4 ms·

I don’t think Stable Diffusion is made to produce statistically accurate depictions of groups. Asking for a judge 300 times won’t give you 34% female judge imag

I don’t think Stable Diffusion is made to produce statistically accurate depictions of groups. Asking for a judge 300 times won’t give you 34% female judge images, it will give you individual photos of the demographic most likely to be a judge, which is white male. I do agree this statistically results in exaggerated stereotypes though, I just don’t know how you would get generative images to produce while remembering what they’ve produced in the past and making it representative of the whole population of inputted images.

Swizec 3y ago
I don’t think you need to remember what you’ve produced in the past to create a representative sample. There’s no global counter of male/female babies making sure we have a balanced population either. Wouldn’t something like isMale*P(male=.66) work fine?
- SamoyedFurFluff 3y ago
  I guess my curiosity is, what happens if you asked 300 individual artists(representative of the population) to produce a judge?
- rando_dfad 3y ago
  I wonder where you would put that in the prompt? I wonder how you would generalize this? The issue is the command "draw a judge" shows a white guy 100% of the time. if you automate prompt generation to be "draw a {gender} judge", with as you suggest the term gender has 66% chance of being "male", sure. But how do you extend that automated prompt generation to include "draw a {skin color} judge" or "draw a {gender}, {skin color} judge". or to occasionally generate a character from the Book of Judges. Or Judge Dread.
- _Nat_ 3y ago
  > Wouldn’t something like isMale*P(male=.66) work fine? It doesn't think like that. If it did, they could've just done `P(hasFiveFingersPerHand)=0.99999`. But it doesn't even necessarily draw what you ask it to. Instead, it generally adopts a set of de-noising transforms that it's been trained to believe would tend to lead to what the prompt sounds like.. then whatever those transforms produce would, hopefully, be sorta like what was requested.
  - Der_Einzige 3y ago
    Custom loss functions absolutely work and work basically the way described above. https://colab.research.google.com/drive/1dlgggNa5Mz8sEAGU0wFCHhGLFooW_pf1?usp=sharing 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_ 3y ago
      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 3y ago
        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_ 3y ago
        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?
deleted 3y ago
[deleted]

Swizec 3y ago

I don’t think you need to remember what you’ve produced in the past to create a representative sample. There’s no global counter of male/female babies making sure we have a balanced population either. Wouldn’t something like isMale*P(male=.66) work fine?

SamoyedFurFluff 3y ago
I guess my curiosity is, what happens if you asked 300 individual artists(representative of the population) to produce a judge?
rando_dfad 3y ago
I wonder where you would put that in the prompt? I wonder how you would generalize this? The issue is the command "draw a judge" shows a white guy 100% of the time. if you automate prompt generation to be "draw a {gender} judge", with as you suggest the term gender has 66% chance of being "male", sure. But how do you extend that automated prompt generation to include "draw a {skin color} judge" or "draw a {gender}, {skin color} judge". or to occasionally generate a character from the Book of Judges. Or Judge Dread.
_Nat_ 3y ago
> Wouldn’t something like isMale*P(male=.66) work fine? It doesn't think like that. If it did, they could've just done `P(hasFiveFingersPerHand)=0.99999`. But it doesn't even necessarily draw what you ask it to. Instead, it generally adopts a set of de-noising transforms that it's been trained to believe would tend to lead to what the prompt sounds like.. then whatever those transforms produce would, hopefully, be sorta like what was requested.
- Der_Einzige 3y ago
  Custom loss functions absolutely work and work basically the way described above. https://colab.research.google.com/drive/1dlgggNa5Mz8sEAGU0wFCHhGLFooW_pf1?usp=sharing 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_ 3y ago
    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 3y ago
      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_ 3y ago
        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?

deleted 3y ago

[deleted]