image_edit
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.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.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.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.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.