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.ImagePulseV2-Edit-Structure
ImagePulseV2 Dataset - Image Structure
The ImagePulseV2 dataset is a collection we constructed for training the Diffusion Templates series of models. It comprises multiple subsets generated using models such as Z-Image-Turbo, Qwen-Image, and Qwen-Image-Edit, based on prompts randomly sampled from DiffusionDB.
Open-source code: DiffSynth-Studio
Technical report: arXiv
Project homepage: GitHub
Documentation: English Version, Chinese Version
Online demo: ModelScope Studio
Model… See the full description on the dataset page: https://huggingface.co/datasets/DiffSynth-Studio/ImagePulseV2-Edit-Structure.droid_low_resolutionUni-Edit-Train-Data
Uni-Edit Training Data: Uni-Edit-148k
Project Page | GitHub Repository | Paper
👀 Intro
We introduce Uni-Edit, an intelligent image editing task that serves as the first general task for Unified Multimodal Model (UMM) tuning. Unlike conventional mixed multi-task training that suffers from inherent task conflicts and requires complex multi-stage pipelines, Uni-Edit breaks this paradigm. It achieves true mutual reinforcement by improving image… See the full description on the dataset page: https://huggingface.co/datasets/Uni-Edit/Uni-Edit-Train-Data.Inter-Edit-Test
Inter-Edit-Test
Official test benchmark release for the CVPR 2026 paper:
Inter-Edit: First Benchmark for Interactive Instruction-Based Image Editing
This repository hosts the public release of Inter-Edit-Test, a human-annotated benchmark for the Interactive Instruction-based Image Editing (I^3E) task.
Each sample contains:
a source image,
a coarse user-style interaction mask,
a concise editing instruction,
and a ground-truth edited image.
To simplify large-scale distribution on… See the full description on the dataset page: https://huggingface.co/datasets/a1557811266/Inter-Edit-Test.HQ-EditNano3D-Edit-100k
Nano3D-Edit-100k
This dataset is the official data release for Nano3D, a training-free framework for precise and coherent 3D object editing without masks.
Paper: Nano3D: A Training-Free Approach for Efficient 3D Editing Without MasksProject Page: https://jamesyjl.github.io/Nano3D/
Nano3D integrates FlowEdit into TRELLIS to perform localized 3D edits guided by front-view renderings, and introduces Voxel/Slat-Merge strategies to preserve structural consistency between edited and… See the full description on the dataset page: https://huggingface.co/datasets/yejunliang23/Nano3D-Edit-100k.Sleep-EDF-V2Noob-Edit-OSData
Acknowledgements
The computing resources are sponsored by FishAudio & Feelin, All rights reserved to 39 AI Inc.
About data
Current version's data is for Noob-Edit-v0.1, including several editing tasks and natural language to anime image. Please refer to README in the folder for more info (Chinese only).
edgar-2021-2024camera_pose_editMulti-turn-editingedit-imageedit_image_version2edu-ptbreditsplat_renderedP1_dataseteditedNoob-Edit
Acknowledgements
The computing resources are sponsored by FishAudio & Feelin, All rights reserved to 39 AI Inc.
About data
Current version's data is for Noob-Edit-v0.1, including several editing tasks and natural language to anime image. Please refer to README in the folder for more info (Chinese only).
vl8-edge-noise-bank
vl8-edge noise bank
Curated noise bundle for vl8-edge-sdk's
synth.augment pipeline (issue #9). Repackaged from the
DEMAND corpus on Zenodo.
License: CC-BY-4.0
Source: DEMAND — Joachim Thiemann, Nobutaka Ito, Emmanuel Vincent
Format: 16 kHz mono 16-bit PCM, 6 channels (3 cafe + 3 street), ~40 MB
The SDK lazy-downloads noise-bank-v1.tar.gz from this repo on first
vl8 build --augment and verifies per-file SHA256 against the manifest at… See the full description on the dataset page: https://huggingface.co/datasets/ggix/vl8-edge-noise-bank.octo_dlr_edan_shared_controlflux_image_edit_masksflux_image_edittemp_editeditflow-eval-datasets
EditFlow evaluation datasets
librispeech_pc_testset.tar.gz
realedit.tar.gz