Abstract
With recent advancements in text-to-image (T2I) models, effectively generating multiple instances within a single image prompt has become a crucial challenge. Existing methods, while successful in generating positions of individual instances, often struggle to account for relationship discrepancy and multiple attributes leakage. To address these limitations, this paper proposes the relation-aware disentangled learning (RaDL) framework. RaDL enhances instance-specific attributes through learnable parameters and generates relation-aware image features via Relation Attention, utilizing action verbs extracted from the global prompt. Through extensive evaluations on benchmarks such as COCO-Position, COCO-MIG, and DrawBench, we demonstrate that RaDL outperforms existing methods, showing significant improvements in positional accuracy, multiple attributes consideration, and the relationships between instances. Our results present RaDL as the solution for generating images that consider both the relationships and multiple attributes of each instance within the multi-instance image.
Abstract (translated)
随着文本到图像（T2I）模型的近期进展，有效地在一个图片提示中生成多个实例已经成为一个重要的挑战。现有的方法虽然在生成单个实例的位置方面取得了成功，但在处理关系差异和多重属性泄露时往往力不从心。为了应对这些限制，本文提出了一种基于关系感知解耦学习（RaDL）框架。通过可学习的参数，RaDL增强了每个实例特有的属性，并通过使用从全局提示中提取的动作动词来生成具有关系意识的图像特征。利用Relation Attention机制，该方法能够更好地理解和处理实例之间的关系以及多重属性。 在COCO-Position、COCO-MIG和DrawBench等基准测试上的广泛评估表明，RaDL优于现有的方法，在位置准确性、考虑多重属性及实例间的关系方面均有显著改进。我们的结果将RaDL视为生成同时考虑每个实例之间关系及其多重属性的多实例图像的最佳解决方案。
URL
https://arxiv.org/abs/2507.11947
