Latent Diffusion Models (LDMs) are powerful and potential tools for facilitating generation-based methods for domain generalization. However, existing diffusion-based DG methods are restricted to offline augmentation using LDM and suffer from degraded performance and prohibitive computational costs. To address these challenges, we propose DomainFusion to simultaneously achieve knowledge extraction in the latent space and augmentation in the pixel space of the Latent Diffusion Model (LDM) for efficiently and sufficiently exploiting LDM. We develop a Latent Distillation module that distills gradient priors from LDM to guide the optimization of DG models. Moreover, we design an online lightweight augmentation method by decomposing candidate images into styles and contents for using LDM in a fast and online fashion. Experimental results demonstrate that DomainFusion outperforms diffusion-based methods by a large margin and achieves SOTA performance on existing DG benchmark datasets. Remarkably, DomainFusion can significantly reduce the number of generated images (e.g. by more than 97% on DomainNet) without finetuning LDM.