Initially, guiding diffusion models required additional models alongside the diffusion model to predict specific conditional properties from the random noise at each step. However, classifier-free guidance eliminate the need for additional networks by explicitly incorporating the label (or conditioning data) into the input of the denoising model during both training and inference phases. The classifier-free guidance is adopted for Chemeleon where the text embedding vectors from Crystal CLIP are used as conditioning data, effectively guiding the text-to-crystal structure generation process. The textual descriptions of the crystal structures involve both the chemical composition and crystal system. Composition defines the element identity and ratio, fundamental factors that influence the local coordination preferences and long-range connectivity. The crystal system defines the shape (symmetry) of the unit cell, treated as a case property in this study. Three types of textual descriptions are used, as summarized in Table , each with an example. For the composition model, the textual description consists solely of the reduced composition in alphabetical order. The formatted text form represents structured data format by incorporating both the composition and an additional property, the crystal system, separated by a comma. The general text form allows for a more diverse range of textual descriptions, generated by large language models (LLMs). The details of generating textual descriptions and examples of general text are provided in the Method section and Supplementary Figure . The Validity metric measures the proportion of structurally valid samples, where validity is defined by the absence of atomic overlap and cell lengths shorter than 60 Å. Both models achieve a near-perfect validity rate of 98-99%, indicating their robustness in generating structurally feasible outputs.