[spunker540]

Does SD1.5 suffer from resolution / coherence / complexity issues?

I understand most outputs could be fine tuned for most domains, but still felt sd1.5 had a resolution ceiling, and a complexity ceiling no matter how good the fine tuning

[vunderba]

Yeah SD 1.5 is mostly trained on datasets of resolution of 512x512. That's why you'd get crazy multi-limb goro abominations if you pushed checkpoints too much higher than 768x768 without either using a Hires Fix or Img2Img.

There's not much of a reason to use SD 1.5 over SDXL if image quality is paramount.

A lot of people (myself included) use a pipeline that involves using Flux to get the basic action / image correct, then SDXL as a refiner and finally a decent NMKD-based upscaler.

[raxxorraxor]

Yes, the toolchains around it can alleviate it, but only to a degree. You more or less dependent on a fine tune specifically trained for the things you want. But if you have that, the image quality is usually far better than from any generic model in my opinion, aside from resolution.

Merging any or all concepts is mostly beyond it, but I haven't seen any model being good at it yet. There are some that are significantly better, but often come with other disadvantages.

Overall what these models can do is quite impressive. But if you want a really high quality image, finding the fitting model is as difficult as finding the right prompt. And the general models tend to always fall back to some mean AI standard image.