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https://github.com/google-research/parti


https://github.com/google-research/parti

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README 

        
# Parti



![website](https://img.shields.io/badge/website-blue)



## 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

Imagen, 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,

ViT-VQGAN,

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 following results:



*   Consistent quality improvements by scaling Parti’s encoder-decoder up to 20

    billion parameters.

*   State-of-the-art zero-shot FID score of 7.23 and finetuned FID score of 3.22

    on MS-COCO.

*   Effectiveness across a wide variety of categories and difficulty aspects in

    our analysis on Localized Narratives and PartiPrompts, our new holistic

    benchmark of 1600+ English prompts that we release as part of this work.



![parti-overview](images/parti_overview.jpeg)



## PartiPrompts benchmark



PartiPrompts (P2) is a rich set of over 1600 prompts in English that we release

as part of this work. P2 can be used to measure model capabilities across

various categories and challenge aspects.



![parti-prompts](images/parti-prompts.png)



P2 prompts can be simple, allowing us to gauge the progress from scaling. They

can also be complex, such as the following 67-word description we created for

Vincent van Gogh’s *The Starry Night* (1889):



*Oil-on-canvas painting of a blue night sky with roiling energy. A fuzzy and

bright yellow crescent moon shining at the top. Below the exploding yellow stars

and radiating swirls of blue, a distant village sits quietly on the right.

Connecting earth and sky is a flame-like cypress tree with curling and swaying

branches on the left. A church spire rises as a beacon over rolling blue hills.*



## Acknowledgements



Parti is a collaboration that spans authors across multiple

[Google Research](https://research.google/) teams:



Jiahui Yu\*, Yuanzhong Xu†, Jing Yu Koh†, Thang

Luong†, Gunjan Baid†, Zirui Wang†, Vijay

Vasudevan†, Alexander Ku†, Yinfei Yang, Burcu Karagol

Ayan, Ben Hutchinson, Wei Han, Zarana Parekh, Xin Li, Han Zhang, Jason

Baldridge†, Yonghui Wu\*



*Equal contribution †Core contribution



We would like to thank Elizabeth Adkison, Fred Alcober, Tania Bedrax-Weiss,

Krishna Bharat, Nicole Brichtova, Yuan Cao, William Chan, Zhifeng Chen, Eli

Collins, Claire Cui, Andrew Dai, Jeff Dean, Emily Denton, Toju Duke, Dumitru

Erhan, Brian Gabriel, Zoubin Ghahramani, Jonathan Ho, Michael Jones, Sarah

Laszlo, Quoc Le, Lala Li, Zhen Li, Sara Mahdavi, Kathy Meier-Hellstern, Kevin

Murphy, Paul Natsev, Paul Nicholas, Mohammad Norouzi, Ruoming Pang, Niki Parmar,

Fernando Pereira, Slav Petrov, Vinodkumar Prabhakaran, Utsav Prabhu, Evan

Rapoport, Keran Rong, Negar Rostamzadeh, Chitwan Saharia, Gia Soles, Austin

Tarango, Ashish Vaswani, Tao Wang, Tris Warkentin, Austin Waters and Ben

Zevenbergen for helpful discussions and guidance, Peter Anderson, Corinna

Cortes, Tom Duerig, Douglas Eck, David Ha, Radu Soricut and Rahul Sukthankar for

paper review and feedback, Erica Moreira and Victor Gomes for help with resource

coordination, Tom Small for designing the Parti watermark, Google ML Data

Operations team for collecting human evaluations on our generated images and

others in the Google Brain team and Google Research team for support throughout

this project.



We would also like to give particular acknowledgments to the Imagen team,

especially Mohammad Norouzi, Chitwan Saharia, Jonathan Ho and William Chan, for

sharing their near complete results prior to releasing Imagen; their findings on

the importance of CF guidance were particularly helpful for the final Parti

model. We also thank the Make-a-Scene team, especially Oran Gafni, for helpful

discussion on CF-guidance implementation in autoregressive models. We thank the

DALL-E 2 authors, especially Aditya Ramesh, for helpful discussion on MS-COCO

evaluation. We also thank the DALL-Eval authors, especially Jaemin Cho, for help

with reproducing their numbers.



## Note



This is not an officially supported Google product.