GPIC: A Giant Permissive Image Corpus for Visual Generation
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 at https://huggingface.co/datasets/stanford-vision-lab/gpic. Evaluation toolkit and code are available at https://gpic.stanford.edu
Comments: 25 pages; Dataset: https://huggingface.co/datasets/stanford-vision-lab/giant-permissive-image-corpus; Project website: https://gpic.stanford.edu
Cite
@article{arxiv.2605.30341,
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},
journal= {arXiv preprint arXiv:2605.30341},
year = {2026}
}