The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment

Ouyang Ziheng, Yiren Song, Yaoli Liu, Shihao Zhu, Qibin Hou, Ming-Ming Cheng, Mike Zheng Shou

The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment: 21 upvotes on Hugging Face Daily Papers, #9 of 47 papers on 2025-12-02. Day-by-day upvote history. It lost 17 votes when the Hub removed votes in bulk.

Previous works have explored various customized generation tasks given a reference image, but they still face limitations in generating consistent fine-grained details. In this paper, our aim is to solve the inconsistency problem of generated images by applying a reference-guided post-editing approach and present our ImageCritic. We first construct a dataset of reference-degraded-target triplets obtained via VLM-based selection and explicit degradation, which effectively simulates the common inaccuracies or inconsistencies observed in existing generation models. Furthermore, building on a thorough examination of the model's attention mechanisms and intrinsic representations, we accordingly devise an attention alignment loss and a detail encoder to precisely rectify inconsistencies. ImageCritic can be integrated into an agent framework to automatically detect inconsistencies and correct them with multi-round and local editing in complex scenarios. Extensive experiments demonstrate that ImageCritic can effectively resolve detail-related issues in various customized generation scenarios, providing significant improvements over existing methods.

Paper page on Hugging Face · arXiv

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