# tkarras/progressive_growing_of_gans

Progressive Growing of GANs for Improved Quality, Stability, and Variation

Repository: https://github.com/tkarras/progressive_growing_of_gans
Canonical: https://ross.abutalabs.com/products/progressive_growing_of_gans
Homepage: http://research.nvidia.com/publication/2017-10_Progressive-Growing-of
Language: Python
License: NOASSERTION
License Family: other
Last push: 2022-02-17T12:44:14+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": 3232, "days_push": 1658, "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 6179, forks 1083 (observed 2026-08-28T04:09:38.182858+00:00)

## What it is
Official TensorFlow implementation of the ICLR 2018 NVIDIA paper 'Progressive Growing of GANs', which trains generators and discriminators from low to high resolution for stable, high-quality image synthesis. It includes training code, pretrained networks, and a higher-quality CelebA dataset.

## Use cases
- train a GAN to generate high-resolution images
- reproduce the progressive growing of GANs paper results
- generate synthetic celebrity faces with a GAN
- study progressive GAN training methodology
- benchmark GAN image quality with inception score

## When to choose
- you want the reference implementation of the Progressive GAN paper
- you need to generate 1024x1024 images with GANs on NVIDIA GPUs
- you are doing research on GAN training stability and metrics

## When to avoid
- you need a maintained, modern GAN framework - use StyleGAN2-ADA or StyleGAN3 instead
- you want diffusion models or transformer-based generators
- you need a non-TensorFlow or CPU-only training setup

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing, gpu-computing
- domain: deep-learning, machine-learning, computer-vision, image-processing, artificial-intelligence
- platform: python
- tags: gan, generative-adversarial-networks, tensorflow, research-code, image-generation, nvidia-research, iclr-2018, gpu, linux

## Member repositories
- tkarras/progressive_growing_of_gans (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:38.182858+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 2026-08-29T17:47:25.255671+00:00, confidence not recorded.
  - readme: https://github.com/tkarras/progressive_growing_of_gans (fetched 2026-08-28T04:09:38.182858+00:00, sha 3221f805f067)
  - homepage: http://research.nvidia.com/publication/2017-10_Progressive-Growing-of (fetched 2026-08-29T08:44:07.040678+00:00, sha 2ce45c72bf09)
  - site_page: https://www.nvidia.com/en-us/about-nvidia/privacy-policy (fetched 2026-08-29T08:44:07.049984+00:00, sha 5362c58d0750)
  - site_page: https://www.nvidia.com/en-us/about-nvidia/privacy-center (fetched 2026-08-29T08:44:07.053284+00:00, sha b098377da9cc)
  - site_page: https://www.nvidia.com/en-us/about-nvidia/terms-of-service (fetched 2026-08-29T08:44:07.054973+00:00, sha 85469b6ff0a1)
  - site_page: https://www.nvidia.com/en-us/about-nvidia/accessibility (fetched 2026-08-29T08:44:07.057241+00:00, sha 8f61ce4143ec)
  - site_page: https://www.nvidia.com/en-us/about-nvidia/company-policies (fetched 2026-08-29T08:44:07.058821+00:00, sha b169611fd7d6)
- Data as of 2026-08-30T08:39:29.467469+00:00.