Image diffusion models have facilitated the generation of visually compelling images, and this powerful generative capability has also opened new avenues for tasks such as image morphing. Previous image morphing approaches using diffusion models primarily focus on interpolating text embeddings and latent vectors. However, these methods lack explicit shape control, resulting in morphing processes that lack of smooth shape transitions. Moreover, the direct interpolation of text embeddings and latent vectors without constraints pushes the process out of the domain of the diffusion model, leading to noticeable artifacts. To address these issues, we propose a novel diffusion model-based method that leverages normal maps as geometric guidance to control image morphing. By integrating 3D reconstruction techniques with variational implicit surface methods, our approach ensures smoother and more stable morphing sequences, preserving shape consistency throughout the transformation. Comparative experiments demonstrate that our method produces smooth, consistent, and stable results and outperforms existing SOTA techniques, such as IMPUS and DiffMorpher, especially when dealing with images with large shape differences.