OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation
  and Editing
Authors: name: Zhihong Chen, name: Xuehai Bai, name: Yang Shi, name: Chaoyou Fu, name: Huanyu Zhang, name: Haotian Wang, name: Xiaoyan Sun, name: Zhang Zhang, name: Liang Wang, name: Yuanxing Zhang, name: Pengfei Wan, name: Yi-Fan Zhang

The performance of unified multimodal models for image generation and editing
is fundamentally constrained by the quality and comprehensiveness of their
training data. While existing datasets have covered basic tasks like style
transfer and simple object manipulation, they often lack the systematic
structure and challenging scenarios required for real-world applications. To
address this bottleneck, we introduce OpenGPT-4o-Image, a large-scale dataset
constructed using a novel methodology that combines hierarchical task taxonomy
with automated data generation. Our taxonomy not only includes fundamental
capabilities such as text rendering and style control but also introduces
highly practical yet challenging categories like scientific imagery for
chemistry illustrations and complex instruction editing requiring simultaneous
execution of multiple operations. Through an automated pipeline leveraging
structured resource pools and GPT-4o, we generate 80k high-quality
instruction-image pairs with controlled diversity, covering 11 major domains
and 51 subtasks. Extensive experiments show that fine-tuning leading models on
our dataset achieves significant performance gains across multiple benchmarks,
with improvements of up to 18\% on editing tasks (UniWorld-V1 on ImgEdit-Bench)
and 13% on generation tasks (Harmon on GenEval). Our work demonstrates that
systematic data construction is key to advancing multimodal AI capabilities.