tamarott/SinGAN

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

3344 stars · 621 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

Official PyTorch implementation of SinGAN, an ICCV 2019 best-paper generative model trained on a single natural image. It learns patch statistics across scales to generate random samples and supports image manipulation tasks like harmonization, editing, super-resolution, paint-to-image, and animation.

Use cases

• train a generative model from a single image
• generate random image samples of arbitrary size
• super-resolution of a single image
• image harmonization of pasted objects
• turn a paint or clipart into a realistic photo
• animate a single image
• edit and rearrange objects in a photo

When to choose

• you want to generate diverse samples from just one training image without a large dataset
• you need single-image super-resolution or harmonization with a research-grade reference implementation
• you are reproducing the ICCV 2019 SinGAN paper results

When to avoid

• you need modern PyTorch support - the code only works with torch 1.4 or earlier
• you need large-scale or fast image generation, since a model must be trained per image
• you want a maintained production tool rather than research code

Facets

library · maturity maintenance

machine-learning deep-learning image-processing stable-diffusion deep-learning computer-vision image-processing artificial-intelligence python windows gan single-image-generation super-resolution image-harmonization image-animation pytorch research-code iccv-2019 linux macos gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="tamarott/SinGAN")