GPIC: A Giant Permissive Image Corpus for Visual Generation

Keshigeyan Chandrasegaran*1, Kyle Sargent*1, Suchir Agarwal1, Michael Jang1,
Michael Poli1,2, Juan Carlos Niebles1,4, Justin Johnson3, Jiajun Wu1, Li Fei-Fei1
1 Stanford University   2 Radical Numerics   3 University of Michigan   4 Salesforce Research  
* Equal contribution
arXiv 🤗 Dataset 🤗 Models Code/ Evaluation Toolkit Thread
GPIC Dataset Overview

Abstract

Studying scalable methods for visual generative modeling requires large, accessible, and stable datasets. We introduce GPIC, a Giant Permissive Image Corpus of approximately 28 trillion pixels. GPIC comprises diverse internet images captioned by a state-of-the-art vision-language model, including 100M training, 200K validation, and 1M test examples. Moreover, all GPIC images are permissively licensed for both research and commercial use. GPIC is safety-filtered, deduplicated, and centrally hosted on Hugging Face. We provide a benchmarking protocol for generative modeling on GPIC. Finally, we provide a reference baseline for pixel-space flow matching on GPIC. Our dataset, benchmark, and models are available on Hugging Face.

GPIC at a Glance

Click on a photo to see its caption.

GPIC Statistics

GPIC Stats Overview

BibTeX

@misc{chandrasegaran2026gpic,
      title={GPIC: A Giant Permissive Image Corpus for Visual Generation}, 
      author={Keshigeyan Chandrasegaran and Kyle Sargent and Suchir Agarwal and Michael Jang and Michael Poli and Juan Carlos Niebles and Justin Johnson and Jiajun Wu and Li Fei-Fei},
      year={2026},
      eprint={2605.30341},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2605.30341}, 
}

Acknowledgments

We thank Radical Numerics and World Labs for providing compute for this project. We thank Willie Neiswanger, Yue Zhao, Armin W. Thomas, Garyk Brixi, Manling Li, Tristan Thrush, Wenlong Huang, Bailey Trang, and Aryaman Arora for their feedback on the manuscript. We thank Agrim Gupta for valuable discussions. We thank members of the Stanford Vision Lab and the CogAI group for their feedback.