ajbrock/BigGAN-PyTorch

Health v2 · maintenance only

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2923 stars · 488 forks observed · 2026-08-28

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

A PyTorch implementation of BigGAN for large-scale high-fidelity image synthesis, written by the paper's first author. It supports multi-GPU training with gradient accumulation, FID evaluation, and pretrained model sampling.

Use cases

• train a BigGAN on ImageNet
• generate high-fidelity images with a GAN
• reproduce BigGAN paper results in PyTorch
• sample images from pretrained BigGAN weights
• compute FID metrics for GAN evaluation
• experiment with truncation and latent interpolations

When to choose

• you need a faithful BigGAN implementation for research
• you have multiple GPUs and want to train large-scale GANs
• you want pretrained BigGAN weights for sampling or fine-tuning

When to avoid

• you need a maintained, up-to-date framework (code targets PyTorch 1.0.1)
• you want simple, single-GPU GAN training
• you need a general-purpose generative model library rather than BigGAN specifically

Facets

library · maturity maintenance

deep-learning machine-learning image-processing gpu-computing deep-learning machine-learning image-processing python gan biggan pytorch image-synthesis generative-models research-code gpu linux

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