license: apache-2.0
tags:
- code
size_categories:
- 1K<n<10K
dataset_info:
features:
- name: id
dtype: string
- name: source_prompt
dtype: string
- name: target_prompt
dtype: string
- name: image
dtype: image
- name: image_width
dtype: int32
- name: image_height
dtype: int32
splits:
- name: test
num_bytes: 29728028
num_examples: 80
download_size: 29723807
dataset_size: 29728028
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
SNR-Edit: Structure-Aware Noise Rectification for Inversion-Free Flow-Based Editing
Yongyuan Chen Yan Zhao Shiyu Yu Deng Cai
🚧 Coming Soon
SNR-Bench comprises 80 high-quality image-editing cases. Approximately 50% are sampled from PIE-Bench to ensure continuity with standard benchmarks, and the remaining 50% are collected from the web to introduce richer textures and more complex real-world scenes. We cover four editing operations: adjust, change, remove, and add. To minimize ambiguity and improve instruction consistency, all editing instructions for the non--PIE-Bench subset are manually written, refined, and verified through human annotation.
The dataset is currently being prepared and will be released soon.
This repository contains the SNR-Bench dataset used in the paper SNR-Edit: Structure-Aware Noise Rectification for Inversion-Free Flow-Based Editing.
📌 Citation
If you find this dataset helpful, please consider citing:
@misc{jiang2026snreditstructureawarenoiserectification,
title={SNR-Edit: Structure-Aware Noise Rectification for Inversion-Free Flow-Based Editing},
author={Lifan Jiang and Boxi Wu and Yuhang Pei and Tianrun Wu and Yongyuan Chen and Yan Zhao and Shiyu Yu and Deng Cai},
year={2026},
eprint={2601.19180},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2601.19180},
}