# GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset

arXiv: https://arxiv.org/abs/2507.21033 · HTML: https://mlsanity.com/paper/2507.21033/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- Placed in Image editing and quality assessment, one of 16 directions in Multimodal LLMs / vision-language models.
- Builds on 24 of its 27 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Image editing and quality assessment](https://mlsanity.com/mllm/image-editing-and-quality-assessment/)

## Builds on (most cited first, 24 of 24)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Visual Instruction Tuning](https://mlsanity.com/paper/2304.08485/) (2023)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Sigmoid Loss for Language Image Pre-Training](https://mlsanity.com/paper/2303.15343/) (2023)
- [InstructPix2Pix: Learning to Follow Image Editing Instructions](https://mlsanity.com/paper/2211.09800/) (2022)
- [Prompt-to-Prompt Image Editing with Cross Attention Control](https://mlsanity.com/paper/2208.01626/) (2022)
- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [Imagic: Text-Based Real Image Editing with Diffusion Models](https://mlsanity.com/paper/2210.09276/) (2022)
- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [Step1X-Edit: A Practical Framework for General Image Editing](https://mlsanity.com/paper/2504.17761/) (2025)
- [UltraEdit: Instruction-based Fine-Grained Image Editing at Scale](https://mlsanity.com/paper/2407.05282/) (2024)
- [OmniGen2: Towards Instruction-Aligned Multimodal Generation](https://mlsanity.com/paper/2506.18871/) (2025)
- [ImgEdit: A Unified Image Editing Dataset and Benchmark](https://mlsanity.com/paper/2505.20275/) (2025)
- [HQ-Edit: A High-Quality Dataset for Instruction-based Image Editing](https://mlsanity.com/paper/2404.09990/) (2024)
- [OmniEdit: Building Image Editing Generalist Models Through Specialist Supervision](https://mlsanity.com/paper/2411.07199/) (2024)
- [ShareGPT-4o-Image: Aligning Multimodal Models with GPT-4o-Level Image Generation](https://mlsanity.com/paper/2506.18095/) (2025)
- [SeedEdit: Align Image Re-Generation to Image Editing](https://mlsanity.com/paper/2411.06686/) (2024)
- [SeedEdit 3.0: Fast and High-Quality Generative Image Editing](https://mlsanity.com/paper/2506.05083/) (2025)
- [textttComplex-Edit: CoT-Like Instruction Generation for Complexity-Controllable Image Editing Benchmark](https://mlsanity.com/paper/2504.13143/) (2025)

## Built on by (most cited first, 30 of 70)

- [Emu3.5: Native Multimodal Models are World Learners](https://mlsanity.com/paper/2510.26583/) (2025)
- [Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation](https://mlsanity.com/paper/2508.09987/) (2025)
- [EditReward: A Human-Aligned Reward Model for Instruction-Guided Image Editing](https://mlsanity.com/paper/2509.26346/) (2025)
- [Pico-Banana-400K: A Large-Scale Dataset for Text-Guided Image Editing](https://mlsanity.com/paper/2510.19808/) (2025)
- [NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale](https://mlsanity.com/paper/2508.10711/) (2025)
- [EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning](https://mlsanity.com/paper/2509.20360/) (2025)
- [OpenVE-3M: A Large-Scale High-Quality Dataset for Instruction-Guided Video Editing](https://mlsanity.com/paper/2512.07826/) (2025)
- [Reconstruction Alignment Improves Unified Multimodal Models](https://mlsanity.com/paper/2509.07295/) (2025)
- [Skywork UniPic 2.0: Building Kontext Model with Online RL for Unified Multimodal Model](https://mlsanity.com/paper/2509.04548/) (2025)
- [Kiwi-Edit: Versatile Video Editing via Instruction and Reference Guidance](https://mlsanity.com/paper/2603.02175/) (2026)
- [InstructX: Towards Unified Visual Editing with MLLM Guidance](https://mlsanity.com/paper/2510.08485/) (2025)
- [InternVL-U: Democratizing Unified Multimodal Models for Understanding, Reasoning, Generation and Editing](https://mlsanity.com/paper/2603.09877/) (2026)
- [Factuality Matters: When Image Generation and Editing Meet Structured Visuals](https://mlsanity.com/paper/2510.05091/) (2025)
- [VIVA: VLM-Guided Instruction-Based Video Editing with Reward Optimization](https://mlsanity.com/paper/2512.16906/) (2025)
- [Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation](https://mlsanity.com/paper/2509.19244/) (2025)
- [SliderEdit: Continuous Image Editing with Fine-Grained Instruction Control](https://mlsanity.com/paper/2511.09715/) (2025)
- [DeepGen 1.0: A Lightweight Unified Multimodal Model for Advancing Image Generation and Editing](https://mlsanity.com/paper/2602.12205/) (2026)
- [EasyV2V: A High-quality Instruction-based Video Editing Framework](https://mlsanity.com/paper/2512.16920/) (2025)
- [ThinkGen: Generalized Thinking for Visual Generation](https://mlsanity.com/paper/2512.23568/) (2025)
- [EditMGT: Unleashing Potentials of Masked Generative Transformers in Image Editing](https://mlsanity.com/paper/2512.11715/) (2025)
- [Quantifying the Gap between Understanding and Generation within Unified Multimodal Models](https://mlsanity.com/paper/2602.02140/) (2026)
- [JoyAI-Image: Awaking Spatial Intelligence in Unified Multimodal Understanding and Generation](https://mlsanity.com/paper/2605.04128/) (2026)
- [Bernini: Latent Semantic Planning for Video Diffusion](https://mlsanity.com/paper/2605.22344/) (2026)
- [TBAC-UniImage: Unified Understanding and Generation by Ladder-Side Diffusion Tuning](https://mlsanity.com/paper/2508.08098/) (2025)
- [UnicEdit-10M: A Dataset and Benchmark Breaking the Scale-Quality Barrier via Unified Verification for Reasoning-Enriched Edits](https://mlsanity.com/paper/2512.02790/) (2025)
- [SuperFlow: Training Flow Matching Models with RL on the Fly](https://mlsanity.com/paper/2512.17951/) (2025)
- [SpatialEdit: Benchmarking Fine-Grained Image Spatial Editing](https://mlsanity.com/paper/2604.04911/) (2026)
- [LightFusion: A Light-weighted, Double Fusion Framework for Unified Multimodal Understanding and Generation](https://mlsanity.com/paper/2510.22946/) (2025)
- [MammothModa2: A Unified AR-Diffusion Framework for Multimodal Understanding and Generation](https://mlsanity.com/paper/2511.18262/) (2025)
- [Trust Your Critic: Robust Reward Modeling and Reinforcement Learning for Faithful Image Editing and Generation](https://mlsanity.com/paper/2603.12247/) (2026)

## Abstract (arXiv)

Recent advancements in large multimodal models like GPT-4o have set a new standard for high-fidelity, instruction-guided image editing. However, the proprietary nature of these models and their training data creates a significant barrier for open-source research. To bridge this gap, we introduce GPT-IMAGE-EDIT-1.5M, a publicly available, large-scale image-editing corpus containing more than 1.5 million high-quality triplets (instruction, source image, edited image). We systematically construct this dataset by leveraging the versatile capabilities of GPT-4o to unify and refine three popular image-editing datasets: OmniEdit, HQ-Edit, and UltraEdit. Specifically, our methodology involves 1) regenerating output images to enhance visual quality and instruction alignment, and 2) selectively rewriting prompts to improve semantic clarity. To validate the efficacy of our dataset, we fine-tune advanced open-source models on GPT-IMAGE-EDIT-1.5M. The empirical results are exciting, e.g., the fine-tuned FluxKontext achieves highly competitive performance across a comprehensive suite of benchmarks, including 7.24 on GEdit-EN, 3.80 on ImgEdit-Full, and 8.78 on Complex-Edit, showing stronger instruction following and higher perceptual quality while maintaining identity. These scores markedly exceed all previously published open-source methods and substantially narrow the gap to leading proprietary models. We hope the full release of GPT-IMAGE-EDIT-1.5M can help to catalyze further open research in instruction-guided image editing.