py --content_dir <content_dir> --style_dir <style_dir>
For more details and parameters, please refer to --help option.
I share the model trained by this code as iter_1000000.pth at release.
References
- [1]: X. Huang and S. Belongie. "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.", in ICCV, 2017.
- [2]: Original implementation in Torch