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https://github.com/HyperGAN/HyperGAN
Composable GAN framework with api and user interface
https://github.com/HyperGAN/HyperGAN
artificial-intelligence computer-vision gan generative-adversarial-network hypergan machine-learning machine-learning-api online-learning python pytorch sponsors unsupervised-learning
Last synced: about 1 month ago
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Composable GAN framework with api and user interface
- Host: GitHub
- URL: https://github.com/HyperGAN/HyperGAN
- Owner: HyperGAN
- License: mit
- Created: 2016-06-24T22:49:56.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2023-01-21T17:54:27.000Z (over 1 year ago)
- Last Synced: 2024-04-13T21:53:23.911Z (5 months ago)
- Topics: artificial-intelligence, computer-vision, gan, generative-adversarial-network, hypergan, machine-learning, machine-learning-api, online-learning, python, pytorch, sponsors, unsupervised-learning
- Language: Python
- Homepage:
- Size: 37.3 MB
- Stars: 1,190
- Watchers: 41
- Forks: 167
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- Changelog: changelog.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
- Security: SECURITY.md
Awesome Lists containing this project
README
# 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.