hypergan

HyperGAN

# README ## HyperGAN 1.0 [](https://hypergan.gitbook.io/hypergan/) [](https://discord.gg/t4WWBPF) [](https://twitter.com/hypergan) A composable GAN built for developers, researchers, and artists. HyperGAN is in pre-release and open beta.  _Logos generated with_ [_examples/colorizer_](./examples/colorizer.py) See more on the [hypergan youtube](https://www.youtube.com/channel/UCU33XvBbMnS8002_NB7JSvA) ## Table of contents * [About](#about) * [Documentation](https://hypergan.gitbook.io/hypergan/) * [Changelog](./changelog.md) * [Quick start](#quick-start) * [Requirements](#requirements) * [Install](#install) * [Train](#train) * [API](#api) * [Using a trained hypergan model](#using-a-trained-hypergan-model) * [Training a gan](#training-a-gan) * [Examples](#examples) * [Tutorials](#tutorials) * [The pip package hypergan](#the-pip-package-hypergan) * [Training](#training) * [Sampling](#sampling) * [Additional Arguments](#additional-arguments) * [Running on CPU](#running-on-cpu) * [Troubleshooting](#troubleshooting) * [Development Mode](#development-mode) * [Datasets](#datasets) * [Creating a Dataset](#creating-a-dataset) * [Downloadable Datasets](#downloadable-datasets) * [Cleaning up data](#cleaning-up-data) * [Features](#features) * [Showcase](#showcase) * [Sponsors](#sponsors) * [Contributing](./#contributing.md) * [Versioning](#Versioning) * [Citation](#citation) ## About HyperGAN builds generative adversarial networks in pytorch and makes them easy to train and share. For a general introduction to GANs see [http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/](http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/) Join the community [discord](https://discord.gg/t4WWBPF). ## Documentation * [Gitbook documentation](https://hypergan.gitbook.io/) ## Changelog See the full changelog here: [Changelog.md](changelog.md) ## Quick start ### Requirements OS: Windows, OSX, Linux For training: GPU: Nvidia, GTX 1080+ recommended ### Install 1. Install HyperGAN For users: `pip3 install hypergan` For developers: Download this repo and run `python3 setup.py develop` 2. Test it out * `hypergan train preset:celeba -s 128x128x3` 3. Join the community * Once you've made something cool, be sure to share it on the Discord \([https://discord.gg/t4WWBPF](https://discord.gg/t4WWBPF)\). ### Create a new model ```bash 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 ```bash hypergan new mymodel -l ``` See all configuration templates with `--list-templates` or `-l`. ### Train ```bash hypergan train folder/ -s 32x32x3 -c mymodel --resize ``` ## API ```python import hypergan as hg ``` Note this API is currently under work in 1.0. If you are reading this before 1.0 is released check the examples. See the [gitbook documentation](https://hypergan.gitbook.io/) for more details. ### Using a trained hypergan model ```python my_gan = hg.GAN('model.hypergan') batch_sample = my_gan.sample() ``` ### Training a gan ```python gan = hg.GAN("default.json", inputs=hg.inputs.ImageLoader(...)) trainable_gan = hg.TrainableGAN(gan) for step in trainable_gan.train(): print("I'm on step ", step) ``` ### Examples See the examples [https://github.com/hypergan/HyperGAN/tree/master/examples](https://github.com/hypergan/HyperGAN/tree/master/examples) ### Tutorials See the tutorials [https://hypergan.gitbook.io/hypergan/tutorials](https://hypergan.gitbook.io/hypergan/tutorials) ## The pip package hypergan ```bash pip install hypergan ``` ### Training ```bash # Train a 32x32 gan with batch size 32 on a folder of pngs hypergan train [folder] -s 32x32x3 -b 32 --config [name] ``` ### Sampling ```bash hypergan sample [folder] -s 32x32x3 -b 32 --config [name] --sampler batch_walk --save_samples ``` By default hypergan will not save training samples to disk. To change this, use `--save_samples`. ### Additional Arguments To see a detailed list, run ```bash hypergan -h ``` ### Running on CPU You can switch the backend with: ```bash hypergan [...] -B cpu ``` Don't train on CPU! It's too slow. ### Troubleshooting Make sure that your cuda, nvidia drivers, pillow, pytorch, and pytorch vision are the latest version. Check the discord for help. ### Development mode If you wish to modify hypergan ```bash git clone https://github.com/hypergan/hypergan cd hypergan python3 setup.py develop ``` Make sure to `pip3 uninstall hypergan` to avoid version conflicts. ## Datasets To build a new network you need a dataset. ### Creating a Dataset Datasets in HyperGAN are meant to be simple to create. Just use a folder of images. Nested folders work too. ### Cleaning up data HyperGAN is built to be resilient to all types of unclean data. By default images are resized then cropped if necessary. See `--nocrop`, `--random_crop` and `--resize` for additional image scaling options. ## Features A list of features in the 1.0 release: * API * CLI * Viewer - an electron app to explore and create models * Cross platform - Windows, OSX, Linux * Inference - Add AI content generation to your project * Training - Train custom models using accelerated parallel training backends * Sharing - Share built models with each other. Use them in python projects as hypergan models, or in any project as onxx models * Customizable - Define custom architectures in the json, or replace any component with your own pytorch creation * Data - Built to work on unclean data and multiple data types * Unsupervised learning * Unsupervised alignment - Align one distribution to another or discover new novel distributions. * Transfer learning * Online learning ## Showcase ### 1.0 models are still training Submit your showcase with a pull request! For more, see the \#showcase room in [](https://discord.gg/t4WWBPF) ## Sponsors We are now accepting financial sponsors. Sponsor to (optionally) be listed here. https://github.com/sponsors/hypergan ## 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/](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. ## Citation ```text HyperGAN Community HyperGAN, (2016-2020+), GitHub repository, https://github.com/HyperGAN/HyperGAN ``` HyperGAN comes with no warranty or support.