# v1: initial release # v2: add open and save folder icons # v3: Add new Utilities tab for Dreambooth folder preparation # v3.1: Adding captionning of images to utilities import gradio as gr import easygui import json import math import os import subprocess import pathlib import argparse from library.common_gui import ( get_folder_path, remove_doublequote, get_file_path, get_any_file_path, get_saveasfile_path, color_aug_changed, save_inference_file, gradio_advanced_training, run_cmd_advanced_training, gradio_training, gradio_config, gradio_source_model, run_cmd_training, # set_legacy_8bitadam, update_my_data, check_if_model_exist, ) from library.dreambooth_folder_creation_gui import ( gradio_dreambooth_folder_creation_tab, ) from library.tensorboard_gui import ( gradio_tensorboard, start_tensorboard, stop_tensorboard, ) from library.dataset_balancing_gui import gradio_dataset_balancing_tab from library.utilities import utilities_tab from library.merge_lora_gui import gradio_merge_lora_tab from library.svd_merge_lora_gui import gradio_svd_merge_lora_tab from library.verify_lora_gui import gradio_verify_lora_tab from library.resize_lora_gui import gradio_resize_lora_tab from library.sampler_gui import sample_gradio_config, run_cmd_sample from easygui import msgbox folder_symbol = '\U0001f4c2' # 📂 refresh_symbol = '\U0001f504' # 🔄 save_style_symbol = '\U0001f4be' # 💾 document_symbol = '\U0001F4C4' # 📄 path_of_this_folder = os.getcwd() def save_configuration( save_as, file_path, pretrained_model_name_or_path, v2, v_parameterization, logging_dir, train_data_dir, reg_data_dir, output_dir, max_resolution, learning_rate, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latents, caption_extension, enable_bucket, gradient_checkpointing, full_fp16, no_token_padding, stop_text_encoder_training, # use_8bit_adam, xformers, save_model_as, shuffle_caption, save_state, resume, prior_loss_weight, text_encoder_lr, unet_lr, network_dim, lora_network_weights, color_aug, flip_aug, clip_skip, gradient_accumulation_steps, mem_eff_attn, output_name, model_list, max_token_length, max_train_epochs, max_data_loader_n_workers, network_alpha, training_comment, keep_tokens, lr_scheduler_num_cycles, lr_scheduler_power, persistent_data_loader_workers, bucket_no_upscale, random_crop, bucket_reso_steps, caption_dropout_every_n_epochs, caption_dropout_rate, optimizer, optimizer_args, noise_offset, LoRA_type, conv_dim, conv_alpha, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, vae_batch_size, min_snr_gamma, ): # Get list of function parameters and values parameters = list(locals().items()) original_file_path = file_path save_as_bool = True if save_as.get('label') == 'True' else False if save_as_bool: print('Save as...') file_path = get_saveasfile_path(file_path) else: print('Save...') if file_path == None or file_path == '': file_path = get_saveasfile_path(file_path) # print(file_path) if file_path == None or file_path == '': return original_file_path # In case a file_path was provided and the user decide to cancel the open action # Return the values of the variables as a dictionary variables = { name: value for name, value in parameters # locals().items() if name not in [ 'file_path', 'save_as', ] } # Extract the destination directory from the file path destination_directory = os.path.dirname(file_path) # Create the destination directory if it doesn't exist if not os.path.exists(destination_directory): os.makedirs(destination_directory) # Save the data to the selected file with open(file_path, 'w') as file: json.dump(variables, file, indent=2) return file_path def open_configuration( ask_for_file, file_path, pretrained_model_name_or_path, v2, v_parameterization, logging_dir, train_data_dir, reg_data_dir, output_dir, max_resolution, learning_rate, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latents, caption_extension, enable_bucket, gradient_checkpointing, full_fp16, no_token_padding, stop_text_encoder_training, # use_8bit_adam, xformers, save_model_as, shuffle_caption, save_state, resume, prior_loss_weight, text_encoder_lr, unet_lr, network_dim, lora_network_weights, color_aug, flip_aug, clip_skip, gradient_accumulation_steps, mem_eff_attn, output_name, model_list, max_token_length, max_train_epochs, max_data_loader_n_workers, network_alpha, training_comment, keep_tokens, lr_scheduler_num_cycles, lr_scheduler_power, persistent_data_loader_workers, bucket_no_upscale, random_crop, bucket_reso_steps, caption_dropout_every_n_epochs, caption_dropout_rate, optimizer, optimizer_args, noise_offset, LoRA_type, conv_dim, conv_alpha, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, vae_batch_size, min_snr_gamma, ): # Get list of function parameters and values parameters = list(locals().items()) ask_for_file = True if ask_for_file.get('label') == 'True' else False original_file_path = file_path if ask_for_file: file_path = get_file_path(file_path) if not file_path == '' and not file_path == None: # load variables from JSON file with open(file_path, 'r') as f: my_data = json.load(f) print('Loading config...') # Update values to fix deprecated use_8bit_adam checkbox, set appropriate optimizer if it is set to True, etc. my_data = update_my_data(my_data) else: file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action my_data = {} values = [file_path] for key, value in parameters: # Set the value in the dictionary to the corresponding value in `my_data`, or the default value if not found if not key in ['ask_for_file', 'file_path']: values.append(my_data.get(key, value)) # This next section is about making the LoCon parameters visible if LoRA_type = 'Standard' if my_data.get('LoRA_type', 'Standard') == 'LoCon': values.append(gr.Row.update(visible=True)) else: values.append(gr.Row.update(visible=False)) return tuple(values) def train_model( print_only, pretrained_model_name_or_path, v2, v_parameterization, logging_dir, train_data_dir, reg_data_dir, output_dir, max_resolution, learning_rate, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latents, caption_extension, enable_bucket, gradient_checkpointing, full_fp16, no_token_padding, stop_text_encoder_training_pct, # use_8bit_adam, xformers, save_model_as, shuffle_caption, save_state, resume, prior_loss_weight, text_encoder_lr, unet_lr, network_dim, lora_network_weights, color_aug, flip_aug, clip_skip, gradient_accumulation_steps, mem_eff_attn, output_name, model_list, # Keep this. Yes, it is unused here but required given the common list used max_token_length, max_train_epochs, max_data_loader_n_workers, network_alpha, training_comment, keep_tokens, lr_scheduler_num_cycles, lr_scheduler_power, persistent_data_loader_workers, bucket_no_upscale, random_crop, bucket_reso_steps, caption_dropout_every_n_epochs, caption_dropout_rate, optimizer, optimizer_args, noise_offset, LoRA_type, conv_dim, conv_alpha, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, vae_batch_size, min_snr_gamma, ): print_only_bool = True if print_only.get('label') == 'True' else False if pretrained_model_name_or_path == '': msgbox('Source model information is missing') return if train_data_dir == '': msgbox('Image folder path is missing') return if not os.path.exists(train_data_dir): msgbox('Image folder does not exist') return if reg_data_dir != '': if not os.path.exists(reg_data_dir): msgbox('Regularisation folder does not exist') return if output_dir == '': msgbox('Output folder path is missing') return if int(bucket_reso_steps) < 1: msgbox('Bucket resolution steps need to be greater than 0') return if not os.path.exists(output_dir): os.makedirs(output_dir) if stop_text_encoder_training_pct > 0: msgbox( 'Output "stop text encoder training" is not yet supported. Ignoring' ) stop_text_encoder_training_pct = 0 if check_if_model_exist(output_name, output_dir, save_model_as): return # If string is empty set string to 0. if text_encoder_lr == '': text_encoder_lr = 0 if unet_lr == '': unet_lr = 0 # if (float(text_encoder_lr) == 0) and (float(unet_lr) == 0): # msgbox( # 'At least one Learning Rate value for "Text encoder" or "Unet" need to be provided' # ) # return # Get a list of all subfolders in train_data_dir subfolders = [ f for f in os.listdir(train_data_dir) if os.path.isdir(os.path.join(train_data_dir, f)) ] total_steps = 0 # Loop through each subfolder and extract the number of repeats for folder in subfolders: # Extract the number of repeats from the folder name repeats = int(folder.split('_')[0]) # Count the number of images in the folder num_images = len( [ f for f, lower_f in ( (file, file.lower()) for file in os.listdir( os.path.join(train_data_dir, folder) ) ) if lower_f.endswith(('.jpg', '.jpeg', '.png', '.webp')) ] ) print(f'Folder {folder}: {num_images} images found') # Calculate the total number of steps for this folder steps = repeats * num_images # Print the result print(f'Folder {folder}: {steps} steps') total_steps += steps # calculate max_train_steps max_train_steps = int( math.ceil( float(total_steps) / int(train_batch_size) * int(epoch) # * int(reg_factor) ) ) print(f'max_train_steps = {max_train_steps}') # calculate stop encoder training if stop_text_encoder_training_pct == None: stop_text_encoder_training = 0 else: stop_text_encoder_training = math.ceil( float(max_train_steps) / 100 * int(stop_text_encoder_training_pct) ) print(f'stop_text_encoder_training = {stop_text_encoder_training}') lr_warmup_steps = round(float(int(lr_warmup) * int(max_train_steps) / 100)) print(f'lr_warmup_steps = {lr_warmup_steps}') run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "train_network.py"' # run_cmd += f' --caption_dropout_rate="0.1" --caption_dropout_every_n_epochs=1' # --random_crop' if v2: run_cmd += ' --v2' if v_parameterization: run_cmd += ' --v_parameterization' if enable_bucket: run_cmd += ' --enable_bucket' if no_token_padding: run_cmd += ' --no_token_padding' run_cmd += ( f' --pretrained_model_name_or_path="{pretrained_model_name_or_path}"' ) run_cmd += f' --train_data_dir="{train_data_dir}"' if len(reg_data_dir): run_cmd += f' --reg_data_dir="{reg_data_dir}"' run_cmd += f' --resolution={max_resolution}' run_cmd += f' --output_dir="{output_dir}"' run_cmd += f' --logging_dir="{logging_dir}"' run_cmd += f' --network_alpha="{network_alpha}"' if not training_comment == '': run_cmd += f' --training_comment="{training_comment}"' if not stop_text_encoder_training == 0: run_cmd += ( f' --stop_text_encoder_training={stop_text_encoder_training}' ) if not save_model_as == 'same as source model': run_cmd += f' --save_model_as={save_model_as}' if not float(prior_loss_weight) == 1.0: run_cmd += f' --prior_loss_weight={prior_loss_weight}' if LoRA_type == 'LoCon' or LoRA_type == 'LyCORIS/LoCon': try: import lycoris except ModuleNotFoundError: print( "\033[1;31mError:\033[0m The required module 'lycoris_lora' is not installed. Please install by running \033[33mupgrade.ps1\033[0m before running this program." ) return run_cmd += f' --network_module=lycoris.kohya' run_cmd += f' --network_args "conv_dim={conv_dim}" "conv_alpha={conv_alpha}" "algo=lora"' if LoRA_type == 'LyCORIS/LoHa': try: import lycoris except ModuleNotFoundError: print( "\033[1;31mError:\033[0m The required module 'lycoris_lora' is not installed. Please install by running \033[33mupgrade.ps1\033[0m before running this program." ) return run_cmd += f' --network_module=lycoris.kohya' run_cmd += f' --network_args "conv_dim={conv_dim}" "conv_alpha={conv_alpha}" "algo=loha"' if LoRA_type == 'Kohya LoCon': run_cmd += f' --network_module=networks.lora' run_cmd += ( f' --network_args "conv_dim={conv_dim}" "conv_alpha={conv_alpha}"' ) if LoRA_type == 'Standard': run_cmd += f' --network_module=networks.lora' if not (float(text_encoder_lr) == 0) or not (float(unet_lr) == 0): if not (float(text_encoder_lr) == 0) and not (float(unet_lr) == 0): run_cmd += f' --text_encoder_lr={text_encoder_lr}' run_cmd += f' --unet_lr={unet_lr}' elif not (float(text_encoder_lr) == 0): run_cmd += f' --text_encoder_lr={text_encoder_lr}' run_cmd += f' --network_train_text_encoder_only' else: run_cmd += f' --unet_lr={unet_lr}' run_cmd += f' --network_train_unet_only' else: if float(text_encoder_lr) == 0: msgbox('Please input learning rate values.') return run_cmd += f' --network_dim={network_dim}' if not lora_network_weights == '': run_cmd += f' --network_weights="{lora_network_weights}"' if int(gradient_accumulation_steps) > 1: run_cmd += f' --gradient_accumulation_steps={int(gradient_accumulation_steps)}' if not output_name == '': run_cmd += f' --output_name="{output_name}"' if not lr_scheduler_num_cycles == '': run_cmd += f' --lr_scheduler_num_cycles="{lr_scheduler_num_cycles}"' else: run_cmd += f' --lr_scheduler_num_cycles="{epoch}"' if not lr_scheduler_power == '': run_cmd += f' --lr_scheduler_power="{lr_scheduler_power}"' run_cmd += run_cmd_training( learning_rate=learning_rate, lr_scheduler=lr_scheduler, lr_warmup_steps=lr_warmup_steps, train_batch_size=train_batch_size, max_train_steps=max_train_steps, save_every_n_epochs=save_every_n_epochs, mixed_precision=mixed_precision, save_precision=save_precision, seed=seed, caption_extension=caption_extension, cache_latents=cache_latents, optimizer=optimizer, optimizer_args=optimizer_args, ) run_cmd += run_cmd_advanced_training( max_train_epochs=max_train_epochs, max_data_loader_n_workers=max_data_loader_n_workers, max_token_length=max_token_length, resume=resume, save_state=save_state, mem_eff_attn=mem_eff_attn, clip_skip=clip_skip, flip_aug=flip_aug, color_aug=color_aug, shuffle_caption=shuffle_caption, gradient_checkpointing=gradient_checkpointing, full_fp16=full_fp16, xformers=xformers, # use_8bit_adam=use_8bit_adam, keep_tokens=keep_tokens, persistent_data_loader_workers=persistent_data_loader_workers, bucket_no_upscale=bucket_no_upscale, random_crop=random_crop, bucket_reso_steps=bucket_reso_steps, caption_dropout_every_n_epochs=caption_dropout_every_n_epochs, caption_dropout_rate=caption_dropout_rate, noise_offset=noise_offset, additional_parameters=additional_parameters, vae_batch_size=vae_batch_size, min_snr_gamma=min_snr_gamma, ) run_cmd += run_cmd_sample( sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, output_dir, ) if print_only_bool: print( '\033[93m\nHere is the trainer command as a reference. It will not be executed:\033[0m\n' ) print('\033[96m' + run_cmd + '\033[0m\n') else: print(run_cmd) # Run the command if os.name == 'posix': os.system(run_cmd) else: subprocess.run(run_cmd) # check if output_dir/last is a folder... therefore it is a diffuser model last_dir = pathlib.Path(f'{output_dir}/{output_name}') if not last_dir.is_dir(): # Copy inference model for v2 if required save_inference_file( output_dir, v2, v_parameterization, output_name ) def lora_tab( train_data_dir_input=gr.Textbox(), reg_data_dir_input=gr.Textbox(), output_dir_input=gr.Textbox(), logging_dir_input=gr.Textbox(), ): dummy_db_true = gr.Label(value=True, visible=False) dummy_db_false = gr.Label(value=False, visible=False) gr.Markdown( 'Train a custom model using kohya train network LoRA python code...' ) ( button_open_config, button_save_config, button_save_as_config, config_file_name, button_load_config, ) = gradio_config() ( pretrained_model_name_or_path, v2, v_parameterization, save_model_as, model_list, ) = gradio_source_model( save_model_as_choices=[ 'ckpt', 'safetensors', ] ) with gr.Tab('Folders'): with gr.Row(): train_data_dir = gr.Textbox( label='Image folder', placeholder='Folder where the training folders containing the images are located', ) train_data_dir_folder = gr.Button('📂', elem_id='open_folder_small') train_data_dir_folder.click( get_folder_path, outputs=train_data_dir, show_progress=False, ) reg_data_dir = gr.Textbox( label='Regularisation folder', placeholder='(Optional) Folder where where the regularization folders containing the images are located', ) reg_data_dir_folder = gr.Button('📂', elem_id='open_folder_small') reg_data_dir_folder.click( get_folder_path, outputs=reg_data_dir, show_progress=False, ) with gr.Row(): output_dir = gr.Textbox( label='Output folder', placeholder='Folder to output trained model', ) output_dir_folder = gr.Button('📂', elem_id='open_folder_small') output_dir_folder.click( get_folder_path, outputs=output_dir, show_progress=False, ) logging_dir = gr.Textbox( label='Logging folder', placeholder='Optional: enable logging and output TensorBoard log to this folder', ) logging_dir_folder = gr.Button('📂', elem_id='open_folder_small') logging_dir_folder.click( get_folder_path, outputs=logging_dir, show_progress=False, ) with gr.Row(): output_name = gr.Textbox( label='Model output name', placeholder='(Name of the model to output)', value='last', interactive=True, ) training_comment = gr.Textbox( label='Training comment', placeholder='(Optional) Add training comment to be included in metadata', interactive=True, ) train_data_dir.change( remove_doublequote, inputs=[train_data_dir], outputs=[train_data_dir], ) reg_data_dir.change( remove_doublequote, inputs=[reg_data_dir], outputs=[reg_data_dir], ) output_dir.change( remove_doublequote, inputs=[output_dir], outputs=[output_dir], ) logging_dir.change( remove_doublequote, inputs=[logging_dir], outputs=[logging_dir], ) with gr.Tab('Training parameters'): with gr.Row(): LoRA_type = gr.Dropdown( label='LoRA type', choices=[ 'Kohya LoCon', # 'LoCon', 'LyCORIS/LoCon', 'LyCORIS/LoHa', 'Standard', ], value='Standard', ) lora_network_weights = gr.Textbox( label='LoRA network weights', placeholder='{Optional) Path to existing LoRA network weights to resume training', ) lora_network_weights_file = gr.Button( document_symbol, elem_id='open_folder_small' ) lora_network_weights_file.click( get_any_file_path, inputs=[lora_network_weights], outputs=lora_network_weights, show_progress=False, ) ( learning_rate, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, num_cpu_threads_per_process, seed, caption_extension, cache_latents, optimizer, optimizer_args, ) = gradio_training( learning_rate_value='0.0001', lr_scheduler_value='cosine', lr_warmup_value='10', ) with gr.Row(): text_encoder_lr = gr.Textbox( label='Text Encoder learning rate', value='5e-5', placeholder='Optional', ) unet_lr = gr.Textbox( label='Unet learning rate', value='0.0001', placeholder='Optional', ) network_dim = gr.Slider( minimum=1, maximum=1024, label='Network Rank (Dimension)', value=8, step=1, interactive=True, ) network_alpha = gr.Slider( minimum=1, maximum=1024, label='Network Alpha', value=1, step=1, interactive=True, ) with gr.Row(visible=False) as LoCon_row: # locon= gr.Checkbox(label='Train a LoCon instead of a general LoRA (does not support v2 base models) (may not be able to some utilities now)', value=False) conv_dim = gr.Slider( minimum=1, maximum=512, value=1, step=1, label='Convolution Rank (Dimension)', ) conv_alpha = gr.Slider( minimum=1, maximum=512, value=1, step=1, label='Convolution Alpha', ) # Show of hide LoCon conv settings depending on LoRA type selection def LoRA_type_change(LoRA_type): print('LoRA type changed...') if ( LoRA_type == 'LoCon' or LoRA_type == 'Kohya LoCon' or LoRA_type == 'LyCORIS/LoHa' or LoRA_type == 'LyCORIS/LoCon' ): return gr.Group.update(visible=True) else: return gr.Group.update(visible=False) LoRA_type.change( LoRA_type_change, inputs=[LoRA_type], outputs=[LoCon_row] ) with gr.Row(): max_resolution = gr.Textbox( label='Max resolution', value='512,512', placeholder='512,512', ) stop_text_encoder_training = gr.Slider( minimum=0, maximum=100, value=0, step=1, label='Stop text encoder training', ) enable_bucket = gr.Checkbox(label='Enable buckets', value=True) with gr.Accordion('Advanced Configuration', open=False): with gr.Row(): no_token_padding = gr.Checkbox( label='No token padding', value=False ) gradient_accumulation_steps = gr.Number( label='Gradient accumulate steps', value='1' ) with gr.Row(): prior_loss_weight = gr.Number( label='Prior loss weight', value=1.0 ) lr_scheduler_num_cycles = gr.Textbox( label='LR number of cycles', placeholder='(Optional) For Cosine with restart and polynomial only', ) lr_scheduler_power = gr.Textbox( label='LR power', placeholder='(Optional) For Cosine with restart and polynomial only', ) ( # use_8bit_adam, xformers, full_fp16, gradient_checkpointing, shuffle_caption, color_aug, flip_aug, clip_skip, mem_eff_attn, save_state, resume, max_token_length, max_train_epochs, max_data_loader_n_workers, keep_tokens, persistent_data_loader_workers, bucket_no_upscale, random_crop, bucket_reso_steps, caption_dropout_every_n_epochs, caption_dropout_rate, noise_offset, additional_parameters, vae_batch_size, min_snr_gamma, ) = gradio_advanced_training() color_aug.change( color_aug_changed, inputs=[color_aug], outputs=[cache_latents], ) ( sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, ) = sample_gradio_config() with gr.Tab('Tools'): gr.Markdown( 'This section provide Dreambooth tools to help setup your dataset...' ) gradio_dreambooth_folder_creation_tab( train_data_dir_input=train_data_dir, reg_data_dir_input=reg_data_dir, output_dir_input=output_dir, logging_dir_input=logging_dir, ) gradio_dataset_balancing_tab() gradio_merge_lora_tab() gradio_svd_merge_lora_tab() gradio_resize_lora_tab() gradio_verify_lora_tab() button_run = gr.Button('Train model', variant='primary') button_print = gr.Button('Print training command') # Setup gradio tensorboard buttons button_start_tensorboard, button_stop_tensorboard = gradio_tensorboard() button_start_tensorboard.click( start_tensorboard, inputs=logging_dir, show_progress=False, ) button_stop_tensorboard.click( stop_tensorboard, show_progress=False, ) settings_list = [ pretrained_model_name_or_path, v2, v_parameterization, logging_dir, train_data_dir, reg_data_dir, output_dir, max_resolution, learning_rate, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latents, caption_extension, enable_bucket, gradient_checkpointing, full_fp16, no_token_padding, stop_text_encoder_training, # use_8bit_adam, xformers, save_model_as, shuffle_caption, save_state, resume, prior_loss_weight, text_encoder_lr, unet_lr, network_dim, lora_network_weights, color_aug, flip_aug, clip_skip, gradient_accumulation_steps, mem_eff_attn, output_name, model_list, max_token_length, max_train_epochs, max_data_loader_n_workers, network_alpha, training_comment, keep_tokens, lr_scheduler_num_cycles, lr_scheduler_power, persistent_data_loader_workers, bucket_no_upscale, random_crop, bucket_reso_steps, caption_dropout_every_n_epochs, caption_dropout_rate, optimizer, optimizer_args, noise_offset, LoRA_type, conv_dim, conv_alpha, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, vae_batch_size, min_snr_gamma, ] button_open_config.click( open_configuration, inputs=[dummy_db_true, config_file_name] + settings_list, outputs=[config_file_name] + settings_list + [LoCon_row], show_progress=False, ) button_load_config.click( open_configuration, inputs=[dummy_db_false, config_file_name] + settings_list, outputs=[config_file_name] + settings_list + [LoCon_row], show_progress=False, ) button_save_config.click( save_configuration, inputs=[dummy_db_false, config_file_name] + settings_list, outputs=[config_file_name], show_progress=False, ) button_save_as_config.click( save_configuration, inputs=[dummy_db_true, config_file_name] + settings_list, outputs=[config_file_name], show_progress=False, ) button_run.click( train_model, inputs=[dummy_db_false] + settings_list, show_progress=False, ) button_print.click( train_model, inputs=[dummy_db_true] + settings_list, show_progress=False, ) return ( train_data_dir, reg_data_dir, output_dir, logging_dir, ) def UI(**kwargs): css = '' if os.path.exists('./style.css'): with open(os.path.join('./style.css'), 'r', encoding='utf8') as file: print('Load CSS...') css += file.read() + '\n' interface = gr.Blocks(css=css) with interface: with gr.Tab('LoRA'): ( train_data_dir_input, reg_data_dir_input, output_dir_input, logging_dir_input, ) = lora_tab() with gr.Tab('Utilities'): utilities_tab( train_data_dir_input=train_data_dir_input, reg_data_dir_input=reg_data_dir_input, output_dir_input=output_dir_input, logging_dir_input=logging_dir_input, enable_copy_info_button=True, ) # Show the interface launch_kwargs = {} if not kwargs.get('username', None) == '': launch_kwargs['auth'] = ( kwargs.get('username', None), kwargs.get('password', None), ) if kwargs.get('server_port', 0) > 0: launch_kwargs['server_port'] = kwargs.get('server_port', 0) if kwargs.get('inbrowser', False): launch_kwargs['inbrowser'] = kwargs.get('inbrowser', False) if kwargs.get('listen', True): launch_kwargs['server_name'] = '0.0.0.0' print(launch_kwargs) interface.launch(**launch_kwargs) if __name__ == '__main__': # torch.cuda.set_per_process_memory_fraction(0.48) parser = argparse.ArgumentParser() parser.add_argument( '--username', type=str, default='', help='Username for authentication' ) parser.add_argument( '--password', type=str, default='', help='Password for authentication' ) parser.add_argument( '--server_port', type=int, default=0, help='Port to run the server listener on', ) parser.add_argument( '--inbrowser', action='store_true', help='Open in browser' ) parser.add_argument( '--listen', action='store_true', help='Launch gradio with server name 0.0.0.0, allowing LAN access', ) args = parser.parse_args() UI( username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port, )