# 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 shutil 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, set_pretrained_model_name_or_path_input, ) from library.dreambooth_folder_creation_gui import ( gradio_dreambooth_folder_creation_tab, ) 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 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, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latent, caption_extention, 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, ): # 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', ] } # 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( file_path, pretrained_model_name_or_path, v2, v_parameterization, logging_dir, train_data_dir, reg_data_dir, output_dir, max_resolution, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latent, caption_extention, enable_bucket, gradient_checkpointing, full_fp16, no_token_padding, stop_text_encoder_training, use_8bit_adam, xformers, save_model_as_dropdown, 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, ): # Get list of function parameters and values parameters = list(locals().items()) original_file_path = file_path 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...") 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 ['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, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latent, 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, ): 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 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 in os.listdir(os.path.join(train_data_dir, folder)) if f.endswith('.jpg') or f.endswith('.jpeg') or f.endswith('.png') or f.endswith('.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') # 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"' if v2: run_cmd += ' --v2' if v_parameterization: run_cmd += ' --v_parameterization' if cache_latent: run_cmd += ' --cache_latents' if enable_bucket: run_cmd += ' --enable_bucket' if gradient_checkpointing: run_cmd += ' --gradient_checkpointing' if full_fp16: run_cmd += ' --full_fp16' if no_token_padding: run_cmd += ' --no_token_padding' if use_8bit_adam: run_cmd += ' --use_8bit_adam' if xformers: run_cmd += ' --xformers' if shuffle_caption: run_cmd += ' --shuffle_caption' if save_state: run_cmd += ' --save_state' if color_aug: run_cmd += ' --color_aug' if flip_aug: run_cmd += ' --flip_aug' if mem_eff_attn: run_cmd += ' --mem_eff_attn' 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' --train_batch_size={train_batch_size}' # run_cmd += f' --learning_rate={learning_rate}' run_cmd += f' --lr_scheduler={lr_scheduler}' run_cmd += f' --lr_warmup_steps={lr_warmup_steps}' run_cmd += f' --max_train_steps={max_train_steps}' run_cmd += f' --use_8bit_adam' run_cmd += f' --xformers' run_cmd += f' --mixed_precision={mixed_precision}' run_cmd += f' --save_every_n_epochs={save_every_n_epochs}' run_cmd += f' --seed={seed}' run_cmd += f' --save_precision={save_precision}' run_cmd += f' --logging_dir="{logging_dir}"' if not caption_extension == '': run_cmd += f' --caption_extension={caption_extension}' 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}' run_cmd += f' --network_module=networks.lora' if not float(text_encoder_lr) == 0: run_cmd += f' --text_encoder_lr={text_encoder_lr}' else: run_cmd += f' --network_train_unet_only' if not float(unet_lr) == 0: run_cmd += f' --unet_lr={unet_lr}' else: run_cmd += f' --network_train_text_encoder_only' # if network_train == 'Text encoder only': # run_cmd += f' --network_train_text_encoder_only' # elif network_train == 'Unet only': # run_cmd += f' --network_train_unet_only' run_cmd += f' --network_dim={network_dim}' if not lora_network_weights == '': run_cmd += f' --network_weights="{lora_network_weights}"' if int(clip_skip) > 1: run_cmd += f' --clip_skip={str(clip_skip)}' if int(gradient_accumulation_steps) > 1: run_cmd += f' --gradient_accumulation_steps={int(gradient_accumulation_steps)}' # if not vae == '': # run_cmd += f' --vae="{vae}"' if not output_name == '': run_cmd += f' --output_name="{output_name}"' print(run_cmd) # Run the command 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 UI(username, password): 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 if not username == '': interface.launch(auth=(username, password)) else: interface.launch() 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...' ) with gr.Accordion('Configuration file', open=False): with gr.Row(): button_open_config = gr.Button('Open 📂', elem_id='open_folder') button_save_config = gr.Button('Save 💾', elem_id='open_folder') button_save_as_config = gr.Button( 'Save as... 💾', elem_id='open_folder' ) config_file_name = gr.Textbox( label='', placeholder="type the configuration file path or use the 'Open' button above to select it...", interactive=True, ) # config_file_name.change( # remove_doublequote, # inputs=[config_file_name], # outputs=[config_file_name], # ) with gr.Tab('Source model'): # Define the input elements with gr.Row(): pretrained_model_name_or_path = gr.Textbox( label='Pretrained model name or path', placeholder='enter the path to custom model or name of pretrained model', ) pretrained_model_name_or_path_file = gr.Button( document_symbol, elem_id='open_folder_small' ) pretrained_model_name_or_path_file.click( get_any_file_path, inputs=[pretrained_model_name_or_path], outputs=pretrained_model_name_or_path, ) pretrained_model_name_or_path_folder = gr.Button( folder_symbol, elem_id='open_folder_small' ) pretrained_model_name_or_path_folder.click( get_folder_path, outputs=pretrained_model_name_or_path, ) model_list = gr.Dropdown( label='(Optional) Model Quick Pick', choices=[ 'custom', 'stabilityai/stable-diffusion-2-1-base', 'stabilityai/stable-diffusion-2-base', 'stabilityai/stable-diffusion-2-1', 'stabilityai/stable-diffusion-2', 'runwayml/stable-diffusion-v1-5', 'CompVis/stable-diffusion-v1-4', ], ) save_model_as_dropdown = gr.Dropdown( label='Save trained model as', choices=[ 'same as source model', 'ckpt', 'diffusers', 'diffusers_safetensors', 'safetensors', ], value='same as source model', ) with gr.Row(): v2 = gr.Checkbox(label='v2', value=True) v_parameterization = gr.Checkbox( label='v_parameterization', value=False ) pretrained_model_name_or_path.change( remove_doublequote, inputs=[pretrained_model_name_or_path], outputs=[pretrained_model_name_or_path], ) model_list.change( set_pretrained_model_name_or_path_input, inputs=[model_list, v2, v_parameterization], outputs=[ pretrained_model_name_or_path, v2, v_parameterization, ], ) 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 ) 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 ) 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 ) 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 ) 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(): 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, ) with gr.Row(): lr_scheduler = gr.Dropdown( label='LR Scheduler', choices=[ 'constant', 'constant_with_warmup', 'cosine', 'cosine_with_restarts', 'linear', 'polynomial', ], value='cosine', ) lr_warmup = gr.Textbox(label='LR warmup (% of steps)', 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="1e-3", placeholder='Optional' ) network_dim = gr.Slider( minimum=1, maximum=128, label='Network Dimension', value=8, step=1, interactive=True, ) with gr.Row(): train_batch_size = gr.Slider( minimum=1, maximum=32, label='Train batch size', value=1, step=1, ) epoch = gr.Textbox(label='Epoch', value=1) save_every_n_epochs = gr.Textbox( label='Save every N epochs', value=1 ) with gr.Row(): mixed_precision = gr.Dropdown( label='Mixed precision', choices=[ 'no', 'fp16', 'bf16', ], value='fp16', ) save_precision = gr.Dropdown( label='Save precision', choices=[ 'float', 'fp16', 'bf16', ], value='fp16', ) num_cpu_threads_per_process = gr.Slider( minimum=1, maximum=os.cpu_count(), step=1, label='Number of CPU threads per process', value=os.cpu_count(), ) with gr.Row(): seed = gr.Textbox(label='Seed', value=1234) max_resolution = gr.Textbox( label='Max resolution', value='512,512', placeholder='512,512', ) with gr.Row(): caption_extention = gr.Textbox( label='Caption Extension', placeholder='(Optional) Extension for caption files. default: .caption', ) stop_text_encoder_training = gr.Slider( minimum=0, maximum=100, value=0, step=1, label='Stop text encoder training', ) with gr.Row(): enable_bucket = gr.Checkbox( label='Enable buckets', value=True ) cache_latent = gr.Checkbox(label='Cache latent', value=True) use_8bit_adam = gr.Checkbox( label='Use 8bit adam', value=True ) xformers = gr.Checkbox(label='Use xformers', value=True) with gr.Accordion('Advanced Configuration', open=False): with gr.Row(): full_fp16 = gr.Checkbox( label='Full fp16 training (experimental)', value=False ) no_token_padding = gr.Checkbox( label='No token padding', value=False ) gradient_checkpointing = gr.Checkbox( label='Gradient checkpointing', value=False ) gradient_accumulation_steps = gr.Number( label='Gradient accumulate steps', value='1' ) shuffle_caption = gr.Checkbox( label='Shuffle caption', value=False ) with gr.Row(): prior_loss_weight = gr.Number( label='Prior loss weight', value=1.0 ) color_aug = gr.Checkbox( label='Color augmentation', value=False ) flip_aug = gr.Checkbox(label='Flip augmentation', value=False) color_aug.change( color_aug_changed, inputs=[color_aug], outputs=[cache_latent], ) clip_skip = gr.Slider( label='Clip skip', value='1', minimum=1, maximum=12, step=1 ) mem_eff_attn = gr.Checkbox( label='Memory efficient attention', value=False ) with gr.Row(): save_state = gr.Checkbox( label='Save training state', value=False ) resume = gr.Textbox( label='Resume from saved training state', placeholder='path to "last-state" state folder to resume from', ) resume_button = gr.Button('📂', elem_id='open_folder_small') resume_button.click(get_folder_path, outputs=resume) # 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) 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() button_run = gr.Button('Train model') settings_list = [ pretrained_model_name_or_path, v2, v_parameterization, logging_dir, train_data_dir, reg_data_dir, output_dir, max_resolution, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latent, caption_extention, enable_bucket, gradient_checkpointing, full_fp16, no_token_padding, stop_text_encoder_training, use_8bit_adam, xformers, save_model_as_dropdown, 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, ] button_open_config.click( open_configuration, inputs=[config_file_name] + settings_list, outputs=[config_file_name] + settings_list, ) button_save_config.click( save_configuration, inputs=[dummy_db_false, config_file_name] + settings_list, outputs=[config_file_name], ) button_save_as_config.click( save_configuration, inputs=[dummy_db_true, config_file_name] + settings_list, outputs=[config_file_name], ) button_run.click( train_model, inputs=settings_list, ) return ( train_data_dir, reg_data_dir, output_dir, logging_dir, ) 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' ) args = parser.parse_args() UI(username=args.username, password=args.password)