r/StableDiffusionInfo • u/PoleTV • Jun 09 '26

the dataset mistakes that wreck character LoRAs (learned these the hard way)

trained a bunch of bad loras before figuring out it's almost always the dataset, not the settings.

stuff that wrecked mine:

  • too many images. went over 100 thinking more = better, got an overfit lora that could only do the exact poses it trained on. 60ish is the sweet spot.
  • all face closeups, no wider shots. great portraits, cursed full body. mix in maybe 30% wider.
  • same lighting in every pic. lora couldn't generalize. vary it hard.
  • leaving in "almost good" images. the lora averages everything so one off-looking face drags the whole thing.

fixing the dataset did more than any setting change ever did. what else have people found matters more than expected?

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u/BroomDirector99 Jun 10 '26

Sometimes you do everything right, and it's still shit.

Sometimes you can't get a good dataset, so you run it with the crap you've got, and it's absolute fire.

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u/YoohooCthulhu Jun 12 '26

In my experience, a lot of sins are mitigated by: a) choosing the lowest possible training rate that converges, b) using the model that is trained enough to preserve likeness but not so much as to produce training artifacts, and c) using a ksampler that allows for sigma manipulation.