[shostack]

Is there anything akin to creating Stable Diffusion embeddings where it can train a very discrete concept that takes up a few kilobytes and use that with the base model?

Such an approach could in theory make it so you spend a little upfront to train more complex (read: concepts costing many tokens) and can subsequently reuse it cheaply because you're using an embedding of the vectors for that complex concept instead which may only take a single token.