28万亿像素的开源图像数据集，支持视觉生成研究与商用。
GPIC: A Giant Permissive Image Corpus for Visual Generation
- 构建含1亿训练图的巨型开放图像集，自动标注并去重
- 数据经安全过滤，支持科研与商业使用，托管于Hugging Face
- 提供基准评测协议与像素空间生成模型基线
构建大规模、可访问且稳定的视觉生成建模数据集需依赖海量图像。本文提出GPIC，一个约28万亿像素的巨型开放图像语料库，包含1亿张训练图像、20万张验证图像和100万张测试图像，所有图像均通过先进视觉语言模型自动标注。所有图像均采用宽松许可，可用于研究与商业用途。GPIC经过安全过滤、去重处理，并在Hugging Face上集中托管。本文还提供了基于GPIC的生成建模评测协议及像素空间流匹配的参考基线模型。数据集、基准与模型已公开，详见https://huggingface.co/datasets/stanford-vision-lab/gpic；评估工具与代码见https://gpic.stanford.edu。
原文摘要 · Abstract (English)
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
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