google-research/parti

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1589 stars · 85 forks observed · 2026-08-28

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

Parti is Google Research's Pathways Autoregressive Text-to-Image model, which generates high-fidelity photorealistic images from text by treating generation as sequence-to-sequence modeling over image tokens produced by ViT-VQGAN. The repository also releases PartiPrompts (P2), a benchmark of 1600+ English prompts for evaluating text-to-image models.

Use cases

• generate photorealistic images from text prompts
• research autoregressive text-to-image generation
• benchmark text-to-image models with PartiPrompts
• encode and reconstruct images as discrete tokens with ViT-VQGAN
• study scaling behavior of large generative vision-language models

When to choose

• you are researching autoregressive text-to-image generation or model scaling
• you need a holistic prompt benchmark for evaluating text-to-image models
• you want to build on ViT-VQGAN image tokenization

When to avoid

• you need a production-ready image generation service or API
• you want a diffusion-based generator like Stable Diffusion or Imagen
• you lack large-scale GPU/TPU resources for 20B-parameter models

Facets

library · maturity maintenance

machine-learning deep-learning image-processing artificial-intelligence machine-learning image-processing python text-to-image autoregressive-model image-generation research benchmark vit-vqgan google-research gpu linux

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