This repository includes:

1. A library of building blocks for state-of-the-art image synthesis.
2. Reference implementations of popular deep learning algorithms.

In the examples folder, you'll find a documented implementation of neural style transfer based on the following:

• A Neural Algorithm of Artistic Style, Gatys et al. 2015.
• Improving the Neural Algorithm of Artistic Style, Novak & Nikulin, 2016.
• Stable and Controllable Neural Synthesi, Risser et al, 2017.

python examples/iterative.py --style texture.png --output-size 256x256 --output generated1.png 

python examples/iterative.py --content image.png --output generated2.png

python examples/iterative.py --content image.png --style texture.png --output generated3.png

You will likely need to experiment with the default options to obtain good results:

• --scales=N: Coarse-to-fine rendering with downsampled images.
• --iterations=N: Number of steps to run the optimizer at each scale.
• --style-layers A B C D: Specify convolution layers of VGG19 manually, by default 1 6 11 20 29 for relu*_1.
• --style-weights a b c d: Override loss weights for style layers, by default 1.0 for each.
• --content-layers E F: Specify convolution layers of VGG19 manually, by default 20 for relu4_1.
• --content-weights e f: Override loss weight for content layers, by default 1.0.
• --seed image.png: Provide a starting image for the optimization.