See, isn’t that a more seductive title than “Personalizing Text-to-Image Generation using Textual Inversion“? 😌 But the so-titled paper seems really important in helping generative models like DALL•E to become much more precise. The team writes:

We ask: how can we use language-guided models to turn our cat into a painting, or imagine a new product based on our favorite toy? Here we present a simple approach that allows such creative freedom.

Using only 3-5 images of a user-provided concept, like an object or a style, we learn to represent it through new “words” in the embedding space of a frozen text-to-image model. These “words” can be composed into natural language sentences, guiding personalized creation in an intuitive way.

Check out the kind of thing it yields: