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LORA Training - Over- and Under- Training

LORA Training - Over- and Under- Training

hey there,

so if you read my training guide and visit some Youtube videos, you still don't really know what is

"Over- and Under- Training"

to get an idea, it is important to generate samples during training.

the samples should start with a prompt that you should choose.

may "a young woman look at me"

so in the begining the training generates samples of a woman that look at you.

from about 30% of the total training, there should slowly be some similarity to the training images.

of about 70%, there should be some samples that are really very similar.

when at the end of training, the object of interest looks cut out, then overtraining begins.

the goal of a good model is to determine the learning state before overtraining begins, so you can play with it in a large range

CU and have fun

start -> 30% ->  70% -> 95%

you also see it wile the learning, say the loss is arround 0.1 it should be stay around 0.1 is the loss dropping down 0.9 -> 0.8 -> 0.7 its over fitting!

in addition for SDXL training, the image get more and more dark points ore lines so its over trained ...

more helpful videos

https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/issues/124

very good actual video !

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