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

• Python 3.5+
• PyTorch 0.4+
• TorchVision
• Pillow

(optional, for training)

• tqdm
• TensorboardX

Usage

Download models

This command will download a pre-trained decoder as well as a modified VGG-19 network.

bash models/download_models.sh

Convert models

This command will convert the models for Torch to the models for PyTorch.

python torch_to_pytorch.py --model models/vgg_normalised.t7
python torch_to_pytorch.py --model models/decoder.t7

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 here

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