# junyanz/BicycleGAN

Toward Multimodal Image-to-Image Translation

Repository: https://github.com/junyanz/BicycleGAN
Canonical: https://ross.abutalabs.com/products/bicyclegan
Homepage: https://junyanz.github.io/BicycleGAN/
Language: Python
License: NOASSERTION
License Family: other
Topics: pytorch, pix2pix, gans, generative-adversarial-network, deep-learning
Last push: 2020-08-05T01:00:21+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": 3214, "days_push": 2220, "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 1514, forks 252 (observed 2026-08-28T04:04:56.396440+00:00)

## What it is
A PyTorch implementation of BicycleGAN, a model for multimodal image-to-image translation that generates diverse outputs from a single input by sampling a latent code. It is the official research code accompanying the NIPS 2017 paper by UC Berkeley and Adobe Research.

## Use cases
- generate diverse day images from a night photo
- translate edges to realistic shoes with multiple styles
- convert labels to facades with varied appearances
- create morphing videos between translated images
- experiment with conditional GAN training on paired image data

## When to choose
- you need multimodal image-to-image translation with diverse outputs
- you want a reference implementation of the BicycleGAN paper
- you have paired training data and a CUDA GPU

## When to avoid
- you need unpaired image translation (use CycleGAN instead)
- you require actively maintained code with recent PyTorch versions
- you need production-ready inference at scale

## Facets
- artifact type: library
- maturity: maintenance
- function: deep-learning, image-processing, machine-learning
- domain: deep-learning, computer-vision, image-processing, machine-learning
- platform: python
- tags: gan, pytorch, image-to-image-translation, generative-models, research-code, nips-2017, linux, macos, gpu

## Member repositories
- junyanz/BicycleGAN (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:56.396440+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 2026-08-30T04:32:12.844059+00:00, confidence not recorded.
  - readme: https://github.com/junyanz/BicycleGAN (fetched 2026-08-28T04:04:56.396440+00:00, sha ff4568e27203)
  - homepage: https://junyanz.github.io/BicycleGAN/ (fetched 2026-08-29T11:35:50.670492+00:00, sha 2eb9ee1615ce)
- Data as of 2026-08-30T08:39:29.467469+00:00.