  We present the Pathways Autoregressive Text-to-Image (Parti) model, which
generates high-fidelity photorealistic images and supports content-rich
synthesis involving complex compositions and world knowledge. Parti treats
text-to-image generation as a sequence-to-sequence modeling problem, akin to
machine translation, with sequences of image tokens as the target outputs
rather than text tokens in another language. This strategy can naturally tap
into the rich body of prior work on large language models, which have seen
continued advances in capabilities and performance through scaling data and
model sizes. Our approach is simple: First, Parti uses a Transformer-based
image tokenizer, ViT-VQGAN, to encode images as sequences of discrete tokens.
Second, we achieve consistent quality improvements by scaling the
encoder-decoder Transformer model up to 20B parameters, with a new
state-of-the-art zero-shot FID score of 7.23 and finetuned FID score of 3.22 on
MS-COCO. Our detailed analysis on Localized Narratives as well as PartiPrompts
(P2), a new holistic benchmark of over 1600 English prompts, demonstrate the
effectiveness of Parti across a wide variety of categories and difficulty
aspects. We also explore and highlight limitations of our models in order to
define and exemplify key areas of focus for further improvements. See
https://parti.research.google/ for high-resolution images.
