Of course, it's a tiny specialized model vs a big generalist model. They're absolutely incomparable in size/quality though, especially of the text part. How muc

Of course, it's a tiny specialized model vs a big generalist model. They're absolutely incomparable in size/quality though, especially of the text part. How much of this happens because of the poor encoder and worse training in other models, and how much due to the architectural differences? I'm not saying it's not better than the existing image gen models somehow, but it's pretty hard to separate the two since both are present. All current SotA LLMs including 4o itself have negation inaccuracy in text (you need a really complex prompt, or a long one with thousands of tokens, not a toy one), and I don't see why this one should behave differently in similar conditions. Especially considering that it also suffers from pretty much the same artifacts as other image models, just much less (fingers, extra limbs, perspective/lighting issues, overfitting, struggles with out-of-distribution generation etc.)