ImgEdit: A Unified Image Editing Dataset and Benchmark
Authors: name: Yang Ye, name: Xianyi He, name: Zongjian Li, name: Bin Lin, name: Shenghai Yuan, name: Zhiyuan Yan, name: Bohan Hou, name: Li Yuan

Recent advancements in generative models have enabled high-fidelity
text-to-image generation. However, open-source image-editing models still lag
behind their proprietary counterparts, primarily due to limited high-quality
data and insufficient benchmarks. To overcome these limitations, we introduce
ImgEdit, a large-scale, high-quality image-editing dataset comprising 1.2
million carefully curated edit pairs, which contain both novel and complex
single-turn edits, as well as challenging multi-turn tasks. To ensure the data
quality, we employ a multi-stage pipeline that integrates a cutting-edge
vision-language model, a detection model, a segmentation model, alongside
task-specific in-painting procedures and strict post-processing. ImgEdit
surpasses existing datasets in both task novelty and data quality. Using
ImgEdit, we train ImgEdit-E1, an editing model using Vision Language Model to
process the reference image and editing prompt, which outperforms existing
open-source models on multiple tasks, highlighting the value of ImgEdit and
model design. For comprehensive evaluation, we introduce ImgEdit-Bench, a
benchmark designed to evaluate image editing performance in terms of
instruction adherence, editing quality, and detail preservation. It includes a
basic testsuite, a challenging single-turn suite, and a dedicated multi-turn
suite. We evaluate both open-source and proprietary models, as well as
ImgEdit-E1, providing deep analysis and actionable insights into the current
behavior of image-editing models. The source data are publicly available on
https://github.com/PKU-YuanGroup/ImgEdit.