These scripts make use of a technique described in the paper Controlling Perceptual Factors in Neural Style Transfer by Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann and Eli Shechtman. While the multiscale effect with style transfer had already been discovered, it was only after the paper that people realized the significance of it.

The Multiscale Resolution technique (commonly called "multires") makes use of ever increasing image sizes to create larger stylized output images, without the problems that starting at such a large size would have. The idea has also been used with GANs to create even higher resolution images than ever before.

For the histogram matching scripts, you must first download the linear-color-transfer.py, and lum-transfer.py scripts from Neural-Tools:

wget -c https://raw.githubusercontent.com/ProGamerGov/Neural-Tools/master/linear-color-transfer.py

wget -c https://raw.githubusercontent.com/ProGamerGov/Neural-Tools/master/lum-transfer.py

The "seg" scripts require that you download the segmentation supporting modification of Neural-Style:

wget https://gist.githubusercontent.com/ProGamerGov/bcbd27a3d2e431adb73ef158d9990d93/raw/1e7ecd1c9aecaead0d92e3eb0027baa3fa2c4f31/neural_style_seg.lua

The "cp" scripts require that you download the channel pruning model from channel-pruning:

 wget https://github.com/yihui-he/channel-pruning/releases/download/channel_pruning_5x/channel_pruning.caffemodel
 
 wget https://github.com/yihui-he/channel-pruning/releases/download/channel_pruning_5x/channel_pruning.prototxt

By default these scripts likely won't produce good results, so you are encouraged to modify the script parameters to you liking. Though there are some things to keep in mind:

• For the step 1 image size, keep in mind that the amount of change is related to how close the image size is to the size of the images used to train the model (ex: 224, 512).

• As per Justin Johnson (creator of Neural-Style)'s suggestions, setting -tv_weight to 0 can improve results when using a multiscale resolution script like this. Though this dependent on what you want the output to look like.

• More steps seem to let smaller details develop more than when just simply using as few steps as possible.

• You can take the output from one of these scripts, and then use it at the input (by adding -init_image to step 1) used when running the same script again, in order to get forms/content from the style image to be more "complete" in the output image. You can then repeat this many times to enhance it's effect. The effect begins to diminish significantly after doing it over 5 times with an output image, according to my experiments. Starting with a style scale higher than 1 on the first script run, and then lowering it by 0.25 after in each subsequent run, can also greatly increase the detail on your final output image.

• When using -init image in steps after step 1, if you don't specify a seed value then the output seems to remain mostly the same. Specifying a seed value will make sure that the final output always looks the same.

• These scripts are designed to use jcjohnson's neural-style, neural-style-pt, and any similar style transfer scripts.

To run the scripts, you can use:

sh multires_style2content_hist.sh

sh multires_style2content.sh

sh multires_style2content_hist_large_cp.sh

sh multires_content2style.sh

sh multires_style2content_large.sh

sh multires_seg.sh

sh multires_seg_large_cp.sh

You can easily fix permission errors using chmod like this:

chmod u+x ./multires_style2content_hist.sh
chmod u+x ./multires_style2content_hist_large_cp.sh
chmod u+x ./multires_style2content.sh
chmod u+x ./multires_content2style.sh
chmod u+x ./multires_style2content_large.sh
chmod u+x ./multires_seg.sh
chmod u+x ./multires_seg_large_cp.sh
chmod u+x ./lum-transfer.py
chmod u+x ./linear-color-transfer.py