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The authors introduce GPIC, a large-scale (28 trillion pixels) image dataset with 100M training examples, captioned using a state-of-the-art vision-language model and permissively licensed for research and commercial use. The dataset is safety-filtered, deduplicated, and hosted on Hugging Face, addressing the need for large, accessible, and stable datasets for visual generative modeling. A pixel-space flow matching baseline is provided, along with a benchmarking protocol to facilitate future research.
Training generative models just got a whole lot easier: GPIC offers 100M permissively licensed, captioned, and safety-filtered images.
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
Stanford HAI