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

Kari Jaaskelainen

30 Sep 2025 — 1 min read

A structured 80k instruction–image corpus spanning 11 domains and 51 subtasks to train unified visual editors

Unified models for image generation and editing hit a data ceiling: existing corpora emphasize basic manipulations but miss real‑world complexity. OpenGPT‑4o‑Image tackles this with a hierarchical task taxonomy and automated data generation that together yield 80k high‑quality instruction–image pairs covering 11 domains and 51 subtasks.

Beyond fundamentals like text rendering and style transfer, the dataset introduces practical challenges—scientific imagery (e.g., chemistry diagrams), multi‑constraint edits, and compositional instructions requiring several operations in sequence. A structured resource pool plus GPT‑4o‑driven generation ensures controlled diversity while keeping instructions faithful to downstream capabilities.

Fine‑tuning leading models on OpenGPT‑4o‑Image produces substantial gains reported by the authors: up to 18% on editing tasks (e.g., UniWorld‑V1 on ImgEdit‑Bench) and 13% on generation tasks (e.g., Harmon on GenEval). The takeaway: systematic data construction—taxonomy first, then targeted synthesis—advances multimodal capability more than scaling simplistic examples.

Why it matters: as vision–language models converge on unified interfaces, training data must reflect real use cases—compound edits, domain specificity, and precise control. This dataset offers a blueprint and a benchmarkable resource for the community.

Paper: arXiv: OpenGPT‑4o‑Image
Register: AiFeta

#Multimodal #ImageEditing #ImageGeneration #Dataset #VisionLanguage #Benchmarking #GenAI

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