GPIC: A Giant Permissive Image Corpus for Visual Generation
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918530115436544 |
|---|---|
| author | Chandrasegaran, Keshigeyan Sargent, Kyle Agarwal, Suchir Jang, Michael Poli, Michael Niebles, Juan Carlos Johnson, Justin Wu, Jiajun Fei-Fei, Li |
| author_facet | Chandrasegaran, Keshigeyan Sargent, Kyle Agarwal, Suchir Jang, Michael Poli, Michael Niebles, Juan Carlos Johnson, Justin Wu, Jiajun Fei-Fei, Li |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30341 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | GPIC: A Giant Permissive Image Corpus for Visual Generation Chandrasegaran, Keshigeyan Sargent, Kyle Agarwal, Suchir Jang, Michael Poli, Michael Niebles, Juan Carlos Johnson, Justin Wu, Jiajun Fei-Fei, Li Computer Vision and Pattern Recognition Artificial Intelligence 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 |
| title | GPIC: A Giant Permissive Image Corpus for Visual Generation |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.30341 |