Improve GUI
This commit is contained in:
bmaltais
parent
6b41f4f951
commit
2c069d1067
@ -26,10 +26,10 @@ def UI(username, password):
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output_dir_input,
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logging_dir_input,
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) = dreambooth_tab()
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with gr.Tab('Dreambooth LoRA'):
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lora_tab()
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with gr.Tab('Finetune'):
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finetune_tab()
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with gr.Tab('LoRA'):
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lora_tab()
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with gr.Tab('Utilities'):
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utilities_tab(
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train_data_dir_input=train_data_dir_input,
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65
lora_gui.py
65
lora_gui.py
@ -42,7 +42,6 @@ def save_configuration(
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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@ -65,7 +64,7 @@ def save_configuration(
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shuffle_caption,
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save_state,
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resume,
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prior_loss_weight, text_encoder_lr, unet_lr, network_train, network_dim
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prior_loss_weight, text_encoder_lr, unet_lr, network_dim
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):
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original_file_path = file_path
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@ -94,7 +93,6 @@ def save_configuration(
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'reg_data_dir': reg_data_dir,
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'output_dir': output_dir,
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'max_resolution': max_resolution,
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'learning_rate': learning_rate,
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'lr_scheduler': lr_scheduler,
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'lr_warmup': lr_warmup,
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'train_batch_size': train_batch_size,
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@ -120,7 +118,6 @@ def save_configuration(
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'prior_loss_weight': prior_loss_weight,
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'text_encoder_lr': text_encoder_lr,
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'unet_lr': unet_lr,
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'network_train': network_train,
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'network_dim': network_dim
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}
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@ -141,7 +138,6 @@ def open_configuration(
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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@ -164,7 +160,7 @@ def open_configuration(
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shuffle_caption,
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save_state,
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resume,
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prior_loss_weight, text_encoder_lr, unet_lr, network_train, network_dim
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prior_loss_weight, text_encoder_lr, unet_lr, network_dim
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):
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original_file_path = file_path
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@ -192,7 +188,6 @@ def open_configuration(
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my_data.get('reg_data_dir', reg_data_dir),
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my_data.get('output_dir', output_dir),
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my_data.get('max_resolution', max_resolution),
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my_data.get('learning_rate', learning_rate),
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my_data.get('lr_scheduler', lr_scheduler),
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my_data.get('lr_warmup', lr_warmup),
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my_data.get('train_batch_size', train_batch_size),
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@ -220,7 +215,6 @@ def open_configuration(
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my_data.get('prior_loss_weight', prior_loss_weight),
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my_data.get('text_encoder_lr', text_encoder_lr),
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my_data.get('unet_lr', unet_lr),
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my_data.get('network_train', network_train),
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my_data.get('network_dim', network_dim),
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)
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@ -234,7 +228,6 @@ def train_model(
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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@ -257,7 +250,7 @@ def train_model(
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shuffle_caption,
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save_state,
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resume,
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prior_loss_weight, text_encoder_lr, unet_lr, network_train, network_dim
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prior_loss_weight, text_encoder_lr, unet_lr, network_dim
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):
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def save_inference_file(output_dir, v2, v_parameterization):
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# Copy inference model for v2 if required
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@ -294,6 +287,14 @@ def train_model(
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if output_dir == '':
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msgbox('Output folder path is missing')
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return
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# If string is empty set string to 0.
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if text_encoder_lr == '': text_encoder_lr = 0
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if unet_lr == '': unet_lr = 0
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if (float(text_encoder_lr) == 0) and (float(unet_lr) == 0):
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msgbox('At least one Learning Rate value for "Text encoder" or "Unet" need to be provided')
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return
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# Get a list of all subfolders in train_data_dir
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subfolders = [
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@ -394,7 +395,7 @@ def train_model(
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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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run_cmd += f' --train_batch_size={train_batch_size}'
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run_cmd += f' --learning_rate={learning_rate}'
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# run_cmd += f' --learning_rate={learning_rate}'
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run_cmd += f' --lr_scheduler={lr_scheduler}'
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run_cmd += f' --lr_warmup_steps={lr_warmup_steps}'
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run_cmd += f' --max_train_steps={max_train_steps}'
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@ -418,14 +419,18 @@ def train_model(
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if not float(prior_loss_weight) == 1.0:
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run_cmd += f' --prior_loss_weight={prior_loss_weight}'
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run_cmd += f' --network_module=networks.lora'
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if not text_encoder_lr == '':
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if not float(text_encoder_lr) == 0:
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run_cmd += f' --text_encoder_lr={text_encoder_lr}'
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if not unet_lr == '':
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run_cmd += f' --unet_lr={unet_lr}'
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if network_train == 'Text encoder only':
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run_cmd += f' --network_train_text_encoder_only'
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elif network_train == 'Unet only':
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else:
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run_cmd += f' --network_train_unet_only'
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if not float(unet_lr) == 0:
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run_cmd += f' --unet_lr={unet_lr}'
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else:
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run_cmd += f' --network_train_text_encoder_only'
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# if network_train == 'Text encoder only':
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# run_cmd += f' --network_train_text_encoder_only'
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# elif network_train == 'Unet only':
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# run_cmd += f' --network_train_unet_only'
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run_cmd += f' --network_dim={network_dim}'
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@ -695,7 +700,7 @@ def lora_tab(
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)
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with gr.Tab('Training parameters'):
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with gr.Row():
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learning_rate_input = gr.Textbox(label='Learning rate', value=1e-4)
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# learning_rate_input = gr.Textbox(label='Learning rate', value=1e-4, visible=False)
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lr_scheduler_input = gr.Dropdown(
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label='LR Scheduler',
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choices=[
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@ -712,16 +717,16 @@ def lora_tab(
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with gr.Row():
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text_encoder_lr = gr.Textbox(label='Text Encoder learning rate', value=1e-6, placeholder='Optional')
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unet_lr = gr.Textbox(label='Unet learning rate', value=1e-4, placeholder='Optional')
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network_train =gr.Dropdown(
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label='Network to train',
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choices=[
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'Text encoder and Unet',
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'Text encoder only',
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'Unet only',
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],
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value='Text encoder and Unet',
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interactive=True
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)
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# network_train = gr.Dropdown(
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# label='Network to train',
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# choices=[
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# 'Text encoder and Unet',
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# 'Text encoder only',
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# 'Unet only',
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# ],
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# value='Text encoder and Unet',
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# interactive=True
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# )
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network_dim = gr.Slider(
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minimum=1,
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maximum=32,
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@ -846,7 +851,7 @@ def lora_tab(
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reg_data_dir_input,
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output_dir_input,
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max_resolution_input,
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learning_rate_input,
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# learning_rate_input,
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lr_scheduler_input,
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lr_warmup_input,
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train_batch_size_input,
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@ -869,7 +874,7 @@ def lora_tab(
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shuffle_caption,
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save_state,
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resume,
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prior_loss_weight, text_encoder_lr, unet_lr, network_train, network_dim
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prior_loss_weight, text_encoder_lr, unet_lr, network_dim
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]
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button_open_config.click(
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