# znxlwm/pytorch-generative-model-collections

Collection of generative models in Pytorch version.

Repository: https://github.com/znxlwm/pytorch-generative-model-collections
Canonical: https://ross.abutalabs.com/products/pytorch-generative-model-collections
Language: Python
License Family: other
Topics: pytorch, gan, cgan, infogan, acgan, wgan, dragan, ebgan, began, generative-adversarial-network, conditional-gan, mnist, fashion-mnist, package, collection, wgan-gp, lsgan
Last push: 2020-04-12T11:55:29+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3289, "days_push": 2334, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2627, forks 538 (observed 2026-08-28T04:07:05.399274+00:00)

## What it is
A PyTorch collection implementing many GAN variants (GAN, LSGAN, WGAN, WGAN-GP, DRAGAN, CGAN, infoGAN, ACGAN, EBGAN, BEGAN) with a shared architecture for fair comparison. It is a research/educational code collection ported from a TensorFlow reference implementation.

## Use cases
- learn how different GAN variants are implemented in PyTorch
- compare GAN loss functions on the same network architecture
- generate MNIST or Fashion-MNIST images with a GAN
- get a starting codebase for experimenting with GANs
- reproduce results from GAN papers on small datasets

## When to choose
- you want reference implementations of many classic GAN variants in one place
- you are studying or teaching generative adversarial networks
- you need a simple PyTorch GAN baseline on MNIST-like datasets

## When to avoid
- you need production-ready or actively maintained GAN code
- you want modern architectures like diffusion models or StyleGAN
- you need a pip-installable library with an API rather than scripts
- you require a permissively licensed project (no license is specified)

## Facets
- artifact type: learning-resource
- maturity: maintenance
- function: machine-learning, deep-learning
- domain: machine-learning, deep-learning, image-processing
- platform: python
- tags: gan, pytorch, generative-models, mnist, research-code, educational, gpu

## Member repositories
- znxlwm/pytorch-generative-model-collections (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:05.399274+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:19:45.797893+00:00, confidence not recorded.
  - readme: https://github.com/znxlwm/pytorch-generative-model-collections (fetched 2026-08-28T04:07:05.399274+00:00, sha c3238d2b3e58)
- Data as of 2026-08-30T08:39:29.467469+00:00.