Diffusion-based 3D generative models have seen significant progress recently. However, their further advancement is limited by issues like mode collapse and slow generation speed. In this paper, we present a coarse-to-fine 3D Gaussian generation method named MagicGS, which is capable of efficient and high-quality 3D generation from a single image. Our key contribution is to introduce the Combine-SDS strategy by leveraging both 2D diffusion and 3D diffusion priors, which can improve the optimization process and alleviate oversaturation effects. In the first stage, we optimize the 3D Gaussian with Combine-SDS to obtain rough shapes. In the second stage, we extract the mesh as a 3D representation and optimize it to generate high-quality, textured meshes. Through extensive experimentation, we demonstrate our superior performance in terms of both mesh quality and runtime compared to existing methods. Additionally, our method exhibits versatility by supporting multi-modal 3D generation tasks through integration with conditional diffusion models.