# 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 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, run_cmd_training, gradio_training, gradio_config, gradio_source_model, # set_legacy_8bitadam, update_my_data, check_if_model_exist, ) from library.tensorboard_gui import ( gradio_tensorboard, start_tensorboard, stop_tensorboard, ) from library.dreambooth_folder_creation_gui import ( gradio_dreambooth_folder_creation_tab, ) from library.utilities import utilities_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' # 📄 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, color_aug, flip_aug, clip_skip, vae, output_name, max_token_length, max_train_epochs, max_data_loader_n_workers, mem_eff_attn, gradient_accumulation_steps, model_list, token_string, init_word, num_vectors_per_token, max_train_steps, weights, template, keep_tokens, 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, 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, color_aug, flip_aug, clip_skip, vae, output_name, max_token_length, max_train_epochs, max_data_loader_n_workers, mem_eff_attn, gradient_accumulation_steps, model_list, token_string, init_word, num_vectors_per_token, max_train_steps, weights, template, keep_tokens, 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, 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 and set appropriate optimizer if it is set to True 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)) return tuple(values) def train_model( 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, color_aug, flip_aug, clip_skip, vae, output_name, max_token_length, max_train_epochs, max_data_loader_n_workers, mem_eff_attn, gradient_accumulation_steps, model_list, # Keep this. Yes, it is unused here but required given the common list used token_string, init_word, num_vectors_per_token, max_train_steps, weights, template, keep_tokens, 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, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, vae_batch_size, min_snr_gamma, ): 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 token_string == '': msgbox('Token string is missing') return if init_word == '': msgbox('Init word is missing') return if not os.path.exists(output_dir): os.makedirs(output_dir) if check_if_model_exist(output_name, output_dir, save_model_as): 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')) ] ) # Calculate the total number of steps for this folder steps = repeats * num_images total_steps += steps # Print the result print(f'Folder {folder}: {steps} steps') # Print the result # print(f"{total_steps} total steps") if reg_data_dir == '': reg_factor = 1 else: print( 'Regularisation images are used... Will double the number of steps required...' ) reg_factor = 2 # calculate max_train_steps if max_train_steps == '': max_train_steps = int( math.ceil( float(total_steps) / int(train_batch_size) * int(epoch) * int(reg_factor) ) ) else: max_train_steps = int(max_train_steps) 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_textual_inversion.py"' 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}"' 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 resume == '': # run_cmd += f' --resume={resume}' if not float(prior_loss_weight) == 1.0: run_cmd += f' --prior_loss_weight={prior_loss_weight}' if not vae == '': run_cmd += f' --vae="{vae}"' if not output_name == '': run_cmd += f' --output_name="{output_name}"' if int(max_token_length) > 75: run_cmd += f' --max_token_length={max_token_length}' if not max_train_epochs == '': run_cmd += f' --max_train_epochs="{max_train_epochs}"' if not max_data_loader_n_workers == '': run_cmd += ( f' --max_data_loader_n_workers="{max_data_loader_n_workers}"' ) if int(gradient_accumulation_steps) > 1: run_cmd += f' --gradient_accumulation_steps={int(gradient_accumulation_steps)}' 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 += f' --token_string="{token_string}"' run_cmd += f' --init_word="{init_word}"' run_cmd += f' --num_vectors_per_token={num_vectors_per_token}' if not weights == '': run_cmd += f' --weights="{weights}"' if template == 'object template': run_cmd += f' --use_object_template' elif template == 'style template': run_cmd += f' --use_style_template' run_cmd += run_cmd_sample( sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, output_dir, ) 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 ti_tab( train_data_dir=gr.Textbox(), reg_data_dir=gr.Textbox(), output_dir=gr.Textbox(), logging_dir=gr.Textbox(), ): dummy_db_true = gr.Label(value=True, visible=False) dummy_db_false = gr.Label(value=False, visible=False) gr.Markdown('Train a TI using kohya textual inversion 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_input_folder = gr.Button( '📂', elem_id='open_folder_small' ) train_data_dir_input_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_input_folder = gr.Button( '📂', elem_id='open_folder_small' ) reg_data_dir_input_folder.click( get_folder_path, outputs=reg_data_dir, show_progress=False, ) with gr.Row(): output_dir = gr.Textbox( label='Model output folder', placeholder='Folder to output trained model', ) output_dir_input_folder = gr.Button( '📂', elem_id='open_folder_small' ) output_dir_input_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_input_folder = gr.Button( '📂', elem_id='open_folder_small' ) logging_dir_input_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, ) 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(): weights = gr.Textbox( label='Resume TI training', placeholder='(Optional) Path to existing TI embeding file to keep training', ) weights_file_input = gr.Button('📂', elem_id='open_folder_small') weights_file_input.click( get_file_path, outputs=weights, show_progress=False, ) with gr.Row(): token_string = gr.Textbox( label='Token string', placeholder='eg: cat', ) init_word = gr.Textbox( label='Init word', value='*', ) num_vectors_per_token = gr.Slider( minimum=1, maximum=75, value=1, step=1, label='Vectors', ) max_train_steps = gr.Textbox( label='Max train steps', placeholder='(Optional) Maximum number of steps', ) template = gr.Dropdown( label='Template', choices=[ 'caption', 'object template', 'style template', ], value='caption', ) ( 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='1e-5', lr_scheduler_value='cosine', lr_warmup_value='10', ) 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 ) vae = gr.Textbox( label='VAE', placeholder='(Optiona) path to checkpoint of vae to replace for training', ) vae_button = gr.Button('📂', elem_id='open_folder_small') vae_button.click( get_any_file_path, outputs=vae, show_progress=False, ) ( # 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, ) button_run = gr.Button('Train model', variant='primary') # 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, color_aug, flip_aug, clip_skip, vae, output_name, max_token_length, max_train_epochs, max_data_loader_n_workers, mem_eff_attn, gradient_accumulation_steps, model_list, token_string, init_word, num_vectors_per_token, max_train_steps, weights, template, keep_tokens, 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, 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, show_progress=False, ) button_load_config.click( open_configuration, inputs=[dummy_db_false, config_file_name] + settings_list, outputs=[config_file_name] + settings_list, 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=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('Dreambooth TI'): ( train_data_dir_input, reg_data_dir_input, output_dir_input, logging_dir_input, ) = ti_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) 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' ) args = parser.parse_args() UI( username=args.username, password=args.password, inbrowser=args.inbrowser, server_port=args.server_port, )