I tried again with a recent curated post, just putting in the title. (historically, practice I usually start with the post title and see if that outputs anything interesting, and then started exploring other ideas based on my understanding of the post and what visuals I thought would be good. But, this is pretty time consuming, so seeing what the "one shot" result is is useful).

Here's "Liability regimes for AI, watercolor painting", from the latest Midjourney (v6)

for contrast, here's what Midjourney 4 resulted in:

(I kinda like the Lady Justice optin)

Midjourney v3

v2

Midjourney v3 feels like hit some kind of sweet spot of "relatively high quality" but also "a bit more weird/quirky/dreamlike."

I went and tried to do Midjourney v3, this time putting in more "quality" words since it isn't automatically trained on aesthetics as hard:

"Liability regimes for AI", beautiful aquarelle painting by Thomas Schaller, high res:

okay now I guess I'll just wander back up the chain of versions, this time using my Metis on what kinds of prompts will get better results rather than seeing how it handles the dumbest case:

"Liability regimes for AI", beautiful aquarelle painting by Thomas Schaller, high res –– Midjourney V4

"Liability regimes for AI", beautiful aquarelle painting by Thomas Schaller, high res --MJ version 6

Oh, so current Midjourney is actually far better than Dall-E 3 in terms of aesthetics. One thing I still liked about the old Dall-E 2.5 was its relatively strong tendency towards photorealism. Because arguably "a cat reading a book" describes an image of an actual cat reading a book, rather than an image of an illustration of a cat reading a book, or some mixture of photo and Pixar caricature, as in the case of Dall-E 3. Though this could probably be adjusted with adding "a photo of".

I guess the bad aesthetics are to some extent a side effect of some training/fine-tuning step that improves some other metric (like prompt following), and they don't have a person who knows/cares about art enough to block "improvements" with such side effects.

Also probably a lot of it is just mode collapse from simple preference learning optimization. Each of your comparisons shows a daring, risky choice which a rater might not prefer, vs a very bland, neutral, obvious, colorful output. A lot of the image generations gains are illusory, and caused simply by a mode-collapse down onto a few well-rated points:

Our experiments suggest that realism and consistency can both be improved simultaneously; however, there exists a clear tradeoff between realism/consistency and diversity. By looking at Pareto optimal points, we note that earlier models are better at representation diversity and worse in consistency/realism, and more recent models excel in consistency/realism while decreasing the representation diversity.

Same problem as tuning LLMs. It's a sugar-rush, like spending Mickey Mouse bucks at Disney World: it gives you the illusion of progress and feels like it's free, but in reality you've paid for every 'gain'.

We've talked (a little) about integrating Flux more into LW, to make it easier to make good images. (maybe with a soft-nudge towards using "LessWrong watercolor style" by default if you don't specify something else),

Although something habryka brought up is a lot of people's images seem to be coming from substack, which has it's own (bad) version of it.