[Swizec]

My point was more that you can add these guardrails without having to keep track of what the model had previously generated.

And I think if you used a perfectly balanced dataset for training, you’d get these guardrails for free because the right probabilities would be baked into the model’s weights.

[_Nat_]

Yeah, the idea to use random-selection instead of keeping track of generation-history seems reasonable. The idea of guardrails from perfect-balancing seems less obvious to me.

For example, say someone wants to generate a "US President" -- what would the ideal range of outputs be?

The article checked for just two things: sex (male or female) and skin-tone (I, II, III, IV, V, or VI). To date, all US Presidents have been male, and they were probably mostly skin-tones I or II (not bothering to check), except for Obama who was probably.. like IV or something (still not bothering to check).

So if we run StableDiffusion for a "US President", what would a "perfectly balanced" output look like? Should there be any women? What about the skin-tone distribution?

Also, Obama was a 2-term President, so.. if his skin-tone should somehow affect the distribution, should it have a stronger effect because he was in office for longer than average? Or should all US Presidents have the same effect regardless of their time in office? And either way, why?