So you're saying a word is worth 1000 pictures? How did we get this so wrong?

In a sense, language is a clumsy facsimile of the concepts we mean to express, in that we search for words to express the ideas in our minds rather than the other way around. By contrast, an image represents precisely the concept it depicts, by definition.

We forgot this about language by about the time of the Enlightenment era, when the intellectuals of the time thought that forcing everything to inhabit the structures of language (i.e., "rationality") represented the highest moral good one could achieve.

Non-verbal experiences are easier to communicate non-verbally. (Breaking news!)

But, I'd like to see someone try to communicate the Bhagavad Gita or the history of F=ma in visuals using the same amount of bits as text.

They're categorically different media, it's not just a matter of quantity. You could sum up 1000 pictures of bananas with the word "bananas", or you could spend 1000 different words describing nuances and context in just one of those pictures. Something is lost (and something is gained) either way.

Possibly it is because with things like Stable diffusion we give it a lot of passes when things don't exactly right. Images just have to be close enough.

Text however if it is only a single word out, the whole meaning and readability can change. It needs a significantly larger data set to ensure clearer readability.

Just a shot from the hip response on this one.

LLM text is token based not pixel based. And LLM output is ~1K tokens, while a picture is ~1M pixels.

And pictures aren't necessarily made of pixels. They are modelled as a collection waves (JPEG), and displayed as pixels. I don't know how LLM/whatever image models represent images, though.

Images are more global: changing the color pallette changes the tone (ha!) like a descriptive word in text.