It’s really hard getting dark skin sometimes. A lot of the time it’s not even just the model, LoRAs and Textual Inversions make the skin lighter again so you have to try even harder. It’s going to take conscious effort from people to tune models that are inclusive. With the way media is biased right now, I feel like it’s going to take a lot of effort.
Right now people seem to prefer smaller quantized models, with whatever set of even smaller LoRAs on top, that make them output what they want… and only include more generic elements in the base model.
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.
It’s really hard getting dark skin sometimes. A lot of the time it’s not even just the model, LoRAs and Textual Inversions make the skin lighter again so you have to try even harder. It’s going to take conscious effort from people to tune models that are inclusive. With the way media is biased right now, I feel like it’s going to take a lot of effort.
“Inclusive models” would need to be larger.
Right now people seem to prefer smaller quantized models, with whatever set of even smaller LoRAs on top, that make them output what they want… and only include more generic elements in the base model.
I wouldn’t mind. I’m here for it.
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.