HyperGAN/HyperGAN

Health v2 · maintenance only

Adoption not part of the score

1183 stars · 160 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

HyperGAN is a composable GAN (generative adversarial network) framework built on PyTorch, offering both a Python API and a CLI with a user interface for training and sampling. It lets developers, researchers, and artists configure, train, and share GAN models via JSON configuration templates.

Use cases

• train a GAN on my own image dataset
• generate images with a generative adversarial network
• experiment with composable GAN architectures in pytorch
• create a colorizer or image-to-image GAN model
• sample images from a trained GAN checkpoint
• learn how GANs work with a hands-on framework

When to choose

• you want a configurable, preset-driven GAN trainer on PyTorch
• you need both a CLI and a Python API for GAN training and sampling
• you are an artist or researcher generating images without writing model code from scratch

When to avoid

• you need state-of-the-art diffusion or transformer generative models
• you require a project under heavy active development or frequent releases
• you need non-image modalities like text or audio generation

Facets

framework · maturity maintenance

machine-learning deep-learning image-processing machine-learning deep-learning artificial-intelligence computer-vision image-processing python windows gan generative-adversarial-network pytorch unsupervised-learning image-generation online-learning linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="HyperGAN/HyperGAN")