Shipra Saxena — Published On August 26, 2020 and Last Modified On August 27th, 2020
Computer Vision Deep Learning Intermediate Listicle Python


  • GANs are generative models, they create what you feed them.
  • We have listed down 4 prominent GAN Libraries



Currently, GAN is being considered as one of the most exciting research areas in the computer vision. Its prowess to process images is uncomparable and being a data scientist, not exploring it would be a blunder.  Even eminent people like Yann LeCun described GANs as ” the most interesting idea in machine learning in the last 10 years”.

GAN Libraries

When I worked with GAN for the first time, I developed it from scratch using PyTorch and it was indeed a tedious task. It becomes even more difficult when the output is not satisfactory and you want to try another architecture as now you have to rewrite the code. But fortunately, researchers working in various technical giants have developed several GAN libraries to help you out in exploring and developing GAN based applications.

In this article, we are going to see 4 interesting GAN libraries you should definitely know about. Also, I will be giving you an overview of GANs, to begin with

I recommend you to check out our comprehensive Computer Vision Program to get started in this field.


Table of contents

  • A Quick Overview of GANs
  • GAN Libraries
  • TF-GAN
  • Torch-GAN
  • Mimicry
  • IBM- GAN toolkit


A Quick Overview of  GANs

GANs was introduced by Ian Good Fellow in 2014 and is a state of the art deep learning method. It is a member of the Generative Model family that goes through adversarial training.

Generative Modeling is a powerful method where the network learns the distribution of the input data and tries to generate the new data point based on similar distribution. If we look at the examples of Generative models we have Auto Encoders, Boltzmann machines, Generative adversarial networks, Bayesian networks, etc.

Architecture of GANs

GANs consist of two neural networks a Generator G and a Discriminator D. Further, these two models are involved in a zero-sum game during training.

The Generator network learns the distribution of the training data. And when we provide random noise as input, it generates some synthetic data trying to imitate the training samples.

Now here comes the discriminator model(D). It designates a label- Real or Fake to the data generated by G on the basis of the data distribution. This means, the new image comes from the training images or it is some artificially generated image.

The case when D successfully recognizes the image as real or fake leads to an increase in the Generator’s loss. Similarly, when G succeeds in constructing good quality images similar to the real ones and befools the D, it increases the discriminator’s loss. Also, the generator learns from the process and generates better and more realistic images in the next iteration.

Mainly it can be considered as a two-player MIN-MAX game. Here the performance of both networks improves over time. Both networks go through multiple training iterations. With due course of time and several updations in model parameters like weights and biases, they reach the stable state also known as nash equilibrium.


What is Nash Equilibrium?

Nash equilibrium is a stable state of a system involving the interaction of different participants, in which no participant can gain by a unilateral change of strategy if the strategies of the others remain unchanged.

Ultimately in this zero-sum game, we can successfully generate artificial or fake images that mostly look similar to the real training dataset.


Let’s see how useful GANs can be.

For instance, during the lockdown, you got a chance to go through your old photo album. In such a nerve-racking time, it is a good refresher to relive your memories. But since this album was lying in your cupboard for years, untouched, some photographs were damaged and that made you sad. And this is precisely when you decided to use GANs.

The image below was successfully restored with help of GANs, using a method called Image Inpainting.

GAN Libraries

Original Image vs Restored Image

Image Source: Bertalmío et al., 2000.

Image Inpainting is the art to restore damaged images by reconstructing the missing parts by utilizing the available background information. This technique is also used for removing unwanted objects from the given images.

This was just a quick review of GANs. If you want to know more about it, I will suggest you read the following articles.

Now we will see some interesting GAN libraries.



Tensorflow GANs also known as TF- GAN is an open-source lightweight python library. It was developed by Google AI researchers for the easy and effective implementation of GANs.

TF-GAN provides well-developed infrastructure to train and evaluate the Generative Adversarial Network along with effectively tested loss functions and evaluation metrics. The library consists of various modules to implement the model. It provides simple function calls that a user can apply on his own data without writing the code from scratch.

It is easy to install and use just like other packages such as NumPy and pandas, as it provides the PyPi package. Use following code-

#Installing the library
pip install tensorflow-gan
#importing the library 
import tenorflow_gan as tfgan


The following is code for generating images from MNIST dataset using TF-Gan-

# Set up the input.
images = mnist_data_provider.provide_data(FLAGS.batch_size)
noise = tf.random_normal([FLAGS.batch_size, FLAGS.noise_dims])

# Build the generator and discriminator.
gan_model = tfgan.gan_model(
    generator_fn=mnist.unconditional_generator,  # you define
    discriminator_fn=mnist.unconditional_discriminator,  # you define

# Build the GAN loss.
gan_loss = tfgan.gan_loss(

# Create the train ops, which calculate gradients and apply updates to weights.
train_ops = tfgan.gan_train_ops(
    generator_optimizer=tf.train.AdamOptimizer(gen_lr, 0.5),
    discriminator_optimizer=tf.train.AdamOptimizer(dis_lr, 0.5))

# Run the train ops in the alternating training scheme.

What I like about the library-

  1. One important thing about the library is that TF-GAN currently is compatible with Tensorflow-2.0 i.e the latest version of TensorFlow. Also, you can efficiently use it with other frameworks.
  2. Training a Generative adversarial model is a heavy processing task, that used to take weeks. TF-GAN supports Cloud TPU. Hence makes the training process complete in a few hours. To know more about how to use TF-GANs on TPU you can see this tutorial by authors of the library.
  3. In case you need to compare the results of multiple papers, TF-GAN provides you with standard metrics that facilitate the user to efficiently and easily compare different research papers without any statistical bias.

The following are some projects implemented with TF-GAN-

Further to learn more about this exciting GAN library used by the Google researchers read the official document.



Torch-GAN is a PyTorch based framework for writing short and easy to understand codes for developing GANs. This package consists of various Generative adversarial networks along with utilities required for their implementation.

Generally, GANs share a standard design having multiple components like Generator model, Discriminator model, Loss function, and evaluation metrics. While Torch GAN imitates the design of GANs through simple API and allows customizing the components when required.




This GAN Library facilitates the interaction among the components of GAN through a highly versatile trainer which automatically adopts to user-defined models and losses.

Installing the library is simple using pip. You just need to use the following command below and it is done.

pip3 install torchgan

Implementing the Torch-GAN Models

At the core of the design, we have a trainer module responsible for the flexibility and ease of use. The user needs to provide the required specifications i.e the architecture of generator and discriminator models, along with the optimizer associated. The user also needs to provide the loss functions and evaluation metrics.

The library provides the freedom to choose the specifications either from the wide range available or custom variants of their own. In the following image, we can see the implementation of DC-GAN in just 10 lines of code isn’t it amazing.

Torch GAN Implementation


What do I like about this GAN library?

    1. The wide range of GAN architecture it supports. You name the architecture and you will find the TorchGAN implementation of the same. For example Vanilla GAN, DCGAN, CycleGan, Conditional GAN, Generative Multi adversarial network, and many more.
    2. Another important feature of the framework is its extensibility and flexibility. Torch-GAN is a comprehensible package. We can use it efficiently with inbuilt or user-defined functionalities.
    3. Also, it provides efficient performance visualization through a Logger object. It supports console logging as well as visualization of performance using TensorBoard and Vizdom.

If you want to dig deeper don’t forget to read the official documentation of TorchGAN.



With the increased research in the field, we can see several implementations of GANs. It is difficult to compare multiple implementations developed using different frameworks, trained under varying conditions, and evaluated using different metrics. Such a comparison is an unavoidable task for researchers. Hence, this was the main motivation behind the development of Mimicry



Mimicry is a lightweight PyTorch library for the reproducibility of GANs. It provides common functionalities required for training and evaluating a Gan model. That allows the researchers to concentrate on model implementation instead of writing the same boilerplate code again and again.

This GAN library provides the standard implementation of various GAN architectures like DCGAN, Wasserstein GAN with Gradient Penalty (WGAN-GP), Self-supervised GAN (SSGAN), etc. Later it gives the comparison of the baseline performance of multiple GAN models with the same model size, trained under similar conditions.

Just like the other two libraries, we can easily install Mimicry using pip and it is ready to use.

pip install torch-mimicry

Here is the quick implementation of SNGAN using mimicry

import torch
import torch.optim as optim
import torch_mimicry as mmc
from torch_mimicry.nets import sngan

# Data handling objects
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
dataset = mmc.datasets.load_dataset(root='./datasets', name='cifar10')
dataloader =
        dataset, batch_size=64, shuffle=True, num_workers=4)

# Define models and optimizers
netG = sngan.SNGANGenerator32().to(device)
netD = sngan.SNGANDiscriminator32().to(device)
optD = optim.Adam(netD.parameters(), 2e-4, betas=(0.0, 0.9))
optG = optim.Adam(netG.parameters(), 2e-4, betas=(0.0, 0.9))

# Start training
trainer =

Another important feature of mimicry is it provides Tensorboard support for performance visualization. Hence, you can create a loss and probability curve for monitoring training. It can display randomly generated images for checking variety.

Mimicry is an interesting development aimed at aiding researchers. I will personally suggest you read the Mimicry paper.


IBM GAN Toolkit

So far, we have seen some very efficient and state of the art GAN libraries. There are many more GAN libraries like Keras-GAN, PyTorch-GAN, PyGAN, etc. When we observe closely we see some common things among these GAN libraries. They are code-intensive. If you want to use any of them then you must be well versed in-

  • The knowledge and implementation of GANs.
  • Fluent in Python
  • How to use the particular framework

It is a bit difficult to know everything for a software programmer. To solve the issue, here we have a user-friendly GANs tool – The IBM GAN-Toolkit.

The GAN toolkit provides highly flexible, no code variant of implementing GAN models. Additionally, it gives a high level of abstraction for implementing the GAN model. Here, the user just needs to give the model details using a config file or command-line argument. Then the framework will take care of everything else. I personally found it very interesting.

The following steps will help you with installation-

  1. Firstly, we clone the code-
    $ git clone
    $ cd gan-toolkit


  2. Then install all the requirements-
    $ pip install -r requirements.txt

    Now it’s ready to use. Finally, to train the network we have to give a config file in JSON format as follows

    $ python --config my_gan.json

The toolkit implements multiple GAN architecture like vanilla GAN, DC-GAN, Conditional- GAN, and more.

Advantages of GAN toolkit

  1. It facilitates a no-code way of implementing the state of the art computer vision technology. Only, a simple JSON file is required to define a GAN architecture. There is no need to write the training code as the framework will take care of it.
  2. It provides multi library support i.e PyTorch, Keras, and TensorFlow as well.
  3. Also, in the GAN toolkit, we have the freedom to easily mix and match the components from different models. For example, you can use the generator model from DC-GAN, discriminator from C-GAN, and training process from vanilla gan.

Now just read the document and play around with GANs in your own way.


End Notes

GANs are an active field of research. We’re seeing regular updates almost weekly on the next GAN version. You can check out the work researchers have done here.

To conclude, in this article we discussed the 4 most important GAN Libraries that can be easily implemented in Python. On a daily basis, we are seeing tremendous developments and seeing new applications of GANs. The following article explains some of these amazing applications-

Which are the GAN libraries you use? Do you feel any other library should have been listed? Let us know in the comments below

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Shipra Saxena

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