- Dec 31, 2016
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- 708
Those learning rates seem insane. General advice seems to be to have a pretty low rate to (presumably) hit the vector weights you want and to maintain flexibility. LR like those might work very well for things you want want to keep very "fixed", ie like a style you'd not want to make changes to or a very distinct subject, like just the face of a person with fix expression/features.Jimwalrus i have done some test, and for what i see, 30000 step tend to do chromatic aberration at mid cfg.
so i decide to try different train, and i finish to get my best at 3000 step.
I share setting, maybe if you want to try.
15-20 images good quality, close-up and full body (i use 768x768)
Create embedding:
Number of vectors per token : number of image/2.2 (rounded at high value)
Train setting:
Gradient Clipping :norm
Batch size : 1 or 2 (depends on vram)
Gradient accumulation steps : 1
Embedding Learning rate : 0.005:100, 3.09:500, 1.8:700, 2.06:900, 3.269:1000, 1.05:1500, 0.06:2200, 0.9
Max steps : 3000
With these setting i get good result in about 30 mins, and usable TI from low to high cfg.
I made different ti for testing with same setting on different model, for what i see do a train with f222 model get better result for realistic.
If you'd use some of those LR just for a single Epoch you'd have your subject learned, so i'd imagen using it for 3000 steps on 15-20 images you'd beaten that data in really hard. So you'd probably have a good likeness to your dataset but would need very high weight modifiers to your prompts to change this, if you could at all.
(sorry about dragging up a 1,5month old post but reading through all pages takes a while, even just skimming through some of it)