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HyperGAN 0.9


A composable GAN API and CLI. Built for developers, researchers, and artists.

HyperGAN is currently in open beta.

Colorizer 0.9 1

Logos generated with examples/colorizer, AlphaGAN, and the RandomWalk sampler

Table of contents


Generative Adversarial Networks consist of 2 learning systems that learn together. HyperGAN implements these learning systems in Tensorflow with deep learning.

For an introduction, see here

HyperGAN is currently in open beta.


Colorizer 0.9 3 Colorizer 0.9 3

0.9 samples are still training.


API Documentation


See the full changelog here:

Quick start

Minimum requirements

  1. For 256x256, we recommend a GTX 1080 or better. 32x32 can be run on lower-end GPUs.
  2. CPU training is extremely slow. Use a GPU if you can!
  3. Python3


Install hypergan:

  pip3 install hypergan --upgrade

Optional virtualenv:

If you use virtualenv:

  virtualenv --system-site-packages -p python3 hypergan
  source hypergan/bin/activate


If installation fails try this.

  pip3 install numpy tensorflow-gpu hyperchamber pillow pygame

Dependency help

If the above step fails see the dependency documentation:

Create a new project

  hypergan new mymodel

This will create a mymodel.json based off the default configuration. You can change configuration templates with the -c flag.

List configuration templates

  hypergan new mymodel -l

See all configuration templates with --list-templates or -l.


  # Train a 32x32 gan with batch size 32 on a folder of folders of pngs, resizing images as necessary
  hypergan train folder/ -s 32x32x3 -f png -c mymodel --resize

Increasing performance

On ubuntu sudo apt-get install libgoogle-perftools4 and make sure to include this environment variable before training

  LD_PRELOAD="/usr/lib/" hypergan train my_dataset

HyperGAN does not cache image data in memory. Images are loaded every time they're needed, so you can increase performance by pre-processing your inputs, especially by resampling large inputs to the output resolution. e.g. with ImageMagick:

  convert image1.jpg -resize '128x128^' -gravity Center -crop 128x128+0+0 image1.png

Development mode

If you wish to modify hypergan

git clone
cd hypergan
python3 develop

Running on CPU

Make sure to include the following 2 arguments:

CUDA_VISIBLE_DEVICES= hypergan --device '/cpu:0'

Don't train on CPU! It's too slow.

The pip package hypergan

 hypergan -h


  # Train a 32x32 gan with batch size 32 on a folder of pngs
  hypergan train [folder] -s 32x32x3 -f png -b 32 --config [name]


  # Train a 256x256 gan with batch size 32 on a folder of pngs
  hypergan train [folder] -s 32x32x3 -f png -b 32 --config [name] --sampler static_batch --sample_every 5 --save_samples

By default hypergan will not save samples to disk. To change this, use --save_samples.

One way a network learns:

Demo CountPages alpha

To create videos:

  ffmpeg -i samples/%06d.png -vcodec libx264 -crf 22 -threads 0 gan.mp4


To see a detailed list, run

  hypergan -h


See the API documentation at

  import hypergan as hg


See the example documentation


Each example is capable of random search. You can search along any set of parameters, including loss functions, trainers, generators, etc.


To build a new network you need a dataset. Your data should be structured like:


Creating a Dataset

Datasets in HyperGAN are meant to be simple to create. Just use a folder of images.

Unsupervised learning

The default mode of hypergan.


For jpg(pass -f jpg)

Supervised learning

Training with labels allows you to train a classifier.

Each directory in your dataset represents a classification.

Example: Dataset setup for classification of apple and orange images:


You must pass --classloss to hypergan cli to activate this feature.


Configuration in HyperGAN uses JSON files. You can create a new config with the default template by running hypergan new mymodel.

You can see all templates with hypergan new mymodel -l.


A hypergan configuration contains all hyperparameters for reproducing the full GAN.

In the original DCGAN you will have one of the following components:

  • Encoder
  • Generator
  • Discriminator
  • Loss
  • Trainer

Other architectures may differ. See the configuration templates.


A base class for each of the component types listed below.


A generator is responsible for projecting an encoding (sometimes called z space) to an output (normally an image). A single GAN object from HyperGAN has one generator.

Resize Conv

This generator supports any resolution. Works using a combination of final_depth and depth_increase in order to scale output size.

For example: the shape of final_depth=16 and depth_increase=16 when working on images of 64x64x3

  64x64x3 -> 32x32x16 -> 16x16x32 -> 8x8x48 -> 4x4x64

The same network on 128x128x3:

  128x128x3 -> 64x64x16 -> 32x32x32 -> 16x16x48 -> 8x8x64 -> 4x4x80
attribute description type
final_depth The features for the last convolution layer(before projecting to final output). int > 0
depth_increase Working backwards, each previous layer will contain this many more features. int > 0
activation Activations to use. See activations f(net):net
final_activation Final activation to use. This is usually set to tanh to squash the output range. See activations. f(net):net
layer_filter On each resize of G, we call this method. Anything returned from this method is added to the graph before the next convolution block. See common layer filters f(net):net
layer_regularizer This "regularizes" each layer of the generator with a type. See layer regularizers f(name)(net):net
block This is called at each layer of the generator, after the resize. Can also be the string deconv f(...) see source code
resize_image_type See tf.resize_images for values enum(int)


Sometimes referred to as the z-space representation or latent space. In dcgan the 'encoder' is random uniform noise.

Can be thought of as input to the generator.

Uniform Encoder

attribute description type
z The dimensions of random uniform noise inputs int > 0
min Lower bound of the random uniform noise int
max Upper bound of the random uniform noise int > min
projections See more about projections below [f(config, gan, net):net, ...]
modes If using modes, the number of modes to have per dimension int > 0


This encoder takes a random uniform value and outputs it as many possible types. The primary idea is that you are able to query Z as a random uniform distribution, even if the gan is using a spherical representation.

Some projection types are listed below.

"identity" projection

"sphere" projection

"gaussian" projection

"modal" projection

One of many

"binary" projection


Category Encoder

Uses categorical prior to choose 'one-of-many' options.


A discriminator's main purpose(sometimes called a critic) is to separate out G from X, and to give the Generator a useful error signal to learn from.

Note a discriminator can be an encoder sometimes(like in the case of AlphaGAN)

Pyramid Discriminator

Architecturally similar to the ResizeConvGenerator.

For example: the shape of initial_depth=16 and depth_increase=16 when working on images of 64x64x3

  64x64x3 -> 32x32x16 -> 16x16x32 -> 8x8x48 -> 4x4x64

The same network on 128x128x3:

  128x128x3 -> 64x64x16 -> 32x32x32 -> 16x16x48 -> 8x8x64 -> 4x4x80
attribute description type
activation Activations to use. See activations f(net):net
initial_depth The initial number of filters to use. int > 0
depth_increase Increases the filter sizes on each convolution by this amount int > 0
final_activation Final activation to use. None is common here, and is required for several loss functions. f(net):net
layers The number of convolution layers int > 0
layer_filter Append information to each layer of the discriminator f(config, net):net
layer_regularizer batch_norm_1, layer_norm_1, or None f(batch_size, name)(net):net
fc_layer_size The size of the linear layers at the end of this network(if any). int > 0
fc_layers fully connected layers at the end of the discriminator(standard dcgan is 0) int >= 0
noise Instance noise. Can be added to the input X float >= 0
progressive_enhancement If true, enable progressive enhancement boolean



Wasserstein Loss is simply:

 d_loss = d_real - d_fake
 g_loss = d_fake

d_loss and g_loss can be reversed as well - just add a '-' sign.

Least-Squares GAN

 d_loss = (d_real-b)**2 - (d_fake-a)**2
 g_loss = (d_fake-c)**2

a, b, and c are all hyperparameters.

Standard GAN and Improved GAN

Includes support for Improved GAN. See hypergan/losses/ for details.

Supervised loss

Supervised loss is for labeled datasets. This uses a standard softmax loss function on the outputs of the discriminator.

Categorical loss

This is currently untested.

Cramer loss

No good results yet

Softmax loss

Not working as well as the others

Boundary Equilibrium Loss

Use with the AutoencoderDiscriminator.

See the began configuration template.

Loss configuration

attribute description type
batch_norm batch_norm_1, layer_norm_1, or None f(batch_size, name)(net):net
create Called during graph creation f(config, gan, net):net
discriminator Set to restrict this loss to a single discriminator(defaults to all) int >= 0 or None
label_smooth improved gan - Label smoothing. float > 0
labels lsgan - A triplet of values containing (a,b,c) terms. [a,b,c] floats
reduce Reduces the output before applying loss f(net):net
reverse Reverses the loss terms, if applicable boolean


Determined by the GAN implementation. These variables are the same across all trainers.


attribute description type
g_learn_rate Learning rate for the generator float >= 0
g_beta1 (adam) float >= 0
g_beta2 (adam) float >= 0
g_epsilon (adam) float >= 0
g_decay (rmsprop) float >= 0
g_momentum (rmsprop) float >= 0
d_learn_rate Learning rate for the discriminator float >= 0
d_beta1 (adam) float >= 0
d_beta2 (adam) float >= 0
d_epsilon (adam) float >= 0
d_decay (rmsprop) float >= 0
d_momentum (rmsprop) float >= 0
clipped_gradients If set, gradients will be clipped to this value. float > 0 or None
d_clipped_weights If set, the discriminator will be clipped by value. float > 0 or None

Downloadable datasets


Contributions are welcome and appreciated! We have many open issues in the Issues tab.

See how to contribute.


HyperGAN uses semantic versioning.

TLDR: x.y.z

  • x is incremented on stable public releases.
  • y is incremented on API breaking changes. This includes configuration file changes and graph construction changes.
  • z is incremented on non-API breaking changes. z changes will be able to reload a saved graph.




If you wish to cite this project, do so like this:

  255bits(Martyn, Mikkel et al),
  HyperGAN, (2017), 
  GitHub repository,