Comments (4)
It seems that it's due to some bugs in v24.1.2~4
since I can repeat this problem in wsl ubuntu2204
and windows11
during v24.1.2~4
when training standard sdxl lora
. (It seems that it also exists in v1.0.x)
Now I can simply prevent such a problem by using v23.1.x
in wsl ubuntu2204
.
But... it's still hard for me to find out where the problem is, that is the code that cause the problem. Loaind images & texts or Sampling?
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The example prompt in your first post has a negative prompt (the long text after --n
), but where is the positive prompt? Not sure if that's related to the issue, but it's the first thing that comes to mind.
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Oh, it's not a big deal. The positive prompt is a description of my target character.
The whole prompt is like this 1girl, _char_name_, _char_feature_, detached collar,blue vest,white shirt,skirt, white thighhighs, solo --w 1024 --h 1024 --l 7.0 --s 24 --n lowres,normal quality, low quality,worst quality,extra digits,fewer digits,wrong hand,bad hands, poorly drawn hands, bad_perspective,error, jpeg artifacts,ugly, duplicate, morbid, mutilated,wrong feet, bad feet,mutated hands,poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs,missing arms, missing legs,fused fingers, long neck, username, watermark,censored,beginner,bad_art,bad arm, bad leg, bad reflection,unfinished, unfinished background,
.
I have tested prompts like this in various devices with ubuntu and kohya_ss v22.4
or simply lora-scripts
. The sample result is correct as expected.
from kohya_ss.
I did a quick test with your sample prompt and my samples look okay. Trained a lora on sdxl-base-1.0, kohya_ss v24.1.4, Ubuntu-22.04 on WSL2/Windows 10 21H2.
What happens if you shorten your negative prompt, or avoided using it at all?
And what are your WSL version numbers from wsl --version
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Related Issues (20)
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- How to train LORA with textEncoder 1 only. I get sdxl_train_network.py: error: unrecognized arguments: --train_text_encoder --learning_rate_te2=0" HOT 1
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