[Der_Einzige]

Custom loss functions absolutely work and work basically the way described above.

https://colab.research.google.com/drive/1dlgggNa5Mz8sEAGU0wF...

You can see them define a custom color loss and apply it simultaneously with the regular diffusion loss. I've actually expanded this notebook to allow regional specification of the custom loss.

It's quite difficult to define a function that detects if an individual has 5 fingers or not. That's the real issue.

[_Nat_]

The comment I'd responded to seemed to have thought that StableDiffusion picked what the sex of a person would be according to some internal odds that could be modified.

My point was that it doesn't actually think like that. For example, prompting StableDiffusion for a picture of a doctor doesn't necessarily get it to draw a human at all, much less a doctor of a pre-determined sex; instead, StableDiffusion de-noises the image until the result emerges, where that result would (ideally) contain a doctor of whatever sex it happened to come up with.

That said, you're right that we can add more code to try to guide things.

We could even just brute-force it by just re-generating images over-and-over, or tweaking them after generation, until they match exactly what we wanted. (Realistically, something like branch-and-bound would probably be preferred to blindly guess-and-check-ing.)

[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?