datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
GPT-Image-Edit-1.5M
GPT-Image-Edit-1.5M A Million-Scale, GPT-Generated Image Dataset
📃Arxiv | 🌐 Project Page | 💻Github
GPT-Image-Edit-1.5M is a comprehensive image editing dataset that is built upon HQ-Edit, UltraEdit, OmniEdit and Complex-Edit, with all output images regenerated with GPT-Image-1.
📣 News
[2025.08.20] 🚀 We provide a script for multi-process downloading. See Multi-process Download.
[2025.07.27] 🤗 We release GPT-Image-Edit, a state-of-the-art image editing model with… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/GPT-Image-Edit-1.5M.multi_reference_image_editing
Multi-Reference Instruction-Based Image Editing Dataset
Overview
This dataset contains 20,000 high-resolution image pairs and multi-modal instructions designed for training advanced image-to-image editing models. It combines two complementary example types: 10,000 reference-grounded edits, where structural or stylistic changes are driven by up to three provided visual reference images, and 10,000 occlusion-based inpainting/outpainting edits, where the model must… See the full description on the dataset page: https://huggingface.co/datasets/molbal/multi_reference_image_editing.ramanv-image-editing-pairsramanv-image-real-style-editorialramanv-image-editing
ramanv-image-editing
Image editing dataset for training FLUX.1-Kontext / InstructPix2Pix style models.
Size
592,141 total editing pairs
Sources: ultraedit
Schema
Each shard tar contains {uid}_src.jpg, {uid}_edit.jpg, {uid}_mask.png (where available).
Metadata per record: instruction, prompt, edit_type, caption_before/after, license, sha256.
Licenses
MagicBrush, InstructPix2Pix, Pico-Banana, HumanEdit: CC-BY-4.0
UltraEdit, AnyEdit… See the full description on the dataset page: https://huggingface.co/datasets/lingamvamshikrishnareddy/ramanv-image-editing.Text_Guided_Image_Editing
Dataset Card
Dataset in ImagenHub.
Citation
Please kindly cite our paper if you use our code, data, models or results:
@article{ku2023imagenhub,
title={ImagenHub: Standardizing the evaluation of conditional image generation models},
author={Max Ku and Tianle Li and Kai Zhang and Yujie Lu and Xingyu Fu and Wenwen Zhuang and Wenhu Chen},
journal={arXiv preprint arXiv:2310.01596},
year={2023}
}
gpt-image-edit-1-5m-hqeditcharacter_turnaround_sheet_qwen_image_edit_2509_datasetBase images were generated by Qwen Image and I used Wan to do 360 degree rotation. I then took frames from the rotation and concatenated them together using imagemagick.
Mask_Guided_Image_Editing
Dataset Card
Dataset in ImagenHub.
Citation
Please kindly cite our paper if you use our code, data, models or results:
@article{ku2023imagenhub,
title={ImagenHub: Standardizing the evaluation of conditional image generation models},
author={Max Ku and Tianle Li and Kai Zhang and Yujie Lu and Xingyu Fu and Wenwen Zhuang and Wenhu Chen},
journal={arXiv preprint arXiv:2310.01596},
year={2023}
}
MMH3_Image_Edit_WorkflowThis is just an example of using MiniMax H3 as an image editor. The actual workflow that I use requires several custom nodes, some of which are not published, so this one is simply a bare bones demonstration.
This uses the hybrid MiniMax H3 model from here: https://huggingface.co/smhfacct/Minimax-H3-fl2va-ref2va-hybrid-models/tree/main
It uses the custom VAE from here: https://huggingface.co/Mamad8/MiniMax-H3-Image-VAE/tree/main
It uses the LoRA from here:… See the full description on the dataset page: https://huggingface.co/datasets/fizzlepoof/MMH3_Image_Edit_Workflow.ramanv-image-editing-assembledImage-Gen-or-Image-Editing
Image Gen or Image Editing
This dataset is designed for text classification of prompts provided by users. It determines whether a prompt is intended for image generation or image editing.
pb-image-editing-10k-sftprecise_benchmark_for_object_level_image_editing
VOCEdits: A benchmark for precise geometric object-level editing
Sample format: (input image, edit prompt, input mask, ground-truth output mask, ...)
Please refer to our paper for more details: "📜 POEM: Precise Object-level Editing via MLLM control", SCIA 2025.
How to Evaluate?
Before evaluation, you should first generate your edited images.
Use datasets library to download dataset. You should only use input image, edit prompt, and id columns to generate edited images.… See the full description on the dataset page: https://huggingface.co/datasets/monurcan/precise_benchmark_for_object_level_image_editing.Text_Guided_Image_Editing_Base64pb-image-editing-10k-sftSubject_Driven_Image_Editing
Dataset Card
Dataset in ImagenHub.
Citation
Please kindly cite our paper if you use our code, data, models or results:
@article{ku2023imagenhub,
title={ImagenHub: Standardizing the evaluation of conditional image generation models},
author={Max Ku and Tianle Li and Kai Zhang and Yujie Lu and Xingyu Fu and Wenwen Zhuang and Wenhu Chen},
journal={arXiv preprint arXiv:2310.01596},
year={2023}
}
ImageEditingRequestV1Image-Editing-ver1character_turnaround_sheet_qwen_image_edit_2509_datasetBase images were generated by Qwen Image and I used Wan to do 360 degree rotation. I then took frames from the rotation and concatenated them together using imagemagick.
image-editing-style-instruction-following
Image Editing Style Instruction-Following Dataset
A multimodal dataset for evaluating and training models on style-guided image editing via natural language instruction-following. Each sample contains a reference style image, human-written editing instructions, and multiple style-transferred output images.
Overview
Item
Details
Samples
19 sets
Images per sample
1 input (style reference) + 3 outputs (style-transferred)
Total images
76 (19 inputs + 57… See the full description on the dataset page: https://huggingface.co/datasets/obaydata/image-editing-style-instruction-following.multi-modal-image-editgpt-image-edit-benchmark-results
GPT-Image-Edit — Benchmark Results
This repository contains evaluation results of GPT-Image-Edit across four standard image-editing benchmarks. All scores were computed using the official evaluation scripts provided by each benchmark.
📊 Benchmarks
Benchmark
Metrics
Folder
GEdit-EN
12 editing categories + Avg
gedit/
Complex-Edit
IF, IP, PQ, Overall
complex_edit/
ImgEdit-Full
10 editing operations + Overall
imgedit/
OmniContext
Contextual edit scores… See the full description on the dataset page: https://huggingface.co/datasets/UCSC-VLAA/gpt-image-edit-benchmark-results.multi-edit-image-pairs
Image Editing Dataset
This dataset contains image editing examples with instructions.
Dataset Structure
instruction: Text instruction for editing
original_image: Original image before editing
edited_image: Image after applying the edit
68750-Sets-of-Image-Editing-Reasoning-Based-Editing-Data
Description
68,750개 규모의 추론 기반 이미지 편집(Reasoning-Based Editing) 데이터셋으로, 지식 기반 편집 62,500개와 물리 규칙 기반 편집 6,250개로 구성됩니다. 다양한 장면, 카테고리 및 문제 유형을 포함하며, 각 데이터는 원본 이미지, 편집 결과 이미지, 중국어 텍스트 문서 및 영어 텍스트 문서로 구성됩니다. 대부분의 카테고리는 제공된 지시에 따라 편집 과정에서 추론이 필요한 데이터를 포함합니다.
원본 이미지는 문서에 제공된 지시에 따라 편집하여 결과 이미지를 생성했으며, 이미지와 텍스트 내용의 매칭 정확도는 95% 이상입니다. 본 데이터셋은 가상 장면 생성, 이미지 합성, 데이터 증강 및 추론 기반 이미지 편집 등의 작업에 활용할 수 있습니다.
자세한 내용은 아래 링크를 참고해 주세요: https://ko.nexdata.ai/datasets/llm/2125?source=Hf.kr… See the full description on the dataset page: https://huggingface.co/datasets/Nexdata-kr/68750-Sets-of-Image-Editing-Reasoning-Based-Editing-Data.qwen_image_layered_editing_with_qwen_image_edit_magic_brushapply_texture_qwen_image_edit_2509_datasetAll input images were created using Qwen-Image (w/ lightning lora) and the outputs were generated using Qwen-Image-Edit-2509. No external sources were used to generate this dataset.
1.51-Million-Single-Image-Editing-Sample-Data
1.51-Million-Sets-of-Single-image-and-Multi-image-Fusion-Image-Editing-Data
Description
This dataset is just a sample of 1.51 Million Sets of Single-image and Multi-image Fusion Image Editing Data. Editing types include 500,000 sets of portrait/object consistency editing, 300,000 sets of structural edits, 210,000 sets of mixed editing, and 450,000 sets of spatial editing, and 50,000 sets of style transfer editing. The editing targets cover scenes such as people, animals… See the full description on the dataset page: https://huggingface.co/datasets/Nexdata-AI/1.51-Million-Single-Image-Editing-Sample-Data.NegGenBench2.0_image_based_editing_realsafe-vs-unsafe-image-edits-batch2