For more inclusive models, or for current ones? In order to add something, either the size has to grow, or something would need to get pushed out (content, or quality). 4GB models are already at the limit of usefulness, both DALLE3 and SDXL run at about 12B parameters, so to make them “more inclusive” they’d have to grow.
Wait, by “fine-tuning”… do you mean LoRAs? Because those are more like brain surgery with a sledgehammer, rather the opposite of “fine”. I don’t think it’s possible for LoRAs to avoid having undesirable side effects… and I don’t think people even want that.
Actual “fine” tuning, would be adding the LoRA’s training data to the original set, then training the whole model from scratch… and that would require increasing the model’s size to encode the increased amount of data for the same output quality.
Nice read, and an interesting approach… although it kind of tries to hide the elephant in the room:
This work has the potential to shift the way that image gen-erators operate at achievable costs to ensure that several cat-egories of harm from ‘AI’ generated models are mitigated, while the generated images become much more realistic and representative of the AI-generated images that populations want around the world.
They show that the approach optimizes for less “stereotypes” and less “offensive”, which in most cultures leads from worse to better “cultural representation”… but notice how there is a split in the “Indian” culture cohort, with an equal amount finding “more stereotypical, more offensive” to be just as good at “cultural representation”:
They basically made the model more politically correct and “idealized”, but in the process removed part of a culture representation that wasn’t wrong, because the “culture” itself is split to begin with.
That’s my point. They claim to reduce misrepresentation, while at the same time they erase a bunch of correct representations.
Going back to what I was saying: fine tuning doesn’t increase diversity, it only shifts the biases. Encoding actual diversity would require increasing the model, then making sure it can output every correct representation.
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Are you ready to run a 100B FP64 parameter model? Or even a 10B FP32 one?
Over time, I wouldn’t be surprised if 500B INT8 models became commonplace with neuromorphic RAM, but there’s still some time for that to happen.
You don’t need that many parameters, 4gb checkpoints work just fine.
For more inclusive models, or for current ones? In order to add something, either the size has to grow, or something would need to get pushed out (content, or quality). 4GB models are already at the limit of usefulness, both DALLE3 and SDXL run at about 12B parameters, so to make them “more inclusive” they’d have to grow.
I’m saying SD 1.5 and SDXL capture the concepts just fine, it’s just during fine-tuning people train away some of the diversity.
Wait, by “fine-tuning”… do you mean LoRAs? Because those are more like brain surgery with a sledgehammer, rather the opposite of “fine”. I don’t think it’s possible for LoRAs to avoid having undesirable side effects… and I don’t think people even want that.
Actual “fine” tuning, would be adding the LoRA’s training data to the original set, then training the whole model from scratch… and that would require increasing the model’s size to encode the increased amount of data for the same output quality.
I mean like this. This paper just dropped the other day.
Nice read, and an interesting approach… although it kind of tries to hide the elephant in the room:
They show that the approach optimizes for less “stereotypes” and less “offensive”, which in most cultures leads from worse to better “cultural representation”… but notice how there is a split in the “Indian” culture cohort, with an equal amount finding “more stereotypical, more offensive” to be just as good at “cultural representation”:
They basically made the model more politically correct and “idealized”, but in the process removed part of a culture representation that wasn’t wrong, because the “culture” itself is split to begin with.
“Indian” is a huge population of very diverse people.
That’s my point. They claim to reduce misrepresentation, while at the same time they erase a bunch of correct representations.
Going back to what I was saying: fine tuning doesn’t increase diversity, it only shifts the biases. Encoding actual diversity would require increasing the model, then making sure it can output every correct representation.