neural-style: Neural Style in Pytorch! 🎨

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 inspired by the implementations of:

Anish Athalye: Neural Style in Tensorflow,
Justin Johnson: Neural Style in Torch, and
ProGamerGov: Neural Style in PyTorch

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

Examples: Style Transfer

Catriona Gray and Woman I by Willem de Kooning

Janelle Monae and Starry Night by Vincent van Gogh

Andrew Y. Ng and Oil Painting of a Girl in Rain

Style Transfers of Golden Bridge

Some Old Man + Increasing Style Weights of Starry Night

Examples: Style Transfer while Preserving the original color

Janelle Monae and Starry Night by Vincent van Gogh + Preserve Original Color

Requirements

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

Data Files

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

Dependecies

Usage

If you don't have a GPU, you may want to run the notebook in Google Colab! Colab is a cloud-GPU service with an interface similar to Jupyter notebook. A separate instruction is included to get started with Colab.

Local GPU

After installing the dependencies, run models/download_model.sh script to download the pretrained VGG19 weights.

sh models/download_models.sh

Codes are implemented inside the neural_style.ipynb notebook. Jupyter notebook environment is needed to run notebook.

jupyter notebook

Google Colab

The included notebook file is a Google-Colab-ready notebook! 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://web.eecs.umich.edu/~justincj/models/vgg19-d01eb7cb.pth
!mkdir models
!cp vgg19-d01eb7cb.pth models/

# Download Images
!wget -c https://github.com/iamRusty/neural-style-pytorch/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

Options

Image

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

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

Model

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'

Todo!

Multiple Style blending
High-res Style Transfer
Color-preserving Style Transfer

rrmina/neural-style-pytorch