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test.py
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from PySide6 import QtWidgets
import sys
# from main_ui_files.MainUI import MainWidget
# from main_ui_files.QueueUI import QueueWidget
# from main_ui_files.ArgsListUI import ArgsWidget
# from main_ui_files.SubsetListUI import SubsetListWidget
# from main_ui_files.NetworkUI import NetworkWidget
# from main_ui_files.GeneralUI import GeneralWidget
# from main_ui_files.OptimizerUI import OptimizerWidget
# from main_ui_files.SavingUI import SavingWidget
# from main_ui_files.SampleUI import SampleWidget
# from main_ui_files.NoiseOffsetUI import NoiseOffsetWidget
# from main_ui_files.LoggingUI import LoggingWidget
# from main_ui_files.BucketUI import BucketWidget
def main():
app = QtWidgets.QApplication(sys.argv)
window = QtWidgets.QMainWindow()
window.setGeometry(
QtWidgets.QApplication.screens()[0].size().width() / 2
- (window.geometry().width() / 2),
QtWidgets.QApplication.screens()[0].size().height() / 2
- (window.geometry().height() / 2),
window.geometry().width() + 10,
750,
)
central_widget = QtWidgets.QWidget(window)
central_widget.setLayout(QtWidgets.QVBoxLayout())
window.setCentralWidget(central_widget)
test_widget = MainWidget()
get_args = QtWidgets.QPushButton()
get_args.setText("Get Args")
get_args.clicked.connect(lambda: print(test_widget.get_args))
save_toml = QtWidgets.QPushButton()
save_toml.setText("Save Toml")
save_toml.clicked.connect(lambda: test_widget.save_toml())
load_toml = QtWidgets.QPushButton()
load_toml.setText("Load Toml")
load_toml.clicked.connect(lambda: test_widget.load_toml())
central_widget.layout().addWidget(test_widget)
central_widget.layout().addWidget(get_args)
central_widget.layout().addWidget(save_toml)
central_widget.layout().addWidget(load_toml)
window.show()
app.exec()
def test_load_args(test_widget):
args = {
"general_args": {
"pretrained_model_name_or_path": "F:/ai_stuff/stable_diffusion/AnimeFullFinal.safetensors",
"mixed_precision": "bf16",
"seed": 23,
"max_data_loader_n_workers": 1,
"persistent_data_loader_workers": True,
"max_token_length": 225,
"prior_loss_weight": 1.0,
"clip_skip": 2,
"xformers": True,
"max_train_epochs": 10,
"gradient_accumulation_steps": 4,
},
"network_args": {
"network_dim": 4,
"network_alpha": 1.0,
"min_timestep": 0,
"max_timestep": 1000,
"network_dropout": 0.5,
"network_args": {
"conv_dim": 16,
"conv_alpha": 8.0,
"module_dropout": 0.25,
"down_lr_weight": [
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
],
"mid_lr_weight": 1.0,
"up_lr_weight": [
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
1.0,
],
"block_dims": [
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
],
"block_alphas": [
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
],
"conv_block_dims": [
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
32,
],
"conv_block_alphas": [
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
16.0,
],
},
},
"optimizer_args": {
"optimizer_type": "AdamW8bit",
"lr_scheduler": "cosine",
"learning_rate": 0.0001,
"max_grad_norm": 1.0,
"lr_scheduler_type": "LoraEasyCustomOptimizer.CustomOptimizers.CosineAnnealingWarmupRestarts",
"lr_scheduler_num_cycles": 4,
"unet_lr": 0.0005,
"warmup_ratio": 0.1,
"min_snr_gamma": 8,
"scale_weight_norms": 5.0,
"lr_scheduler_args": {"min_lr": 1e-06, "gamma": 0.85},
"optimizer_args": {"weight_decay": "0.1", "betas": "0.9,0.99"},
},
"saving_args": {
"output_dir": "F:/Desktop/wdas",
"save_precision": "fp16",
"save_model_as": "safetensors",
"output_name": "szcb911",
"save_every_n_epochs": 2,
"save_toml": True,
},
"noise_args": {"multires_noise_iterations": 6, "multires_noise_discount": 0.3},
}
dataset = {
"general_args": {"resolution": 896, "batch_size": 2},
"bucket_args": {
"enable_bucket": True,
"min_bucket_reso": 256,
"max_bucket_reso": 1024,
"bucket_reso_steps": 64,
},
"subsets": [
{
"num_repeats": 4,
"caption_extension": ".txt",
"shuffle_caption": True,
"random_crop": True,
"image_dir": "F:/ai_stuff/stable_diffusion/lora_datasets/styles/szcb911",
"keep_tokens": 0,
"caption_dropout_rate": 0.04,
"caption_dropout_every_n_epochs": 0,
"caption_tag_dropout_rate": 0.0,
},
{
"num_repeats": 4,
"caption_extension": ".txt",
"shuffle_caption": True,
"random_crop": True,
"image_dir": "F:/ai_stuff/stable_diffusion/lora_datasets/styles/szcb911",
"keep_tokens": 0,
"caption_dropout_rate": 0.04,
"caption_dropout_every_n_epochs": 0,
"caption_tag_dropout_rate": 0.0,
"name": "test_12327381923798",
},
],
}
test_widget.load_args(args, dataset)
if __name__ == "__main__":
main()