<p>Underwater object detection plays a crucial role in applications such as marine ecological monitoring and underwater rescue operations. However, challenges such as limited underwater data availability and low scene diversity hinder detection accuracy. In this paper, we propose the Underwater Layout-Guided Diffusion Framework (ULGF), a diffusion model-based framework designed to augment underwater detection datasets. Unlike conventional methods that generate underwater images by integrating in-air information, ULGF operates exclusively on a small set of underwater images and their corresponding labels, requiring no external data. We have publicly released the ULGF source code and the generated dataset for further research. Our approach enables the generation of high-fidelity, diverse, and theoretically infinite underwater images, substantially enhancing object detection performance in real-world underwater scenarios. Furthermore, we evaluate the quality of the generated underwater images, demonstrating that ULGF produces images with a smaller domain gap.</p>