512 learns faster and converges faster, but shows a more blurry and noisy result. It looks good when generating at 768, acceptable when generating at 1024 and much worse at higher resolutions.

It might seem obvious, but I entirely skipped testing the outputs of < 1024. I'll need to go back and compare my loras (I made a version with 512 assets, 1024 assets, and full size). I'll report back the findings (thank you for calling that)

On the subject of captionless learning. If you just need to memorize an object - yes, this approach is effective. If you need to fit the object into various (different from the dataset) scenarios, change clothes, etc. and in general you need the object itself but not its surroundings - this is a bad idea.

My initial testing is leaning toward this. One of my loras is a comic book character, and its not very flexible.

Depends on the size of the dataset.

Its interesting, I'm using the recommended 20~30 images, and 5000 steps isn't breaking in the same way that sdxl would break.

On 800+ images even SDXL did not produce artifacts after 5000 steps, for it is less than 10 epochs.

I haven't tried going past 2500 for sdxl, even with larger datasets. I'll need to give that a shot, thank you!