OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing
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arXiv
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| Autores principales: | , , , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912615131774976 |
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| author | Chen, Zhihong Bai, Xuehai Shi, Yang Fu, Chaoyou Zhang, Huanyu Wang, Haotian Sun, Xiaoyan Zhang, Zhang Wang, Liang Zhang, Yuanxing Wan, Pengfei Zhang, Yi-Fan |
| author_facet | Chen, Zhihong Bai, Xuehai Shi, Yang Fu, Chaoyou Zhang, Huanyu Wang, Haotian Sun, Xiaoyan Zhang, Zhang Wang, Liang Zhang, Yuanxing Wan, Pengfei Zhang, Yi-Fan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24900 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing Chen, Zhihong Bai, Xuehai Shi, Yang Fu, Chaoyou Zhang, Huanyu Wang, Haotian Sun, Xiaoyan Zhang, Zhang Wang, Liang Zhang, Yuanxing Wan, Pengfei Zhang, Yi-Fan Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.24900 |