IMPORTANT NOTE: On Google-Colab All data files and dependencies can be installed by running the uppermost cell of the notebook! See Usage!

• Pre-trained VGG19 network weights - put it in models/ directory
• torchvision - torchvision.models contains the VGG19 model skeleton

• PyTorch
• NumPy
• Jupyter
• opencv2
• Copy

You will be running the notebook in Google Colab! Colab is a cloud service with an interface similar to Jupyter notebook. It allows you to get an accelerator, such as a GPU, for Free!

Uncomment and run the first cell to download the demo pictures, and VGG19 weights. It will also install the dependencies (i.e. PyTorch and torchvision).

# Download VGG19 Model
!wget -c https://s3-us-west-2.amazonaws.com/jcjohns-models/vgg19-d01eb7cb.pth
!mkdir models
!cp vgg19-d01eb7cb.pth models/

# Download Images
!wget -c https://github.com/ofirbb/spectrum-workshop/archive/master.zip
!unzip -q master.zip
!mkdir images
!cp neural-style-pytorch-master/images/1-content.png images
!cp neural-style-pytorch-master/images/1-style.jpg images

• MAX_IMAGE_SIZE: sets the max dimension of height or weight. Bigger GPU memory is needed to run larger images. Default is 512px.
• INIT_IMAGE: sets the initial image file to either 'random' or 'content'. Default is random which initializes a noise image. Content copies a resized content image, giving free optimization of content loss!
• CONTENT_PATH: path of the content image
• STYLE_PATH: path of the style image
• PRESERVE_COLOR: determines whether to preserve the color of the content image. True preserves the color of the content image. Default value is False
• PIXEL_CLIP: determines whether to clip the resulting image. True clips the pixel values to [0, 255]. Default value is True

• OPTIMIZER: sets the optimizer to either 'adam' or 'lbfgs'. Default optimizer is Adam with learning rate of 10. L-BFGS was used in the original (matlab) implementation of the reference paper.
• ADAM_LR: learning rate of the adam optimizer. Default is 1e1
• CONTENT_WEIGHT: Multiplier weight of the loss between content representations and the generated image. Default is 5e0
• STYLE_WEIGHT: Multiplier weight of the loss between style representations and the generated image. Default is 1e2
• TV_WEIGHT: Multiplier weight of the Total Variation Denoising. Default is 1e-3
• NUM_ITER: Iterations of the style transfer. Default is 500
• SHOW_ITER: Number of iterations before showing and saving the generated image. Default is 100

• VGG19_PATH = path of VGG19 Pretrained weights. Default is 'models/vgg19-d01eb7cb.pth'
• POOL: Defines which pooling layer to use. The reference paper suggests using average pooling! Default is 'max'

An implementation of the neural style in PyTorch! This notebook implements Image Style Transfer Using Convolutional Neural Networks by Leon Gatys, Alexander Ecker, and Matthias Bethge. Color preservation/Color transfer is based on the 2nd approach of discussed in Preserving Color in Neural Artistic Style Transfer by Leon Gatys, Matthias Betge, Aaron Hertzmann, and Eli Schetman.

This implementation is a slight adaptation of the code by Rusty.

The original caffe pretrained weights of VGG19 were used for this implementation, instead of the pretrained VGG19's in PyTorch's model zoo.