How to make a bot that turns a photo into a comic book: step-by-step instructions for dummies

Part one, supplemented.
Cotans, hi.
I am Sasha and I indulge in neurons.

At the request of the workers, I finally gathered my thoughts and decided to gash a series of short and almost step-by-step instructions.

Instructions on how to train and deploy your neural network from scratch, at the same time making friends with the telegram bot.

Instructions for dummies like me.
Today we will choose the architecture of our neural network, test it and collect our first dataset for training.

Architecture choice

After the relatively successful launch of the selfie2anime bot (using the ready-made UGATIT model), I wanted to do the same, but mine. For example, a model that turns your photos into comics.

Here are some examples from my photo2comicsbot , and we will do something similar.

Since the UGATIT model was too heavy for my video card, I drew attention to an older, but less voracious analogy - CycleGAN

In this implementation, there are several model architectures and a convenient visual display of the learning process in the browser.

CycleGAN, like architectures for transferring styles across a single image, does not require paired images for training. This is important, because otherwise we would have to redraw all the photos ourselves into comics to create a training set.

The task that we will set for our algorithm consists of two parts.
At the output, we should get a picture that:

a) similar to a comic book
b) similar to the original image

Point β€œa” can be implemented using the usual GAN, where the trained Critic will be responsible for β€œresembling comics”.

More on GAN

The GAN, or Generative Adversarial Network, is a pair of two neural networks: Generator and Critic.
The generator converts the input, for example, from a photo to a comic book, and the critic compares the resulting β€œfake” result with a real comic book. The Generator's job is to trick the Critic, and vice versa.

In the learning process, the Generator learns to create comics that are increasingly similar to real ones, and the Critic learns to better distinguish between them.

The second part is somewhat more complicated. If we had paired pictures, where there would be photographs in set β€œA”, and in set β€œB” they were, but redrawn into comics (that is, what we want to get from the model), we could just to compare the result produced by the Generator with the paired image from set β€œB” of our training set.

In our case, sets β€œA” and β€œB” are in no way connected with each other. In set β€œA” - random photos, in set β€œB” - random comics.

It is pointless to compare a fake comic with some random comic from set β€œB”, since this will at least duplicate the function of the Critic, not to mention the unpredictable result.

This is where the CycleGAN architecture comes to the rescue.

In short, this is a GAN pair, the first of which converts an image from category β€œA” (for example, a photo) to category β€œB” (for example, a comic book), and the second one back, from category β€œB” to category β€œA”.

Models are trained both on the basis of comparing the original photo with the restored one (as a result of the cycle β€œA” - β€œB” - ”A”, β€œphoto-comic-photo), and the data of the Critics, as in a regular GAN.

This makes it possible to complete both parts of our task: to generate a comic book that is indistinguishable from other comics, and at the same time resembles the original photo.

Model installation and verification

To implement our cunning plan, we need:

  • CUDA-enabled graphics card with 8GB RAM
  • Linux OS
  • Miniconda / Anaconda with Python 3.5+

Video cards with less than 8GB of RAM can also work if you conjure with the settings. It will also work on Windows, but more slowly, I had a difference of at least 1.5-2 times.

If you don’t have a GPU with CUDA support, or you are too lazy to set it all up, you can always use Google Colab. If there is a sufficient number of people who want to, I’ll fill out the tutorial and how to crank up all of the following in a Google cloud.

Miniconda can be taken here
Installation instructions

After installing Anaconda / Miniconda (hereinafter referred to as conda), create a new environment for our experiments and activate it:

(Windows users need to start Anaconda Prompt first from the Start menu)

conda create --name cyclegan conda activate cyclegan 

Now all packages will be installed in the active environment without affecting the rest of the environment. This is convenient if you need certain combinations of versions of various packages, for example, if you are using someone else's old code and you need to install obsolete packages without spoiling your life and main working environment.

Next, simply follow the README.MD instructions from the distribution:

Save the CycleGAN distribution:

(or just download the archive from GitHub)

 git clone cd pytorch-CycleGAN-and-pix2pix 

Install the necessary packages:

 conda install numpy pyyaml mkl mkl-include setuptools cmake cffi typing conda install pytorch torchvision -c pytorch conda install visdom dominate -c conda-forge 

Download the finished dataset and the corresponding model:

 bash ./datasets/ horse2zebra bash ./scripts/ horse2zebra 

Pay attention to what photos are in the downloaded dataset.

If you open the script files from the previous paragraph, you can see that there are other ready-made datasets and models for them.

Finally, test the model on the downloaded dataset:

 python --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout 

The results will be saved in the / results / horse2zebra_pretrained / folder

Creating a training set

An equally important step after choosing the architecture of the future model (and searching for a finished implementation on github) is to compile a dataset, or a data set, on which we will train and test our model.
Almost everything depends on what data we use. For example, UGATIT for the selfie2anime bot was trained on female selfies and female faces from anime. Therefore, with male photos, she behaves at least funny, replacing brutal bearded men with little girls with a high collar. In the photo, your humble servant after he learned that he is watching an anime.

As you already understood, it is worth selecting those photos / comics that you want to use at the input and get at the output. Are you planning to process selfies - add selfies and close-ups of faces from comics, photos of buildings - add photos of buildings and pages from comics with buildings.
As sample photos, I used DIV2K and Urban100 , flavored with photos of Google stars to enhance the diversity.

I took comics from the Marvel universe, the entire page, throwing out ads and announcements where the picture does not look like a comic book. I can’t attach the link for obvious reasons, but at the request of Marvel Comics you can easily find scanned options on your favorite sites with comics, if you know what I mean.

It is important to pay attention to the drawing, it differs in different series, and the color scheme.

I had a lot of deadpool and spiderman, so the skin goes very red.

An incomplete list of other public datasets can be found here .

The folder structure in our dataset should be as follows:

β”œβ”€β”€ trainA
β”œβ”€β”€ trainB
β”œβ”€β”€ testA
└── testB

trainA - our photos (about 1000pcs)
testA - some photos for model tests (30pcs will be enough)
trainB - our comics (about 1000 pcs.)
testB - comics for tests (30pcs.)

It is advisable to place the dataset on an SSD, if possible.

That's all for today, in the next issue we will begin to train the model and get the first results!

Be sure to write if something went wrong with you, this will help improve leadership and ease the suffering of subsequent readers.

If you have already tried to train the model, feel free to share the results in the comments. See you soon!

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