When training a diffusion model via the dreambooth approach, you get a new model, that has learned to associate the concept you where trying to teach it, with a previously chosen unique identifier (a vector that represents the concept inside of a sentence). The textual inversion technique takes the opposite approach. It returns a unique identifier, that when fed to the starting model produces pictures of the newly learned concept. In other words, it looks for vectors that correspond to the concept we try to teach it. Dreambooth usually delivers better results, but generates large models, that are difficult to share and manage. Textual inversion generates a lightweight textual embedding, which is easy to share, but may result in slightly worse images.
spam fails_task lang_mismatch pii not_appropriate hate_speech sexual_content quality toxicity humor helpfulness creativity violence