# gligen/GLIGEN

Open-Set Grounded Text-to-Image Generation

Repository: https://github.com/gligen/GLIGEN
Canonical: https://ross.abutalabs.com/products/gligen
Language: Python
License: MIT
License Family: permissive
Last push: 2024-03-06T06:15:54+00:00

## Health v2 (maintenance only)
Score: 31/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 94
- inputs: {"age_days": 1328, "days_push": 910, "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 2223, forks 167 (observed 2026-08-28T04:06:27.830309+00:00)

## What it is
GLIGEN is a research library for open-set grounded text-to-image generation, extending frozen diffusion models with spatial grounding via bounding boxes, keypoints, and reference images. It provides training and inference code plus a diffusers integration for box-conditioned generation and inpainting.

## Use cases
- generate images with objects placed at specified bounding boxes
- layout-to-image generation from text prompts
- inpaint regions of an image conditioned on box and concept
- ground text-to-image models on keypoints or reference images
- combine with Grounding DINO for automatic box annotation
- research on controlled diffusion image generation

## When to choose
- you need spatial control over where generated objects appear
- you want grounded generation integrated with diffusers
- you are reproducing CVPR 2023 grounded text-to-image research

## When to avoid
- you only need plain text-to-image without spatial grounding
- you need actively maintained production tooling rather than research code
- you prefer ControlNet-style conditioning workflows

## Facets
- artifact type: library
- maturity: maintenance
- function: image-processing, machine-learning, deep-learning
- domain: image-processing, computer-vision, artificial-intelligence
- platform: python
- tags: text-to-image, stable-diffusion, grounded-generation, layout-to-image, diffusion-models, cvpr-2023, gpu

## Member repositories
- gligen/GLIGEN (main) score 31

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:27.830309+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 2026-08-30T02:45:03.722273+00:00, confidence not recorded.
  - readme: https://github.com/gligen/GLIGEN (fetched 2026-08-28T04:06:27.830309+00:00, sha e6bf962fac49)
- Data as of 2026-08-30T08:39:29.467469+00:00.