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# This is an example extension for custom training. It is great for experimenting with new ideas.
from toolkit.extension import Extension
# This is for generic training (LoRA, Dreambooth, FineTuning)
class SDTrainerExtension(Extension):
# uid must be unique, it is how the extension is identified
uid = "sd_trainer"
# name is the name of the extension for printing
name = "SD Trainer"
# This is where your process class is loaded
# keep your imports in here so they don't slow down the rest of the program
@classmethod
def get_process(cls):
# import your process class here so it is only loaded when needed and return it
from .SDTrainer import SDTrainer
return SDTrainer
# This is for generic training (LoRA, Dreambooth, FineTuning)
class UITrainerExtension(Extension):
# uid must be unique, it is how the extension is identified
uid = "ui_trainer"
# name is the name of the extension for printing
name = "UI Trainer"
# This is where your process class is loaded
# keep your imports in here so they don't slow down the rest of the program
@classmethod
def get_process(cls):
# import your process class here so it is only loaded when needed and return it
from .UITrainer import UITrainer
return UITrainer
# for backwards compatability
class TextualInversionTrainer(SDTrainerExtension):
uid = "textual_inversion_trainer"
AI_TOOLKIT_EXTENSIONS = [
# you can put a list of extensions here
SDTrainerExtension, TextualInversionTrainer, UITrainerExtension
]
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