autonomousvision/stylegan-t

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1197 stars · 61 forks observed · 2026-08-28

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

Official training code for StyleGAN-T, an ICML 2023 paper on fast large-scale text-to-image synthesis using GANs. It provides dataset preparation and training tooling in Python/PyTorch but does not include pretrained checkpoints.

Use cases

• train a text-to-image GAN from scratch
• reproduce StyleGAN-T paper experiments
• generate images from text prompts with a GAN instead of diffusion models
• train conditional GANs on custom image datasets
• benchmark fast text-to-image synthesis against diffusion baselines

When to choose

• you want to train or extend a fast text-to-image GAN
• you need the exact training code from the StyleGAN-T paper
• you have GPU resources and your own image-caption datasets

When to avoid

• you just want to generate images with pretrained weights (none are provided)
• you need a production text-to-image service
• you prefer diffusion-based models like Stable Diffusion

Facets

library · maturity active

machine-learning deep-learning llm-training machine-learning deep-learning artificial-intelligence image-processing python text-to-image gan stylegan research-code icml-2023 pytorch no-pretrained-checkpoints gpu linux

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