Fast Neural Style Transfer by PyTorch (Mac OS)

2021-Jan-31: The git repo has been upgraded from PyTorch-0.3.0 to PyTorch-1.7.0. with Python=3.8.3.

Continue my last post Image Style Transfer Using ConvNets by TensorFlow (Windows), this article will introduce the Fast Neural Style Transfer by PyTorch on MacOS.

The original program is written in Python, and uses [PyTorch], [SciPy]. A GPU is not necessary but can provide a significant speedup especially for training a new model. Regular sized images can be styled on a laptop or desktop using saved models.

More details about the algorithm could be found in the following papers:

  1. Perceptual Losses for Real-Time Style Transfer and Super-Resolution (2016).
  2. Instance Normalization: The Missing Ingredient for Fast Stylization (2017).

If you could not download the papers, here are the Papers.

You can find all the source code and images (updated in 2021) at my GitHub: fast_neural_style .

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Image Style Transfer Using ConvNets by TensorFlow (Windows)

This post is talking about how to setup a basic developing environment of Google’s TensorFlow on Windows 10 and apply the awesome application called “Image style transfer”, which is using the convolutional neural networks to create artistic images based on the content image and style image provided by the users.

The early research paper is “A Neural Algorithm of Artistic Style” by Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge on arXiv. You could also try their website application on DeepArt where there are a lot of amazing images uploaded by the people all over the world. DeepArt has apps on Google Play and App Store, but I suggest you use a much faster app Prisma, which is as awesome as DeepArt! [More to read: Why-does-the-Prisma-app-run-so-fast].

Moreover, I strongly recommend the formal paper Image Style Transfer Using Convolutional Neural Networks by the same authors published on CVPR-2016. This paper gives more details about how this method works. In short, the following two figures cover the idea behind the magic.

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Sharing the opinion about Generative Adversarial Networks (GAN)

Generative models, like Generative Adversarial Networks (GAN),  are a rapidly advancing area of research for computer science and machine intelligence nowadays. It’s hard to keep track of them all, not to mention the incredibly creative ways in which researchers have achieved and been working on.

The following figures demonstrate some results of the current works ( Images from

GAN learning to generate images (linear time)

VAE learning to generate images (log time)

I think it is necessary to understand the basic pros and cons of it, and it may be very helpful to your own research. I have not fully reviewed the theory and papers, but after skimmed a few papers, I got the impression that the training process of GAN models is very tricky as well as any neural networks model. Thus, there must be a huge improving space for people to make.

Thanks to the internet!  There are papers and codes everywhere and nobody will be left behind in these days unless he/she wants to.  So working hard and to be a better man (or women or anything good for humanity), cheers!

Here are some papers and blogs that summarized the literature very well.

Here is my old group slide meeting note and download links.

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