# NVlabs/SPADE

Semantic Image Synthesis with SPADE

Repository: https://github.com/NVlabs/SPADE
Canonical: https://ross.abutalabs.com/products/spade
Homepage: https://nvlabs.github.io/SPADE/
Language: Python
License: NOASSERTION
License Family: other
Last push: 2023-08-07T10:52:48+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": 2729, "days_push": 1122, "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 7717, forks 970 (observed 2026-08-28T04:10:02.564436+00:00)

## What it is
Official PyTorch implementation of SPADE (GauGAN), a CVPR 2019 method for synthesizing photorealistic images from semantic segmentation maps using spatially-adaptive normalization. Released by NVIDIA for academic research under CC BY-NC-SA 4.0, with a newer reimplementation available in the Imaginaire repository.

## Use cases
- generate photorealistic images from segmentation maps
- turn a rough sketch layout into a realistic landscape image
- reproduce GauGAN research results on COCO-Stuff or Cityscapes
- train a conditional GAN for semantic image synthesis
- control style and content of generated images via layout input

## When to choose
- you need the original reference implementation of SPADE for research or paper reproduction
- you want to synthesize images conditioned on semantic layouts in PyTorch
- you are studying spatially-adaptive normalization techniques

## When to avoid
- you need commercial use - the license is non-commercial only
- you want the most performant modern implementation - use NVlabs/imaginaire instead
- you lack multi-GPU hardware for training at paper-scale

## Facets
- artifact type: library
- maturity: maintenance
- function: machine-learning, deep-learning, image-processing
- domain: deep-learning, computer-vision, image-processing, artificial-intelligence
- platform: python
- tags: gan, image-synthesis, gaugan, pytorch, semantic-segmentation, research-code, cvpr-2019, non-commercial, gpu, linux

## Member repositories
- NVlabs/SPADE (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:02.564436+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 2026-08-29T17:35:18.886583+00:00, confidence not recorded.
  - readme: https://github.com/NVlabs/SPADE (fetched 2026-08-28T04:10:02.564436+00:00, sha d29afcb83f64)
  - homepage: https://nvlabs.github.io/SPADE/ (fetched 2026-08-29T08:31:25.646787+00:00, sha 8c5b29952c19)
- Data as of 2026-08-30T08:39:29.467469+00:00.