[Der_Einzige]

I am not wrong, you are wrong. The fact is that NLP and other fields are FULL of people using automated benchmarks to claim that they are "state of the art". They are incentivized to downplay or trivialize any quality losses. Scores like ROUGE and BLEU are terrible and the whole community knows it, but they're still used because we have nothing "better".

I can actually see jpg artifacts on the jpg variants of the png files that I generate in Stable Diffusion, and the impacts from quantization down to 3,2, even 1 bit are FAR more than the impacts of switching from png to jpg.

Also, I actually have published peer reviewed research on LLMs and spend a majority of my time on this earth thinking about and coding for them. I know what I'm talking about and you shouldn't try to dismiss my criticisms so quickly.

Even the coomers at civitai have done polls where their own users find dreambooth models better than lora models on average, likely because the likeness of a person can be more properly trained when heavier/stronger methods are utilized. Same dynamic here with quantization.

Yes, as a model scales up in size quantization hurts it less. The claims made that extreme quantization is not noticable at all when the model is super large is just pathetically wrong.