debanga/HyperGAN

Composable GAN framework with api and user interface

HyperGAN 0.10.0-alpha1

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

0.10 preview is not available in pip. You must install from the source. Installation instructions and support are available in our discord

HyperGAN is currently in open beta.

Logos generated with examples/colorizer

See more on the hypergan youtube

StyleGAN is currently state of the art. It is very cool.

Table of contents

• About
• Showcase
• Documentation
• Changelog
• Quick start
  • Requirements
  • Install
  • Testing install
  • Train
  • Development Mode
  • Running on CPU
• The pip package hypergan
• Training
• Sampling
• API
  • Examples
• Datasets
  • Creating a Dataset
  • Downloadable Datasets
  • Cleaning up data
• Contributing
• Versioning
• Sources
• Papers
• Citation

About

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

For an introduction to GANs, see http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/

HyperGAN is a community project. GANs are a very new and active field of research. Join the community discord.

Features

• Community project
• Unsupervised learning
• Transfer learning
• Online learning
• Dataset agnostic
• Reproducible architectures using json configurations
• Domain Specific Language to define custom architectures
• GUI(pygame and tk)
• API
• CLI

Showcase

See the

Documentation

• Model author JSON reference
• Model author tutorial 1
• 0.10.0
• 0.9.x
• Test coverage

Changelog

See the full changelog here: Changelog.md

Quick start

Requirements

Recommended: GTX 1080+

Install

Install hypergan:

  pip3 install hypergan --upgrade

Testing install

To see that tensorflow and hypergan are installed correctly and have access to devices, please run:

  hypergan test

Optional virtualenv:

If you use virtualenv:

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

Dependencies:

If installation fails try this.

  pip3 install numpy tensorflow-gpu hyperchamber pillow pygame

Dependency help

If the above step fails see the dependency documentation:

• tensorflow - https://www.tensorflow.org/install/
• pygame - http://www.pygame.org/wiki/GettingStarted

Create a new model

  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

  # 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

Development mode

If you wish to modify hypergan

git clone https://github.com/hypergan/hypergan
cd hypergan
python3 setup.py 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

Training

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

Sampling

  # 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:

To create videos:

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

Arguments

To see a detailed list, run

  hypergan -h

Examples

See the example documentation https://github.com/hypergan/HyperGAN/tree/master/examples

Datasets

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

  [folder]/[directory]/*.png

Creating a Dataset

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

 [folder]/*.png

For jpg(pass -f jpg)

Downloadable datasets

• Loose images of any kind can be used
• CelebA aligned faces http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
• MS Coco http://mscoco.org/
• ImageNet http://image-net.org/
• youtube-dl (see examples/Readme.md)

Cleaning up data

To convert and resize your data for processing, you can use imagemagick

for i in *.jpg; do convert $i  -resize "300x256" -gravity north   -extent 256x256 -format png -crop 256x256+0+0 +repage $i-256x256.png;done

Contributing

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

See how to contribute.

Versioning

HyperGAN uses semantic versioning. http://semver.org/

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.

Papers

• GAN - https://arxiv.org/abs/1406.2661
• DCGAN - https://arxiv.org/abs/1511.06434
• InfoGAN - https://arxiv.org/abs/1606.03657
• Improved GAN - https://arxiv.org/abs/1606.03498
• Adversarial Inference - https://arxiv.org/abs/1606.00704
• Energy-based Generative Adversarial Network - https://arxiv.org/abs/1609.03126
• Wasserstein GAN - https://arxiv.org/abs/1701.07875
• Least Squares GAN - https://arxiv.org/pdf/1611.04076v2.pdf
• Boundary Equilibrium GAN - https://arxiv.org/abs/1703.10717
• Self-Normalizing Neural Networks - https://arxiv.org/abs/1706.02515
• Variational Approaches for Auto-Encoding Generative Adversarial Networks - https://arxiv.org/pdf/1706.04987.pdf
• CycleGAN - https://junyanz.github.io/CycleGAN/
• DiscoGAN - https://arxiv.org/pdf/1703.05192.pdf
• Softmax GAN - https://arxiv.org/abs/1704.06191
• The Cramer Distance as a Solution to Biased Wasserstein Gradients - https://arxiv.org/abs/1705.10743
• Improved Training of Wasserstein GANs - https://arxiv.org/abs/1704.00028
• More...

Sources

• DCGAN - https://github.com/carpedm20/DCGAN-tensorflow
• InfoGAN - https://github.com/openai/InfoGAN
• Improved GAN - https://github.com/openai/improved-gan

Citation

  HyperGAN Community
  HyperGAN, (2016-2019+), 
  GitHub repository, 
  https://github.com/HyperGAN/HyperGAN

HyperGAN comes with no warranty or support.