mirror of https://github.com/bmaltais/kohya_ss
Sort json config vars
parent
cdf15c6015
commit
5d03104894
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@ -152,12 +152,8 @@ def save_configuration(
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# Return the values of the variables as a dictionary
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variables = {
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name: value
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for name, value in parameters # locals().items()
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if name
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not in [
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'file_path',
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'save_as',
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]
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for name, value in sorted(parameters, key=lambda x: x[0])
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if name not in ['file_path', 'save_as']
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}
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# Extract the destination directory from the file path
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@ -517,7 +513,7 @@ def train_model(
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run_cmd += f' --train_data_dir="{train_data_dir}"'
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if len(reg_data_dir):
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run_cmd += f' --reg_data_dir="{reg_data_dir}"'
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run_cmd += f' --resolution={max_resolution}'
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run_cmd += f' --resolution="{max_resolution}"'
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run_cmd += f' --output_dir="{output_dir}"'
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if not logging_dir == '':
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run_cmd += f' --logging_dir="{logging_dir}"'
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@ -151,12 +151,8 @@ def save_configuration(
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# Return the values of the variables as a dictionary
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variables = {
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name: value
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for name, value in parameters # locals().items()
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if name
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not in [
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'file_path',
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'save_as',
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]
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for name, value in sorted(parameters, key=lambda x: x[0])
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if name not in ['file_path', 'save_as']
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}
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# Extract the destination directory from the file path
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@ -501,7 +497,7 @@ def train_model(
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run_cmd += f' --learning_rate={learning_rate}'
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run_cmd += ' --enable_bucket'
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run_cmd += f' --resolution={max_resolution}'
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run_cmd += f' --resolution="{max_resolution}"'
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run_cmd += f' --min_bucket_reso={min_bucket_reso}'
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run_cmd += f' --max_bucket_reso={max_bucket_reso}'
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@ -1002,8 +1002,8 @@ def gradio_advanced_training(headless=False):
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bucket_no_upscale = gr.Checkbox(
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label="Don't upscale bucket resolution", value=True
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)
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bucket_reso_steps = gr.Number(
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label='Bucket resolution steps', value=64
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bucket_reso_steps = gr.Slider(
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label='Bucket resolution steps', value=64, minimum=1, maximum=128
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)
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random_crop = gr.Checkbox(
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label='Random crop instead of center crop', value=False
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10
lora_gui.py
10
lora_gui.py
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@ -190,12 +190,8 @@ def save_configuration(
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# Return the values of the variables as a dictionary
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variables = {
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name: value
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for name, value in parameters # locals().items()
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if name
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not in [
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'file_path',
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'save_as',
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]
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for name, value in sorted(parameters, key=lambda x: x[0])
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if name not in ['file_path', 'save_as']
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}
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# Extract the destination directory from the file path
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@ -652,7 +648,7 @@ def train_model(
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run_cmd += f' --train_data_dir="{train_data_dir}"'
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if len(reg_data_dir):
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run_cmd += f' --reg_data_dir="{reg_data_dir}"'
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run_cmd += f' --resolution={max_resolution}'
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run_cmd += f' --resolution="{max_resolution}"'
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run_cmd += f' --output_dir="{output_dir}"'
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if not logging_dir == '':
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run_cmd += f' --logging_dir="{logging_dir}"'
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@ -157,12 +157,8 @@ def save_configuration(
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# Return the values of the variables as a dictionary
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variables = {
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name: value
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for name, value in parameters # locals().items()
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if name
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not in [
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'file_path',
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'save_as',
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]
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for name, value in sorted(parameters, key=lambda x: x[0])
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if name not in ['file_path', 'save_as']
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}
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# Extract the destination directory from the file path
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@ -530,7 +526,7 @@ def train_model(
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run_cmd += f' --train_data_dir="{train_data_dir}"'
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if len(reg_data_dir):
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run_cmd += f' --reg_data_dir="{reg_data_dir}"'
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run_cmd += f' --resolution={max_resolution}'
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run_cmd += f' --resolution="{max_resolution}"'
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run_cmd += f' --output_dir="{output_dir}"'
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if not logging_dir == '':
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run_cmd += f' --logging_dir="{logging_dir}"'
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