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Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.

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01UCSC-VLAA /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.imageimage-to-image1M<n<10M90 likes7.2k downloads1y agoHugging Face02lingamvamshikrishnareddy /ramanv-image-editing-synthetic1 likes4k downloads15d agoHugging Face03molbal /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.imageimage-to-image10K<n<100K7 likes1.5k downloads3mo agoHugging Face04lingamvamshikrishnareddy /ramanv-image-editing-pairstext10K<n<100K0 likes1.1k downloads23d agoHugging Face05molbal /identity_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.image-to-image10K<n<100K4 likes1.1k downloads2mo agoHugging Face06lingamvamshikrishnareddy /ramanv-image-real-style-editorialtext10K<n<100K0 likes825 downloads21d agoHugging Face07meimeirun /GPT-Image-Edit-1M GPT-Image-Edit-1M Review Artifact GPT-Image-Edit-1M is a non-commercial research artifact for instruction-guided image editing. It contains GPT-Image-1 regenerated image-editing triplets, auditable quality-control metadata, and a 200-case human-audit package used to calibrate automated judges in the paper. License: CC BY-NC-SA 4.0, subject to upstream dataset licenses and applicable third-party service terms. Reviewer note. The Hugging Face Dataset Viewer shows a 400-row inspection… See the full description on the dataset page: https://huggingface.co/datasets/meimeirun/GPT-Image-Edit-1M.imageimage-to-imagen<1K3 likes575 downloads5mo agoHugging Face08lingamvamshikrishnareddy /ramanv-image-editinggated 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.image1K<n<10K6 likes523 downloads20d agoHugging Face09Image-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 {} image-to-imagen<1K0 likes475 downloads1y agoHugging Face10lee31221 /Outfit_Qwen-Image-Edit-2511_in_Kling Outfit_Qwen-Image-Edit-2511_in_Kling Synthetic outfit-swap pairs for Qwen-Image-Edit-2511 SFT (keyframe garment edit), generated with IDM-VTON as the teacher over VITON-HD. Batches Batches are separate directories in this one repo. Every batch uses a distinct (person, garment) pairing: no person is paired with the garment they already wear, and no pair is repeated across batches. batch_meta_*.json records the seed and the dedup counts, pairs_*.txt the exact… See the full description on the dataset page: https://huggingface.co/datasets/lee31221/Outfit_Qwen-Image-Edit-2511_in_Kling.imageimage-to-image10K<n<100K0 likes373 downloads1mo agoHugging Face1134data /gpt-image-edit-1-5m-hqedittext10K<n<100K0 likes357 downloads1mo agoHugging Face12Image-editing /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 {} image-to-imagen<1K0 likes333 downloads1y agoHugging Face13ImagenHub /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} } imageimage-to-imagen<1K26 likes298 downloads3y agoHugging Face14Image-editing /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 {} image-to-imagen<1K0 likes263 downloads1y agoHugging Face15tarn59 /character_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. imagen<1K5 likes248 downloads10mo agoHugging Face16Image-editing /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 {} image-to-imagen<1K0 likes209 downloads1y agoHugging Face17Image-editing /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 {} image-to-imagen<1K0 likes201 downloads1y agoHugging Face18Image-editing /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 {} image-to-imagen<1K0 likes174 downloads1y agoHugging Face19shirsh10mall /EP_ImageEdit0 likes169 downloads5mo agoHugging Face20ImagenHub /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} } imagen<1K3 likes157 downloads3y agoHugging Face21fizzlepoof /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.tabularn<1K1 likes122 downloads1mo agoHugging Face22guruawe /ramanv-image-editing-assembledgatedtext1M<n<10M0 likes111 downloads5d agoHugging Face23lingamvamshikrishnareddy /ramanv-image-editing-2gated0 likes110 downloads20d agoHugging Face24rjul0249 /pb-image-editing-10k-sftimage10K<n<100K0 likes106 downloads26d agoHugging Face25KingNish /Image-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. texttext-classification1K<n<10K8 likes84 downloads2y agoHugging Face26monurcan /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.image1K<n<10K5 likes81 downloads1y agoHugging Face27pwnyourace /pb-image-editing-10k-sftimage10K<n<100K0 likes79 downloads25d agoHugging Face28ImagenHub /Subject_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} } imagen<1K3 likes77 downloads3y agoHugging Face29taesiri /ImageEditingRequestV1image1K<n<10K4 likes77 downloads2y agoHugging Face30shenzhebei /Qwen-image-edit-lora0 likes69 downloads4mo agoHugging Face

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