GPT-IMAGE-EDIT-1.5M: A Million-Scale, GPT-Generated Image Dataset
Authors: name: Yuhan Wang, name: Siwei Yang, name: Bingchen Zhao, name: Letian Zhang, name: Qing Liu, name: Yuyin Zhou, name: Cihang Xie

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.