> If a human has to work at it for hours, it's more like using Blender than "having an AI draw it" in my mind.

A human has to work at it too; more than few hours when doing more than few quick sketches (memory has its limits; there's a reason artists keep reference drawings around), and obviously they already put years into learning their skills than before, but fair - the human artist already knows how to do things that any given model doesn't yet[0], we kind of have to assemble the overall flow ourselves for now[1].

Then again, you only need to assemble it once, putting those hours of work up front - and if it's done, and it works, it becomes fair to say that AI can, in fact, generate self-consistent comic books.

> I suspect if someone went to the trouble to implement your above solution they'd find the end result isn't as good as they'd hoped. In practice you'd probably find one or more steps don't work correctly- for example, maybe today's multimodal LLM's can't evaluate prompt adherence acceptably.

I agree. I obviously didn't try this myself either (yet, I'm very tempted to try it, to satisfy my own curiosity). However, between my own experience with LLMs and Stable Diffusion, and occasionally browsing Stable Diffusion subreddits, I'm convinced all individual steps work well (and have multiple working alternatives), except for the one you flagged, i.e. evaluating prompt adherence using multimodal LLM - that last one I only feel should work, but I don't know for sure. However, see [1] for alternative approach :).

My point thus is, all individual steps are possible, and wiring them together seems pretty straightforward, therefore the whole thing should work if someone bothers to do it.

> If the technology was ready the evidence would be pretty clear- I'd expect to see some very good, very quickly made comic books shown off by AI enthusiast on reddit rather then the clearly limited/ not very good comic book experiments which have been demonstrated so far.

I think the biggest concentration of enthusiasm is to be found in NSWF uses of SD :). On the one hand, you're right; we probably should've seen it done already. On the other hand, my impression is that most people doing advanced SD magic are perfectly satisfied with partially manual workflows. And it kind of makes sense - manual steps allow for flexibility and experimentation, and some things are much simpler to wire by hand or patch up with some tactical photoshopping, than to try and automate them fully. In particular, things judging the quality of output is both easy for humans and hard to automate.

Still, I've recently seen ads of various AI apps claiming to do complex work (such as animating characters in photos) end-to-end automatically - exactly the kind of work that's typically done in partially manual process. So I suspect fully-automated solutions are being built on a case-by-case basis, driven by businesses making apps for the general population; a process that lags some months behind what image gen communities figure out in the open.

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[0] - Though arguably, LLMs contain the procedural knowledge of how a task should be done; just ask it to ELI5 or explain in WikiHow style.

[1] - In fact, I just asked Claude to solve this problem in detail, without giving it my own solution to look at (but hinting at the required complexity level); see this: https://cloud.typingmind.com/share/db36fc29-6229-4127-8336-b... (and excuse the weird errors; Claude is overloaded at the moment, so some responses had to be regenerated; also styling on the shared conversation sucks, so be sure to use the "pop out" button on diagrams to see them in detail).

At very high level, it's the same as mine, but one level below, it uses different tools and approaches, some of which I never knew about - like keeping memory in embedding space instead of text space, and using various other models I didn't know exist.

EDIT: I did some quick web search for some of the ideas Claude proposed, and discovered even more techniques and models I never heard of. Even my own awareness of the image generation space is only scratching the surface of what people are doing.