Latent diffusion models have achieved significant success in point cloud generation recently, where the diffusion process is constructed under a low-dimensional but efficient latent space. However, existing methods usually overlook the differences between consistency information and offset information in the point clouds, leading to difficulty in accurately learning both the overall shape and the offset of points on shape simultaneously. To address this issue, we propose a decomposed latent diffusion model that separately captures consistency information and offset information in the latent space with feature decoupling. To learn effective consistency information, the consistency constraint among different point clouds with a shape is imposed in the latent space. Then, based on the decomposed features, we further design a geometry diffusion model. We predict key points with consistency information to guide the diffusion model. Therefore, the diffusion model can achieve comprehensive and strong geometry feature extraction. Experiments show that our method achieved state-of-the-art generation performance on the ShapeNet dataset.