[vessenes]

OK - basic plan here, which I feel I may have read (just called something like a concept LoRA on r/stablediffusion?):

1. Any concept you're interested in, get inputs with and without it. For images: 100 with, say a pink elephant, 100 without.

2. Calculate the difference between these models as represented by an "Optimal Transport Map".

Apply the map at desired strength, and voila - you don't have a pink elephant anymore. These can stack.

There are lots of obvious and interesting applications here in LLMs - there's some research showing that LLMs have honesty/dishonesty parameter groupings, for instance.

But, I can't really figure out what this OT map is. Is it a single layer tensor? Is it multidimensional? If it's the size of the original model (which they say it is not), then I understand how to apply it - just add weights and rerun. If it's not a copy, where and when is this map applied? Another way to say this is, how is this different than calculating the average difference and storing it in a low-rank adapter? I have no idea.