Joint Multimodal Entity-Relation Extraction (JMERE) aims to extract entity-relationship triples in texts from given image-text pairs. As a joint multimodal information extraction task, it has attracted increasing research interest. Previous works of JMERE typically utilize graph networks to align textual entities and visual objects and achieve promising performance. However, these methods do not pay attention to the inconsistency between text and image and the straight alignment could limit the performance of JMERE models. In this paper, we propose a Consistency-adaptive text-image Alignment Generation (CAG) framework for various text-image consistency scenarios. Specifically, we propose a Consistency Factor (CF) to measure the consistency between images and texts. We also design consistency-adaptive contrastive learning based on CF, which can reduce the impact of inconsistent visual and textual information. Additionally, we adopt JMERE-specifical instruction tuning for better entity-relationship triplet generation. Experimental results on the JMERE dataset demonstrate that our proposed CAG is effective and achieves state-of-the-art performance.