The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment
This repository hosts ImageCritic, a reference-guided post-editing approach designed to correct inconsistencies in generated images. It aims to solve the inconsistency problem in generated images by applying attention alignment and a detail encoder, providing significant improvements over existing methods in various customized generation scenarios.
The model was presented in the paper The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment.
🖼️ Visual Results
ImageCritic can effectively resolve detail-related issues in various customized generation scenarios, providing significant improvements over existing methods.
Online HuggingFace Demo
You can try ImageCritic demo on HuggingFace.
Citation
If you find this project useful for your research, please consider citing our paper:
@article{ouyang2025consistency,
title={The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment},
author={Ouyang, Ziheng and Song, Yiren and Liu, Yaoli and Zhu, Shihao and Hou, Qibin and Cheng, Ming-Ming and Shou, Mike Zheng},
journal={arXiv preprint arXiv:2511.20614},
year={2025}
}
📧 Contact
If you have any comments or questions, please open a new issue or contact Ziheng Ouyang
License
Licensed under a Creative Commons Attribution-NonCommercial 4.0 International for Non-commercial use only. Any commercial use should get formal permission first.
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