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CONTRIBUTING.md

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Thank you for contributing!

First, join our discord to connect with the hypergan community.

There are many opportunities to help:

  • Share a trained model/json configuration
  • Suggest improvements
  • Answer an issue
  • Help out in the support channel
  • Become a developer
  • Implement a paper. There is a huge list of GANs left to implement: https://github.com/GKalliatakis/Delving-deep-into-GANs
  • Create an API example. Warning: this can be fun.
  • Contribute a screenshot, gif or video of your trained network and share it in the discord
  • Search for and share good GAN configurations (examples are searchable)
  • Do your own custom research. Warning: this is addictive.
  • Become a community manager

GANs are very interesting and we welcome and encourage anyone wanting to explore them.

Requesting a feature

Open an issue describing the feature and a little bit about why you want it. Include a link to a paper if applicable.

Adding a feature

Wow you rock - simply send a pull request! Any work contributed will be under the MIT license so everyone can share in the results.

Filing a bug

Create a new issue. Please try to keep your titles short and describe how to reproduce the problem if applicable.

Answering an issue

We have a lot of issues open if anyone can help answer them. Send a note to hypergan in the discord if you are able to help close any issue.

Sharing a network or samples

Create a pull request. Warning: there is a file size github limit on gifs.

Adding documentation

Thank you! Issue a pull request

Project vision

HyperGAN is a community project and it's utility is determined by the community

Developer goals:

We foresee developers training models on bleeding edge consumer hardware, then deploying a generator or discriminator to various platforms(phones, tablets, servers, smart toasters).

HyperGAN hopes to make that easy and highly configurable to do with the API, CLI, and UI.

Artist goals:

Artists may use GANs to generate paintings, align datasets, or things we cant think of (they are very creative people).

HyperGAN can help artists by being simple to use through the command line and providing a ui.

Researcher goals:

Researchers may find it useful to compose various parts of different papers to test concepts or add new composable types.

HyperGAN can help researchers by being sharable (and reproducible) using json configurations.

Current state

HyperGAN is in open beta and available to everyone under the MIT license.

GANs are an active area of research and things will likely shift to best accomodate the state of the art.

Contact

Email can be sent to hypergan@protonmail.com