# POSTECH-CVLab/PyTorch-StudioGAN

StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.

Repository: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN
Canonical: https://ross.abutalabs.com/products/pytorch-studiogan
Homepage: https://github.com/MINGUKKANG
Language: Python
License: NOASSERTION
License Family: other
Topics: pytorch, deep-learning, generative-adversarial-network, biggan, stylegan2, machine-learning, stylegan2-ada, stylegan3, data-efficient-gan-training, clean-fid
Last push: 2024-08-09T17:19:10+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 2266, "days_push": 754, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3487, forks 344 (observed 2026-08-28T04:08:07.072554+00:00)

## What it is
PyTorch-StudioGAN is a PyTorch library providing unified implementations of representative GAN architectures (BigGAN, StyleGAN2/3, etc.) for conditional and unconditional image generation. It includes a large benchmark with pre-trained checkpoints, evaluation metrics like FID and IS, and a YAML-based configuration system for mix-and-matching GAN components.

## Use cases
- train a StyleGAN2 or BigGAN model on CIFAR10 or ImageNet
- compare FID scores of different GAN architectures under identical settings
- evaluate a generative image model with clean-FID or PRDC metrics
- research new GAN losses or regularization techniques
- download pre-trained GAN checkpoints for image synthesis
- benchmark GANs against diffusion and autoregressive generative models

## When to choose
- you need reproducible, unified implementations of many GAN variants for research comparison
- you want a comprehensive benchmark with pre-trained models and evaluation backbones
- you are doing academic research on generative image synthesis

## When to avoid
- you need production image generation rather than research experimentation
- you want diffusion models only, as GANs are the primary focus
- you need a simple high-level API with minimal configuration

## Facets
- artifact type: library
- maturity: stable
- function: machine-learning, deep-learning, image-processing, benchmarking
- domain: deep-learning, machine-learning, image-processing, artificial-intelligence
- platform: python, cross-platform
- tags: gan, pytorch, image-synthesis, generative-models, stylegan, biggan, fid-evaluation, research, gpu, linux

## Member repositories
- POSTECH-CVLab/PyTorch-StudioGAN (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:07.072554+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 2026-08-29T18:36:07.008449+00:00, confidence not recorded.
  - readme: https://github.com/POSTECH-CVLab/PyTorch-StudioGAN (fetched 2026-08-28T04:08:07.072554+00:00, sha f86e6ebd30c1)
  - homepage: https://github.com/MINGUKKANG (fetched 2026-08-29T09:30:10.710594+00:00, sha 9f8b7ec53c67)
- Data as of 2026-08-30T08:39:29.467469+00:00.