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. Project page: https://github.com/limuloo/MIGC.

翻译：我们提出了多实例生成（MIG）任务，该任务专注于在单张图像中生成多个实例，每个实例都严格按照用户规范，精确放置在预定义位置，并具有类别、颜色和形状等属性。MIG面临三个主要挑战：避免实例间的属性泄漏、支持多样化的实例描述，以及在迭代生成中保持一致性。为解决属性泄漏问题，我们提出了多实例生成控制器（MIGC）。MIGC通过分治策略生成多个实例，将具有多重属性的多实例着色任务分解为具有单一属性的单实例任务，随后再进行整合。为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，并通过边界框和掩码进行位置控制。最后，我们提出了Consistent-MIG算法，以增强MIGC和MIGC++的迭代MIG能力。该算法确保在添加、删除或修改实例时，未修改区域的一致性，并在实例属性改变时保持其身份不变。我们引入了COCO-MIG和Multimodal-MIG基准来评估这些方法。在这些基准以及COCO-Position基准和DrawBench上进行的大量实验表明，我们的方法显著优于现有技术，并在位置、属性和数量等方面保持了精确的控制。项目页面：https://github.com/limuloo/MIGC。