datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ramanv-image-editing-syntheticmulti_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-pairsidentity_preservation_image_editing
Identity Preservation Augmentation Dataset for Image Editing
Overview
This dataset contains algorithmically generated image pairs designed to
teach diffusion-based image editing models pixel-level identity
preservation — the ability to keep unchanged regions of an image exactly
intact while applying targeted edits.
Every example consists of a reference image, a target image, and a short
natural-language prompt. The transformation between reference and target is… See the full description on the dataset page: https://huggingface.co/datasets/molbal/identity_preservation_image_editing.ramanv-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.escher-ss2
Dataset Card for escher-ss2
SomethingSomethingv2 dataset
Dataset Structure
Data Instances
Each instance contains:
source_image: The original image
edited_image: The edited version of the image
edit_instruction: The instruction used to edit the image
source_image_caption: Caption for the source image
target_image_caption: Caption for the edited image
Additional metadata fields
Data Splits
{}
escher-aurora-kubric
Dataset Card for escher-aurora-kubric
Aurora-Kubric dataset
Dataset Structure
Data Instances
Each instance contains:
source_image: The original image
edited_image: The edited version of the image
edit_instruction: The instruction used to edit the image
source_image_caption: Caption for the source image
target_image_caption: Caption for the edited image
Additional metadata fields
Data Splits
{}
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}
}
escher-aurora-ag
Dataset Card for escher-aurora-ag
Aurora-AG dataset
Dataset Structure
Data Instances
Each instance contains:
source_image: The original image
edited_image: The edited version of the image
edit_instruction: The instruction used to edit the image
source_image_caption: Caption for the source image
target_image_caption: Caption for the edited image
Additional metadata fields
Data Splits
{}
escher-vismin
Dataset Card for escher-vismin
Vismin dataset
Dataset Structure
Data Instances
Each instance contains:
source_image: The original image
edited_image: The edited version of the image
edit_instruction: The instruction used to edit the image
source_image_caption: Caption for the source image
target_image_caption: Caption for the edited image
Additional metadata fields
Data Splits
{}
escher-human-edit
Dataset Card for escher-human-edit
Human Edit dataset
Dataset Structure
Data Instances
Each instance contains:
source_image: The original image
edited_image: The edited version of the image
edit_instruction: The instruction used to edit the image
source_image_caption: Caption for the source image
target_image_caption: Caption for the edited image
Additional metadata fields
Data Splits
{}
escher-magicbrush
Dataset Card for escher-magicbrush
MagicBrush dataset
Dataset Structure
Data Instances
Each instance contains:
source_image: The original image
edited_image: The edited version of the image
edit_instruction: The instruction used to edit the image
source_image_caption: Caption for the source image
target_image_caption: Caption for the edited image
Additional metadata fields
Data Splits
{}
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}
}
ramanv-image-editing-assembledramanv-image-editing-2pb-image-editing-10k-sftImage-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.
precise_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.pb-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}
}
ImageEditingRequestV1Text_Guided_Image_Editing_Base64image-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.Image-Editing-ver1NegGenBench2.0_image_based_editing_realprofessional-image-editing-dataset-sample
Professional Image Editing Dataset for AI Training — Sample
Professional human-edited visual data for training, fine-tuning and evaluating image-editing and generative AI models.
FixThePhoto produces image-editing training datasets based on professional retouching and visual-production workflows.
Typical data structures can include:
Source → Human-Edited Target
Source → Instruction → Human-Edited Target
AI Output → Human Correction
Masks and alpha mattes
Editing metadata
Human… See the full description on the dataset page: https://huggingface.co/datasets/FixThePhoto/professional-image-editing-dataset-sample.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
qwen_image_layered_editing_with_qwen_image_edit_magic_brush1.51-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.1.51-Million-Sets-of-Single-image-and-Multi-image-Fusion-Image-Editing-Data
Description
151만 그룹 규모의 단일 이미지 및 다중 이미지 융합 이미지 편집 데이터셋입니다. 편집 유형은 인물/객체 일관성 편집 50만 그룹, 구조 편집 30만 그룹, 혼합 편집 21만 그룹, 공간 편집 45만 그룹, 스타일 전환 편집 5만 그룹으로 구성됩니다. 편집 대상은 사람, 동물, 상품, 식물, 풍경 등 다양한 장면을 포함합니다. 어노테이션은 편집 지시에 따라 이미지에서 편집이 필요한 대상을 대상으로 수행되었습니다. 본 데이터셋은 이미지 합성, 데이터 증강, 가상 장면 생성 등의 작업에 활용할 수 있습니다.
자세한 내용은 아래 링크를 참고해 주세요: https://www.nexdata.ai/datasets/llm/2039?source=hf.kr
Specifications
Data size
151만 그룹
Target types
사람(클로즈업, 반신, 전신… See the full description on the dataset page: https://huggingface.co/datasets/Nexdata-kr/1.51-Million-Sets-of-Single-image-and-Multi-image-Fusion-Image-Editing-Data.