MIGC++ is an advanced multi-instance generation controller for image synthesis, designed to address challenges in generating multiple instances within a single image with precise control over position, attributes, and quantity. The MIG task requires models to generate each instance according to specific attributes and positions detailed in instance descriptions while maintaining consistency with the global image description. MIGC, a novel approach, divides the task into separate single-instance subtasks with singular attributes, reducing attribute leakage and improving multi-instance shading. MIGC++ extends this by allowing attribute descriptions through text and images, and position control through boxes and masks, enhancing detailed shading with a refined shader. The Consistent-MIG algorithm ensures consistency in unmodified regions and preserves instance identity during modifications. The COCO-MIG and Multimodal-MIG benchmarks evaluate these methods, demonstrating that MIGC and MIGC++ outperform existing techniques in precision and control. Experiments on these benchmarks, along with COCO-Position and DrawBench, show significant improvements in instance success ratio, mean intersection over union, and attribute control accuracy. MIGC++ achieves 98.5% attribute control accuracy on DrawBench and outperforms other models in multimodal alignment. The methods are implemented with a divide-and-conquer strategy, enhanced attention mechanisms, and a shading aggregation controller, enabling precise and consistent multi-instance generation.MIGC++ is an advanced multi-instance generation controller for image synthesis, designed to address challenges in generating multiple instances within a single image with precise control over position, attributes, and quantity. The MIG task requires models to generate each instance according to specific attributes and positions detailed in instance descriptions while maintaining consistency with the global image description. MIGC, a novel approach, divides the task into separate single-instance subtasks with singular attributes, reducing attribute leakage and improving multi-instance shading. MIGC++ extends this by allowing attribute descriptions through text and images, and position control through boxes and masks, enhancing detailed shading with a refined shader. The Consistent-MIG algorithm ensures consistency in unmodified regions and preserves instance identity during modifications. The COCO-MIG and Multimodal-MIG benchmarks evaluate these methods, demonstrating that MIGC and MIGC++ outperform existing techniques in precision and control. Experiments on these benchmarks, along with COCO-Position and DrawBench, show significant improvements in instance success ratio, mean intersection over union, and attribute control accuracy. MIGC++ achieves 98.5% attribute control accuracy on DrawBench and outperforms other models in multimodal alignment. The methods are implemented with a divide-and-conquer strategy, enhanced attention mechanisms, and a shading aggregation controller, enabling precise and consistent multi-instance generation.