Traditional approaches, such as high-throughput screening and first-principles calculations have been pivotal in Crystal Structure Prediction (CSP) but are computationally expensive and time-consuming, limiting their scalability across vast chemical spaces . Generative approaches leveraging variational autoencoders (VAEs) and generative adversarial networks (GANs) have expedited the discovery of stable crystal structures. However, these models often struggle to accurately represent discrete atom types and their continuous 3D positions while incorporating crystal symmetries. Diffusion-based models have attempted to address these limitations by introducing symmetry-aware diffusion processes or integrating constraints such as periodicity, translation, and rotation through equivariant denoising mechanisms . While these models effectively generate stable structures with symmetry constraints, they exhibit limited flexibility for user interaction. Their reliance on predefined numerical inputs necessitates additional modules to translate textual information into a format usable by the model. This complexity hinders their adaptability to dynamic, interactive inputs such as natural language prompts without significant engineering modifications.