DiffSplat is an innovative 3D generation technology that quickly creates 3D Gaussian point clouds from text prompts and single-view images. This technology leverages a large-scale pre-trained text-to-image diffusion model to efficiently generate 3D content. It addresses the limitations of traditional 3D generation methods concerning dataset size and the ineffective use of 2D pre-trained models, while maintaining 3D consistency. Key advantages of DiffSplat include efficient generation speeds (completed in 1 to 2 seconds), high-quality 3D output, and support for various input conditions. The model has broad prospects in academic research and industrial applications, particularly in scenarios requiring the rapid generation of high-quality 3D models.