lucidrains/gigagan-pytorch

Health v2 · maintenance only

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1942 stars · 106 forks observed · 2026-08-28

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

A PyTorch implementation of GigaGAN, Adobe's state-of-the-art generative adversarial network for text-to-image and unconditional image synthesis, including 1k-4k image upsamplers. It incorporates stability and convergence improvements from Lightweight GAN such as skip layer excitation and auxiliary reconstruction losses.

Use cases

• train a GAN to generate images from text descriptions
• generate high-resolution images with a GAN in pytorch
• upsample images from 1k to 4k resolution
• replicate the GigaGAN paper results
• train an unconditional GAN on my own image dataset
• experiment with generative adversarial network architectures

When to choose

• you want an open-source PyTorch implementation of GigaGAN to train or fine-tune
• you need GAN-based image upsampling toward 4k resolution
• you want to experiment with or extend a research GAN codebase

When to avoid

• you need a production-ready, well-supported text-to-image model out of the box
• you lack GPU resources, since training GANs at scale is compute-intensive
• you prefer diffusion-based image generation with mature tooling

Facets

library · maturity experimental

machine-learning deep-learning image-processing artificial-intelligence deep-learning image-processing python gan gigagan image-generation text-to-image pytorch upsampling generative-models gpu

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