The result of
Textual Inversion: a method to define new keywords in a model ==without modifying it.==
The method has gained attention because its capable of injecting new styles or objects to a model with as few as 3 -5 sample images.
The amazing thing about textual inversion is NOT the ability to add new styles or objects — other fine-tuning methods can do that as well or better. It is the fact that it can do so ==without changing the model.==
First you define a newkeyword that’s not in the model for the new object or style. That new keyword will get tokenized (that is represented by a number) just like any other keywords in the prompt.
Each token is then converted to a unique embedding vector to be used by the model for image generation.
Textual inversion finds the embedding vector of the new keyword that best represents the new style or object, without changing any part of the model.
You can think of it as finding ==a way within the language model to describe the new concept.==
