essed into a numerical representation. This typically involves tokenizing the text, converting each token to an embedding and then feeding those embeddings through a model (typically a transformer) whose output will be used as the conditioning for the diffusion model. 
Textual inversion learns a new token embedding (v* in the diagram above). A prompt (that includes a token which will be mapped to this new embedding) is used in conjunction with 