Molecular geometry optimization, particularly in the context of transition state generation, poses significant computational challenges that hinder its use within large-scale reaction workflows. Traditional methods rely on resource-intensive quantum mechanical approaches like density functional theory, demanding both computational resources and substantial prior reaction knowledge. Recent advancements in deep learning-based diffusion models have shown promise in predicting reaction transition state conformations. Current models rely on extensive architectures that capture a reaction's geometry and ensemble models. This work proposes an equivariant diffusion model, designed to address computational expenses and complex architectures. Our model demonstrates robust generalizability and efficiency in predicting transition state conformations, making it a valuable tool for a broader range of chemical reactions. Our approach is a step towards eliminating the computational barriers associated with classic transition state generation techniques, providing chemists with a powerful tool to rapidly propose transition state structures. Code and data can be found on https://figshare.com/s/cb10fda0c88f18d00baf.