MIGC++: Advanced Multi-Instance Generation Controller for Image Synthesis

Dewei Zhou;You Li;Fan Ma;Zongxin Yang;Yi Yang
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Abstract

We introduce the Multi-Instance Generation (MIG) task, which focuses on generating multiple instances within a single image, each accurately placed at predefined positions with attributes such as category, color, and shape, strictly following user specifications. MIG faces three main challenges: avoiding attribute leakage between instances, supporting diverse instance descriptions, and maintaining consistency in iterative generation. To address attribute leakage, we propose the Multi-Instance Generation Controller (MIGC). MIGC generates multiple instances through a divide-and-conquer strategy, breaking down multi-instance shading into single-instance tasks with singular attributes, later integrated. To provide more types of instance descriptions, we developed MIGC++. MIGC++ allows attribute control through text & images and position control through boxes & masks. Lastly, we introduced the Consistent-MIG algorithm to enhance the iterative MIG ability of MIGC and MIGC++. This algorithm ensures consistency in unmodified regions during the addition, deletion, or modification of instances, and preserves the identity of instances when their attributes are changed. We introduce the COCO-MIG and Multimodal-MIG benchmarks to evaluate these methods. Extensive experiments on these benchmarks, along with the COCO-Position benchmark and DrawBench, demonstrate that our methods substantially outperform existing techniques, maintaining precise control over aspects including position, attribute, and quantity.
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图像合成的高级多实例生成控制器
我们介绍了多实例生成(Multi-Instance Generation, MIG)任务,其重点是在单个图像中生成多个实例,每个实例都精确地放置在预定义的位置,具有类别、颜色和形状等属性,严格遵循用户规范。MIG面临三个主要挑战:避免实例之间的属性泄漏,支持不同的实例描述,以及在迭代生成中保持一致性。为了解决属性泄漏问题,我们提出了多实例生成控制器(MIGC)。MIGC通过分而治之的策略生成多个实例,将多实例着色分解为具有单一属性的单实例任务,稍后进行集成。为了提供更多类型的实例描述,我们开发了migc++。migc++允许通过文本和图像进行属性控制,通过框和蒙版进行位置控制。最后,我们引入了Consistent-MIG算法来增强MIGC和migc++的迭代MIG能力。该算法在增加、删除或修改实例时保证了未修改区域的一致性,并在实例属性发生变化时保持了实例的身份。我们介绍了COCO-MIG和Multimodal-MIG基准来评估这些方法。在这些基准测试上进行的大量实验,以及COCO-Position基准测试和DrawBench,表明我们的方法实质上优于现有技术,保持了对位置、属性和数量等方面的精确控制。
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