Single domain generalized object detection aims to train an object detector on a single source domain and generalize it to any unseen domain. Although existing approaches based on data augmentation exhibit promising results, they overlook domain discrepancies across multiple augmented domains, which limits the performance of object detectors. To tackle these problems, we propose a novel diffusion-based framework, termed SDG-DiffDet, to mitigate the impact of domain gaps on object detectors. The proposed SDG-DiffDet consists of a memory-guided diffusion module and a source-guided denoising module. Specifically, in the memory-guided diffusion module, we design feature statistics memories that mine diverse style information from local parts to augment source features. The augmented features further serve as noise in the diffusion process, enabling the model to capture distribution differences between practical domain distributions. In the source-guided denoising module, we design a text-guided condition to facilitate distribution transfer from any unseen distribution to source distribution in the denoising process. By combining these two designs, our proposed SDG-DiffDet effectively models feature augmentation and target-to-source distribution transfer within a unified diffusion framework, thereby enhancing the generalization ability of object detector. Extensive experiments demonstrate that the proposed SDG-DiffDet achieves state-of-the-art performance across two challenge scenarios.