Easy to run Neural Style Transfer for a given content and style images.
I used it create a wedding gift!
Neural Style Transfer Gatys, 2015 is a ML technique in which a network is trained to generate an output image g from a content image c (e.g. a photograph) which resembles a style image s (e.g. Picasso painting).
Two prerequsities for a high-level understanding.
Firstly, VGG (Simonyan, 2015)[https://arxiv.org/abs/1409.1556] is a very large convolutional architecture trained on a benchmark image recognition task. As a result, the architecture learns how to pick up features of objects and can be used for transfer learning.
Secondly, last layers of convolutional architectures pick up high-level objects (like faces, buildings etc., the actual important bits in the picture).
In Gatys, 2015, both c and s are passed through VGG to create internal representations of the images. The loss function of the architecture then combines two parts - content and style loss.
Content loss measures how similar last layers of g and c are. The loss is 0 if they are exactly the same.
On the other hand, style loss measures how similar all activations of s and g are once passed through VGG.
You see these two losses are kind of pulling the way g will like in different directions (more like content or more like style). Since the final loss is a weighted combination of the two losses, by manipulating the weights you can shape the way the final image g will look like.
conda env create -f environment.yml
By default it runs using content-image.jpg and picasso.jpg, change --style parameter to a new of a style jpg file to change the style, play around with weights to manipulate changes to the input image.
python main.py --style lempicka --num_steps 10 --style_weight 10000000
Credits to Alexis Jackq.