OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing

Kari Jaaskelainen

30 Sep 2025 — 1 min read

80k instruction–image pairs across 11 domains and 51 subtasks, built with systematic taxonomy.

Unified multimodal models stumble when their training data underrepresents real-world complexity. OpenGPT-4o-Image answers with a large-scale dataset constructed via a hierarchical task taxonomy and automated generation pipeline. Beyond basics like text rendering and style transfer, it introduces challenging, practical categories such as scientific imagery (e.g., chemistry illustrations) and multi-operation editing under complex instructions.

The pipeline combines structured resource pools with GPT‑4o to synthesize 80k high-quality instruction–image pairs spanning 11 domains and 51 subtasks, with controlled diversity and difficulty. Fine-tuning leading models on this dataset delivers substantial gains: up to +18% on editing tasks (e.g., UniWorld‑V1 on ImgEdit‑Bench) and +13% on generation benchmarks (e.g., Harmon on GenEval).

Why it matters: Capable multimodal systems require training data that systematically covers core abilities and edge cases alike. By formalizing a taxonomy and automating data creation across it, OpenGPT-4o-Image provides a blueprint—and a resource—for advancing both image generation and precise, instruction-following editing.

Hierarchical taxonomy: from fundamentals to complex, real-world scenarios.
Automated synthesis: consistent, scalable, and diverse data creation.
Measured impact: sizable improvements across editing and generation benchmarks.

Paper: arXiv: OpenGPT-4o-Image
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#Multimodal #ImageGeneration #ImageEditing #Dataset #Taxonomy #ComputerVision #GenAI #Benchmarking

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