# hanzhanggit/StackGAN

Repository: https://github.com/hanzhanggit/StackGAN
Canonical: https://ross.abutalabs.com/products/stackgan
Language: Python
License: MIT
License Family: permissive
Last push: 2020-05-15T19:05:57+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3541, "days_push": 2301, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1860, forks 451 (observed 2026-08-28T04:05:45.355356+00:00)

## What it is
TensorFlow implementation of StackGAN, a stacked generative adversarial network that synthesizes photo-realistic images from text descriptions. It reproduces results from the StackGAN paper on birds (CUB-200) and flowers (Oxford-102) datasets, with two-stage GAN training and pretrained models.

## Use cases
- generate images from text descriptions
- reproduce StackGAN paper results
- train a text-to-image GAN on custom datasets
- experiment with stacked GAN architectures
- research text-to-image synthesis models

## When to choose
- you need the reference implementation of StackGAN for research or paper reproduction
- you want to study two-stage text-to-image GAN training
- you work with the CUB birds or Oxford-102 flowers datasets

## When to avoid
- you need a modern, maintained text-to-image model (diffusion models are state of the art)
- you require Python 3 and recent TensorFlow versions
- you want production-ready image generation without GPU research setup

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: machine-learning, computer-vision, artificial-intelligence
- platform: python
- tags: gan, text-to-image, tensorflow, image-synthesis, research-code, generative-models, natural-language-processing, linux, gpu

## Member repositories
- hanzhanggit/StackGAN (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:45.355356+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 2026-08-30T03:15:55.116897+00:00, confidence not recorded.
  - readme: https://github.com/hanzhanggit/StackGAN (fetched 2026-08-28T04:05:45.355356+00:00, sha a888428f274f)
- Data as of 2026-08-30T08:39:29.467469+00:00.