一致性批判者：通过参考引导的注意力对齐来纠正生成图像中的不一致性 / The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment
1️⃣ 一句话总结
这篇论文提出了一个名为ImageCritic的后编辑方法，它通过分析模型的注意力机制并利用参考图像来检测和修正AI生成图像中的细节不一致问题，从而显著提升图像生成的质量和一致性。
🤖 系统
Abstract - The Consistency Critic: Correcting Inconsistencies in Generated Images via Reference-Guided Attentive Alignment
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.