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

arXiv: https://arxiv.org/abs/2509.24900 · HTML: https://mlsanity.com/paper/2509.24900/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Sep 2025.

## Built on by (most cited first, 28 of 28)

- [InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing](https://mlsanity.com/paper/2603.09877/) (2026)
- [Uni-MoE-2.0-Omni: Scaling Language-Centric Omnimodal Large Model with Advanced MoE, Training and Data](https://mlsanity.com/paper/2511.12609/) (2025)
- [DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing](https://mlsanity.com/paper/2602.12205/) (2026)
- [How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing](https://mlsanity.com/paper/2602.01851/) (2026)
- [UniReason 1.0: A Unified Reasoning Framework for World Knowledge Aligned Image Generation and Editing](https://mlsanity.com/paper/2602.02437/) (2026)
- [Scone: Bridging Composition and Distinction in Subject-Driven Image Generation via Unified Understanding-Generation Modeling](https://mlsanity.com/paper/2512.12675/) (2025)
- [LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model](https://mlsanity.com/paper/2603.01068/) (2026)
- [Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation](https://mlsanity.com/paper/2603.12247/) (2026)
- [WeEdit: A Dataset, Benchmark and Glyph-Guided Framework for Text-centric Image Editing](https://mlsanity.com/paper/2603.11593/) (2026)
- [Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding](https://mlsanity.com/paper/2602.00854/) (2026)
- [Edit-Compass & EditReward-Compass: A Unified Benchmark for Image Editing and Reward Modeling](https://mlsanity.com/paper/2605.13062/) (2026)
- [ScaleEdit-12M: Scaling Open-Source Image Editing Data Generation via Multi-Agent Framework](https://mlsanity.com/paper/2603.20644/) (2026)
- [Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling](https://mlsanity.com/paper/2604.28185/) (2026)
- [Are Image-to-Video Models Good Zero-Shot Image Editors?](https://mlsanity.com/paper/2511.19435/) (2025)
- [Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos](https://mlsanity.com/paper/2605.18984/) (2026)
- [UniDDT: Unifying Multimodal Understanding and Generation with Decoupled Diffusion Transformer](https://mlsanity.com/paper/2606.16255/) (2026)
- [DuoGen: Towards General Purpose Interleaved Multimodal Generation](https://mlsanity.com/paper/2602.00508/) (2026)
- [FlashAR: Efficient Post-Training Acceleration for Autoregressive Image Generation](https://mlsanity.com/paper/2605.09430/) (2026)
- [GenFirst: Generation Before Reconstruction for Stable End-to-End Latent Generative Modeling](https://mlsanity.com/paper/2608.29335/) (2026)
- [WeMMU: Enhanced Bridging of Vision-Language Models and Diffusion Models via Noisy Query Tokens](https://mlsanity.com/paper/2512.02536/) (2025)
- [MCIE: Multimodal LLM-Driven Complex Instruction Image Editing with Spatial Guidance](https://mlsanity.com/paper/2602.07993/) (2026)
- [Best of Both Worlds: Multimodal Reasoning and Generation via Unified Discrete Flow Matching](https://mlsanity.com/paper/2602.12221/) (2026)
- [Illuminating Visual Identity in Universal Multimodal Embeddings](https://mlsanity.com/paper/2608.01794/) (2026)
- [A Simple Baseline for Unifying Understanding, Generation, and Editing via Vanilla Next-token Prediction](https://mlsanity.com/paper/2603.04980/) (2026)
- [Automated In-the-Wild Data Collection for Continual AI Generated Image Detection](https://mlsanity.com/paper/2605.02567/) (2026)
- [Inline Critic Steers Image Editing](https://mlsanity.com/paper/2605.12724/) (2026)
- [CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences](https://mlsanity.com/paper/2606.00931/) (2026)
- [Think Before You Score: Thinking Reward Model for Visual Generation](https://mlsanity.com/paper/2609.37372/) (2026)

## Abstract (arXiv)

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.