google-research/parti

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  Parti is a Pathways Autoregressive Text-to-Image model from Google Research, exploring sequence-to-sequence generation for high-fidelity photorealistic images.

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  # Parti
  
  <a href="https://parti.research.google" target="_blank"></a>
  
  ## Introduction
  
  We introduce the Pathways Autoregressive Text-to-Image model (Parti), an autoregressive text-to-image generation model that achieves high-fidelity photorealistic image generation and supports content-rich synthesis involving complex compositions and world knowledge. Recent advances with diffusion models for text-to-image generation, such as Google’s <a href="https://imagen.research.google/" target="_blank">Imagen</a>, have also shown impressive capabilities and state-of-the-art performance on research benchmarks. Parti and Imagen are complementary in exploring two different families of generative models – autoregressive and diffusion, respectively – opening exciting opportunities for combinations of these two powerful models.
  
  Parti treats text-to-image generation as a sequence-to-sequence modeling problem, analogous to machine translation – this allows it to benefit from advances in large language models, especially capabilities that are unlocked by scaling data and model sizes. In this case, the target outputs are sequences of image tokens instead of text tokens in another language. Parti uses the powerful image tokenizer, <a href="https://doi.org/10.48550/arXiv.2110.04627" target="_blank">ViT-VQGAN</a>, to encode images as sequences of discrete tokens, and takes advantage of its ability to reconstruct such image token sequences as high quality, visually diverse images.
  
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  # Parti: Pathways Autoregressive Text-to-Image Model (Google Research)
  
  <a href="https://parti.research.google" target="_blank"></a>
  
  ## Introduction
  
  Parti (Pathways Autoregressive Text-to-Image model) is a **research project** from Google that explores an autoregressive approach to text-to-image generation. Unlike diffusion models (e.g., Imagen, Stable Diffusion), Parti treats text-to-image generation as a sequence-to-sequence modeling problem, leveraging advances in large language models to achieve high-fidelity photorealistic image generation with complex compositions and world knowledge. This repository provides the research implementation and details of this novel autoregressive architecture.
  
  Parti uses the powerful image tokenizer, <a href="https://doi.org/10.48550/arXiv.2110.04627" target="_blank">ViT-VQGAN</a>, to encode images as sequences of discrete tokens, and takes advantage of its ability to reconstruct such image token sequences as high quality, visually diverse images.
  
  We observed the

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Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  What are the best models for generating high-fidelity photorealistic images from text descriptions?

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  AI recommended (in order):

  1. Midjourney v6
  2. Stable Diffusion XL (SDXL) 1.0
  3. DALL-E 3
  4. Adobe Firefly (Image 3 Model)
  5. Leonardo.Ai

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• CATEGORY QUERY

  Looking for a generative model that converts text to images using a sequence-to-sequence approach.

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  AI recommended (in order):

  1. Stable Diffusion (stability-ai/stable-diffusion)
  2. DALL-E 3
  3. Midjourney
  4. DALL-E 2
  5. Imagen
  6. CogView (THUDM/CogView)

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