taesungp/contrastive-unpaired-translation

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

2543 stars · 451 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of Contrastive Unpaired Translation (CUT), an ECCV 2020 method for unpaired image-to-image translation using patchwise contrastive learning and adversarial training. It trains faster and with less memory than CycleGAN and supports single-image domain translation.

Use cases

• translate images between domains without paired training data
• train an image-to-image translation model faster than CycleGAN
• perform style transfer between unpaired image collections
• train a translation model from a single image per domain
• convert photos to paintings or between visual styles with GANs

When to choose

• you need unpaired image-to-image translation with lower training cost than CycleGAN
• you want a research-grade PyTorch codebase for contrastive learning in image synthesis
• you only have a single image per domain to train on

When to avoid

• you need paired image-to-image translation like pix2pix
• you need a production-ready, actively maintained library with long-term support
• you lack a GPU and need fast training

Facets

library · maturity maintenance

image-processing machine-learning deep-learning computer-vision image-processing deep-learning machine-learning python pytorch gan image-translation contrastive-learning cyclegan research-code eccv-2020 linux macos gpu

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markdown · JSON · MCP: product_card(name="taesungp/contrastive-unpaired-translation")