In this paper, we propose a generation-detection cycle consistent (GDCC) learning framework that jointly optimizes both layout-to-image (L2I) generation and object detection (OD) tasks in an end-to-end manner. The key of GDCC lies in the inherent duality between the two tasks, where L2I takes all object boxes and labels as input conditions to generate images, and OD maps images back to these layout conditions. Specifically, in GDCC, L2I generation is guided by a layout translation cycle loss, ensuring that the layouts used to generate images align with those predicted from the synthesized images. Similarly, OD benefits from an image translation cycle loss, which enforces consistency between the synthesized images fed into the detector and those generated from predicted layouts. While current L2I and OD tasks benefit from large-scale annotated layout-image pairs, our GDCC enables more efficient use of annotation-free synthetic data, thereby further enhancing data efficiency. It is worth noting that our GDCC framework is computationally efficient thanks to the perturbative single-step sampling strategy and a priority timestep re-sampling strategy during training. Besides, GDCC preserves the architectures of L2I, OD models, and the generation pipeline within the framework, thus maintaining the original inference speed. Extensive experiments demonstrate that GDCC significantly improves the controllability of diffusion models and the accuracy of object detectors.