A composable GAN built for developers, researchers, and artists.
HyperGAN is in pre-release and open beta.
Logos generated with examples/colorizer
See more on the hypergan youtube
Table of contents
- Quick start
- The pip package hypergan
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/
Join the community discord.
See the full changelog here: Changelog.md
OS: Windows, OSX, Linux
GPU: Nvidia, GTX 1080+ recommended
- Install HyperGAN
pip3 install hypergan
For developers: Download this repo and run
python3 setup.py develop
- Test it out
hypergan train preset:celeba -s 128x128x3
- Join the community
- Once you've made something cool, be sure to share it on the Discord (https://discord.gg/t4WWBPF).
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
List configuration templates
hypergan new mymodel -l
See all configuration templates with
hypergan train folder/ -s 32x32x3 -c mymodel --resize
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 for more details.
Using a trained hypergan model
my_gan = hg.GAN('model.hypergan') batch_sample = my_gan.sample()
Training a gan
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)
See the examples https://github.com/hypergan/HyperGAN/tree/master/examples
See the tutorials https://hypergan.gitbook.io/hypergan/tutorials
The pip package hypergan
pip install hypergan
# Train a 32x32 gan with batch size 32 on a folder of pngs hypergan train [folder] -s 32x32x3 -b 32 --config [name]
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
To see a detailed list, run
Running on CPU
You can switch the backend with:
hypergan [...] -B cpu
Don't train on CPU! It's too slow.
Make sure that your cuda, nvidia drivers, pillow, pytorch, and pytorch vision are the latest version.
Check the discord for help.
If you wish to modify hypergan
git clone https://github.com/hypergan/hypergan cd hypergan python3 setup.py develop
Make sure to
pip3 uninstall hypergan to avoid version conflicts.
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.
--resize for additional image scaling options.
A list of features in the 1.0 release:
- 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
1.0 models are still training
Submit your showcase with a pull request!
We are now accepting financial sponsors. Sponsor to (optionally) be listed here.
Contributions are welcome and appreciated! We have many open issues in the Issues tab. Join the discord.
HyperGAN uses semantic versioning. http://semver.org/
- 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.
HyperGAN Community HyperGAN, (2016-2020+), GitHub repository, https://github.com/HyperGAN/HyperGAN
HyperGAN comes with no warranty or support.