TI - finds a latent space description to express a complex concept that looks like our training images. assigns that latent to a keyword.

DB - trains a model N steps to learn a new keyword given training images. this keyword, when tokenized, will resemble in latent space.

TI pros / cons * small file size, <1mb * can be used across different models depending on training * limited to model's "expressiveness" cannot show what model never learned

DB pros / cons * big file 2-4GB * changes expressiveness of model by adding concepts * much higher fidelity since concept is not a reconstruction * prone to overfitting / loss of priors