fast-neural-style 🌇 🚀

This repository contains a pytorch implementation of an algorithm for artistic style transfer. The algorithm can be used to mix the content of an image with the style of another image. For example, here is a photograph of a door arch rendered in the style of a stained glass painting.

The model uses the method described in Perceptual Losses for Real-Time Style Transfer and Super-Resolution along with Instance Normalization. The saved-models for examples shown in the README can be downloaded from here.

Requirements

The program is written in Python, and uses pytorch, scipy. A GPU is not necessary, but can provide a significant speed up especially for training a new model. Regular sized images can be styled on a laptop or desktop using saved models.

Usage

Stylize image

python neural_style/neural_style.py eval --content-image </path/to/content/image> --model </path/to/saved/model> --output-image </path/to/output/image> --accel

--content-image: path to content image you want to stylize.
--model: saved model to be used for stylizing the image (eg: mosaic.pth)
--output-image: path for saving the output image.
--content-scale: factor for scaling down the content image if memory is an issue (eg: value of 2 will halve the height and width of content-image)
--accel: use accelerator

Train model

python neural_style/neural_style.py train --dataset </path/to/train-dataset> --style-image </path/to/style/image> --save-model-dir </path/to/save-model/folder> --epochs 2 --accel

There are several command line arguments, the important ones are listed below

--dataset: path to training dataset, the path should point to a folder containing another folder with all the training images. I used COCO 2014 Training images dataset [80K/13GB] (download).
--style-image: path to style-image.
--save-model-dir: path to folder where trained model will be saved.
--accel: use accelerator.

If --accel argument is given, pytorch will search for available hardware acceleration device and attempt to use it. This example is known to work on CUDA, MPS and XPU devices.

Refer to neural_style/neural_style.py for other command line arguments. For training new models you might have to tune the values of --content-weight and --style-weight. The mosaic style model shown above was trained with --content-weight 1e5 and --style-weight 1e10. The remaining 3 models were also trained with similar order of weight parameters with slight variation in the --style-weight (5e10 or 1e11).

Models

Models for the examples shown below can be downloaded from here or by running the script download_saved_models.py.