Style Transfer GANs (Generative Adversarial Networks)

Style Transfer Generative Adversarial Networks take two images and apply the style from one image to the other image. Here are some sample results from here.

Style Transfer GAN examples

For a more technical explanation of how these work, you can refer to the following papers;

Image Style Transfer Using Convolutional Neural Networks
Artistic style transfer for videos
Preserving Color in Neural Artistic Style Transfer

Ever since first seeing this technique I wanted to add it as an image processing option within Visions of Chaos.

If you only want to play around with style transfer or only have a few photos you want to experiment with, then I recommend you use an online service like DeepArt because this can be a tedious process to setup and use on your own PC.

GPU with Cuda support

The methods in this blog will run without a graphics card processor (GPU) but are very slow using only the CPU (ten minutes for a tiny image with few iterations, hours for larger sizes with many iterations).

For fast results you need a Nvidia graphics card that supports Cuda. Check the list here. On board GPUs are not supported. AMD Radeon GPUs are not supported. You need Nvidia.

If you do not have a supported Nvidia graphics card you can continue to get the CPU supported version going if you are very patient and/or a masochist.

For these steps I made a new C:\STGAN\ folder for all the downloads, so that is what you will see referenced in the steps and screenshots.


Python logo

Download the latest version of Python from here.

Install Python. NOTE: you must check the “Add Python to PATH” checkbox on the first Python installer screen.


TensorFlow Logo

TensorFlow is a machine learning platform developed by Google.

To get TensorFlow support in Python type the following inside a command prompt window

pip install --no-cache-dir --ignore-installed --upgrade --force-reinstall tensorflow
pip install --no-cache-dir --ignore-installed --upgrade --force-reinstall tensorflow-gpu

Those commands add support for both CPU and GPU tensorflow


pip install --no-cache-dir --ignore-installed --upgrade --force-reinstall scipy


pip install --no-cache-dir --ignore-installed --upgrade --force-reinstall opencv-python


Now for the actual Python program that handles the style transfer.

Download and extract neural-style-tf (for this example I used the C:\STGAN\neural-style-tf-master\ folder )

Download this model and put it into the extracted neural-style-tf-master directory.

Change into the neural-style-tf-master folder with the command prompt

Now test each of these import lines (from one at a time to verify everything is OK
To do that just type “python” in your terminal and press enter (restart your terminal first if you haven’t after you finished installing everything). The line should now start with “>>>” instead of the directory. Copy and paste the following commands (you can copy paste them all at once to save typing them one at a time) into the Python prompt.

import tensorflow as tf
import numpy as np
import argparse
import struct
import errno
import time
import cv2
import os

They should come back without errors, ie

Python test

Test Run

Here we go! If you got to here then it is time to do a quick (slow) CPU test run.

Go into a command prompt under the folder and run the following command

python --content_img golden_gate.jpg --style_imgs starry-night.jpg --max_size 1000 --max_iterations 100 --print_iterations 1 --original_colors --device /cpu:0 --verbose

You will see various stats and then after some time (on a not so new PC this took 15 minutes) you will see it finish.

You should then see the output under the C:\STGAN\neural-style-tf-master\image_output\ directory.

If you got to here then it is mostly working. The next step is to get GPU support working so the processing times can be much faster.

NVidia CUDA and cuDNN

Waiting 15 minutes per image really tests the patience. If you have a newer GPU then it can be used to speed up the calculations. Firstly you need to download the various support tools and drivers.

Nvidia logo

Download the latest version of CUDA from here and install it.

NOTE: by default the Nvidia installer wants to install extra drivers etc, you only need the libraries option checked, ie


Download the latest cuDNN from here.

You do have to register, but if you do not want to use your real name and email to register, use a fake name and 10 minute mail to get the verification email.

Extract the cuDNN zip to a temp folder and then copy the cudnn64_6.dll (or possibly cudnn64_7.dll) into C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin\


Re-run the test command (note that it now specifies GPU device to use). Also note that the max_size is small here. Larger sizes need more GPU memory and power and may fail, so best to start with a small sized image as a test.

python --content_img golden_gate.jpg --style_imgs starry-night.jpg --max_size 250 --max_iterations 100 --print_iterations 1 --original_colors --device /gpu:0 --verbose

and you should see it is MUCH faster.

If your GPU is not supported or it does not run, you are stuck with CPU so roll back to CPU only support,

pip3 uninstall tensorflow-gpu
pip3 install --no-cache-dir --ignore-installed --upgrade tensorflow

Not all errors mean you cannot get GPU support. Read the output messages and look for any hints on what went wrong. Google error messages. If at all possible you do want to have GPU support for Style Transfer.

Style Transfer in Visions of Chaos

If you made it this far you can now experiment with style tranfer GANs in Visions of Chaos. I have added some basic wrapper code that executes the python command to apply style transfer to any fractal or other image you can create.

Generate any image, then select Image->Image Processing->Style Transfer.

Visions of Chaos Style Transfer GAN

Start with smaller image sizes to get an idea of how long the process will take on your system before going for larger sized images.

You can also select any external image file to apply the style transfer to. So dig out those cat photos and have fun. Note that if you get tired of the limited style images that come with neural-style-tf you can put any image you like under the styles folder and use those. Grab an image of your favorite artist’s works and experiment.

For some examples I used the following photo of Miss Marple.

Miss Marple

And applied some various transfer style images.

MC Escher Plane Filling II

Miss Marple Style Transfer GAN

A Mandelbrot fractal

Miss Marple Style Transfer GAN

Another Mandelbrot fractal

Miss Marple Style Transfer GAN

HR Giger Biomechanical Landscape

Miss Marple Style Transfer GAN

Kandinsky Composition VII

Miss Marple Style Transfer GAN


Miss Marple Style Transfer GAN


Miss Marple Style Transfer GAN

Picasso Les Femmes d’Alger

Miss Marple Style Transfer GAN

Picasso Seated Nude

Miss Marple Style Transfer GAN

Hokusai The Great Wave off Kanagawa

Miss Marple Style Transfer GAN

Munch The Scream

Miss Marple Style Transfer GAN

Turner The Wreck of a Transport Ship

Miss Marple Style Transfer GAN

van Gogh Starry Night

Miss Marple Style Transfer GAN


If you get a failed style transfer and an error message, here are a few things to try;
1. Smaller image size. Depending on the RAM in your PC and GPU you may have maxed out.
2. Reboot. Seems to always fix a stubborn error for me. The Cuda and/or cuDNN seem to be the main culprit. They get hung or locked or something and only a reboot will get them working again.


One response to “Style Transfer GANs (Generative Adversarial Networks)

  1. Thanks for your explanations .
    i checked notify for new comments.
    and many thanks for voc, very good program, i’ll use it to explain things to students.have a nice day .

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