OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation
  and Editing
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.
68db4e25d2bf1f4b15ec747c
Zhihong Chen
68db4e25d2bf1f4b15ec747d
Xuehai Bai
68db4e25d2bf1f4b15ec747e
Yang Shi
claimed_verified
2025-09-30T06:34:34.223Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/673c7319d11b1c2e246ead9c/IjFIO--N7Hm_BOEafhEQv.jpeg
Yang Shi
user
DogNeverSleep
68db4e25d2bf1f4b15ec747f
Chaoyou Fu
68db4e25d2bf1f4b15ec7480
Huanyu Zhang
68db4e25d2bf1f4b15ec7481
Haotian Wang
68db4e25d2bf1f4b15ec7482
Xiaoyan Sun
68db4e25d2bf1f4b15ec7483
Zhang Zhang
68db4e25d2bf1f4b15ec7484
Liang Wang
68db4e25d2bf1f4b15ec7485
Yuanxing Zhang
68db4e25d2bf1f4b15ec7486
Pengfei Wan
68db4e25d2bf1f4b15ec7487
Yi-Fan Zhang
OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation
  and Editing
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.
OpenGPT-4o-Image, a large-scale dataset with hierarchical task taxonomy and automated generation, significantly improves performance in image generation and editing tasks.
unified multimodal models image generation image editing training data hierarchical task taxonomy automated data generation text rendering style control scientific imagery complex instruction editing instruction-image pairs controlled diversity fine-tuning UniWorld-V1 ImgEdit-Bench Harmon GenEval