import gradio as gr import json import math import os import time import sys import toml from datetime import datetime from .common_gui import ( check_if_model_exist, color_aug_changed, get_executable_path, get_file_path, get_saveasfile_path, print_command_and_toml, run_cmd_advanced_training, SaveConfigFile, scriptdir, update_my_data, validate_file_path, validate_folder_path, validate_model_path, validate_args_setting, setup_environment, ) from .class_accelerate_launch import AccelerateLaunch from .class_configuration_file import ConfigurationFile from .class_gui_config import KohyaSSGUIConfig from .class_source_model import SourceModel from .class_basic_training import BasicTraining from .class_advanced_training import AdvancedTraining from .class_folders import Folders from .class_command_executor import CommandExecutor from .class_huggingface import HuggingFace from .class_metadata import MetaData from .dreambooth_folder_creation_gui import ( gradio_dreambooth_folder_creation_tab, ) from .dataset_balancing_gui import gradio_dataset_balancing_tab from .class_sample_images import SampleImages, create_prompt_file from .class_tensorboard import TensorboardManager from .custom_logging import setup_logging # Set up logging log = setup_logging() # Setup command executor executor = None # Setup huggingface huggingface = None use_shell = False train_state_value = time.time() def save_configuration( save_as_bool, file_path, pretrained_model_name_or_path, v2, v_parameterization, sdxl, logging_dir, train_data_dir, reg_data_dir, output_dir, dataset_config, max_resolution, learning_rate, learning_rate_te, learning_rate_te1, learning_rate_te2, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latents, cache_latents_to_disk, caption_extension, enable_bucket, gradient_checkpointing, full_fp16, full_bf16, no_token_padding, stop_text_encoder_training, min_bucket_reso, max_bucket_reso, # use_8bit_adam, xformers, save_model_as, shuffle_caption, save_state, save_state_on_train_end, resume, prior_loss_weight, color_aug, flip_aug, masked_loss, clip_skip, vae, dynamo_backend, dynamo_mode, dynamo_use_fullgraph, dynamo_use_dynamic, extra_accelerate_launch_args, num_processes, num_machines, multi_gpu, gpu_ids, main_process_port, output_name, max_token_length, max_train_epochs, max_train_steps, max_data_loader_n_workers, mem_eff_attn, gradient_accumulation_steps, model_list, keep_tokens, lr_scheduler_num_cycles, lr_scheduler_power, persistent_data_loader_workers, bucket_no_upscale, random_crop, bucket_reso_steps, v_pred_like_loss, caption_dropout_every_n_epochs, caption_dropout_rate, optimizer, optimizer_args, lr_scheduler_args, noise_offset_type, noise_offset, noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, ip_noise_gamma, ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, loss_type, huber_schedule, huber_c, vae_batch_size, min_snr_gamma, weighted_captions, save_every_n_steps, save_last_n_steps, save_last_n_steps_state, log_with, wandb_api_key, wandb_run_name, log_tracker_name, log_tracker_config, scale_v_pred_loss_like_noise_pred, min_timestep, max_timestep, debiased_estimation_loss, huggingface_repo_id, huggingface_token, huggingface_repo_type, huggingface_repo_visibility, huggingface_path_in_repo, save_state_to_huggingface, resume_from_huggingface, async_upload, metadata_author, metadata_description, metadata_license, metadata_tags, metadata_title, ): # Get list of function parameters and values parameters = list(locals().items()) original_file_path = file_path if save_as_bool: log.info("Save as...") file_path = get_saveasfile_path(file_path) else: log.info("Save...") if file_path == None or file_path == "": file_path = get_saveasfile_path(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 # 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) SaveConfigFile( parameters=parameters, file_path=file_path, exclusion=["file_path", "save_as"], ) return file_path def open_configuration( ask_for_file, file_path, pretrained_model_name_or_path, v2, v_parameterization, sdxl, logging_dir, train_data_dir, reg_data_dir, output_dir, dataset_config, max_resolution, learning_rate, learning_rate_te, learning_rate_te1, learning_rate_te2, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latents, cache_latents_to_disk, caption_extension, enable_bucket, gradient_checkpointing, full_fp16, full_bf16, no_token_padding, stop_text_encoder_training, min_bucket_reso, max_bucket_reso, # use_8bit_adam, xformers, save_model_as, shuffle_caption, save_state, save_state_on_train_end, resume, prior_loss_weight, color_aug, flip_aug, masked_loss, clip_skip, vae, dynamo_backend, dynamo_mode, dynamo_use_fullgraph, dynamo_use_dynamic, extra_accelerate_launch_args, num_processes, num_machines, multi_gpu, gpu_ids, main_process_port, output_name, max_token_length, max_train_epochs, max_train_steps, max_data_loader_n_workers, mem_eff_attn, gradient_accumulation_steps, model_list, keep_tokens, lr_scheduler_num_cycles, lr_scheduler_power, persistent_data_loader_workers, bucket_no_upscale, random_crop, bucket_reso_steps, v_pred_like_loss, caption_dropout_every_n_epochs, caption_dropout_rate, optimizer, optimizer_args, lr_scheduler_args, noise_offset_type, noise_offset, noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, ip_noise_gamma, ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, loss_type, huber_schedule, huber_c, vae_batch_size, min_snr_gamma, weighted_captions, save_every_n_steps, save_last_n_steps, save_last_n_steps_state, log_with, wandb_api_key, wandb_run_name, log_tracker_name, log_tracker_config, scale_v_pred_loss_like_noise_pred, min_timestep, max_timestep, debiased_estimation_loss, huggingface_repo_id, huggingface_token, huggingface_repo_type, huggingface_repo_visibility, huggingface_path_in_repo, save_state_to_huggingface, resume_from_huggingface, async_upload, metadata_author, metadata_description, metadata_license, metadata_tags, metadata_title, ): # Get list of function parameters and values parameters = list(locals().items()) 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", encoding="utf-8") as f: my_data = json.load(f) log.info("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( headless, print_only, pretrained_model_name_or_path, v2, v_parameterization, sdxl, logging_dir, train_data_dir, reg_data_dir, output_dir, dataset_config, max_resolution, learning_rate, learning_rate_te, learning_rate_te1, learning_rate_te2, lr_scheduler, lr_warmup, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, cache_latents, cache_latents_to_disk, caption_extension, enable_bucket, gradient_checkpointing, full_fp16, full_bf16, no_token_padding, stop_text_encoder_training, min_bucket_reso, max_bucket_reso, # use_8bit_adam, xformers, save_model_as, shuffle_caption, save_state, save_state_on_train_end, resume, prior_loss_weight, color_aug, flip_aug, masked_loss, clip_skip, vae, dynamo_backend, dynamo_mode, dynamo_use_fullgraph, dynamo_use_dynamic, extra_accelerate_launch_args, num_processes, num_machines, multi_gpu, gpu_ids, main_process_port, output_name, max_token_length, max_train_epochs, max_train_steps, 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 keep_tokens, lr_scheduler_num_cycles, lr_scheduler_power, persistent_data_loader_workers, bucket_no_upscale, random_crop, bucket_reso_steps, v_pred_like_loss, caption_dropout_every_n_epochs, caption_dropout_rate, optimizer, optimizer_args, lr_scheduler_args, noise_offset_type, noise_offset, noise_offset_random_strength, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, ip_noise_gamma, ip_noise_gamma_random_strength, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, loss_type, huber_schedule, huber_c, vae_batch_size, min_snr_gamma, weighted_captions, save_every_n_steps, save_last_n_steps, save_last_n_steps_state, log_with, wandb_api_key, wandb_run_name, log_tracker_name, log_tracker_config, scale_v_pred_loss_like_noise_pred, min_timestep, max_timestep, debiased_estimation_loss, huggingface_repo_id, huggingface_token, huggingface_repo_type, huggingface_repo_visibility, huggingface_path_in_repo, save_state_to_huggingface, resume_from_huggingface, async_upload, metadata_author, metadata_description, metadata_license, metadata_tags, metadata_title, ): # Get list of function parameters and values parameters = list(locals().items()) global train_state_value TRAIN_BUTTON_VISIBLE = [ gr.Button(visible=True), gr.Button(visible=False or headless), gr.Textbox(value=train_state_value), ] if executor.is_running(): log.error("Training is already running. Can't start another training session.") return TRAIN_BUTTON_VISIBLE log.info(f"Start training Dreambooth...") log.info(f"Validating lr scheduler arguments...") if not validate_args_setting(lr_scheduler_args): return log.info(f"Validating optimizer arguments...") if not validate_args_setting(optimizer_args): return TRAIN_BUTTON_VISIBLE # # Validate paths # if not validate_file_path(dataset_config): return TRAIN_BUTTON_VISIBLE if not validate_file_path(log_tracker_config): return TRAIN_BUTTON_VISIBLE if not validate_folder_path(logging_dir, can_be_written_to=True, create_if_not_exists=True): return TRAIN_BUTTON_VISIBLE if not validate_folder_path(output_dir, can_be_written_to=True, create_if_not_exists=True): return TRAIN_BUTTON_VISIBLE if not validate_model_path(pretrained_model_name_or_path): return TRAIN_BUTTON_VISIBLE if not validate_folder_path(reg_data_dir): return TRAIN_BUTTON_VISIBLE if not validate_file_path(resume): return TRAIN_BUTTON_VISIBLE if not validate_folder_path(train_data_dir): return TRAIN_BUTTON_VISIBLE if not validate_model_path(vae): return TRAIN_BUTTON_VISIBLE # # End of path validation # # This function validates files or folder paths. Simply add new variables containing file of folder path # to validate below # if not validate_paths( # dataset_config=dataset_config, # headless=headless, # log_tracker_config=log_tracker_config, # logging_dir=logging_dir, # output_dir=output_dir, # pretrained_model_name_or_path=pretrained_model_name_or_path, # reg_data_dir=reg_data_dir, # resume=resume, # train_data_dir=train_data_dir, # vae=vae, # ): # return TRAIN_BUTTON_VISIBLE if not print_only and check_if_model_exist( output_name, output_dir, save_model_as, headless=headless ): return TRAIN_BUTTON_VISIBLE if dataset_config: log.info( "Dataset config toml file used, skipping total_steps, train_batch_size, gradient_accumulation_steps, epoch, reg_factor, max_train_steps calculations..." ) if max_train_steps > 0: if lr_warmup != 0: lr_warmup_steps = round( float(int(lr_warmup) * int(max_train_steps) / 100) ) else: lr_warmup_steps = 0 else: lr_warmup_steps = 0 if max_train_steps == 0: max_train_steps_info = f"Max train steps: 0. sd-scripts will therefore default to 1600. Please specify a different value if required." else: max_train_steps_info = f"Max train steps: {max_train_steps}" else: if train_data_dir == "": log.error("Train data dir is empty") return TRAIN_BUTTON_VISIBLE # 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: try: # Extract the number of repeats from the folder name repeats = int(folder.split("_")[0]) log.info(f"Folder {folder}: {repeats} repeats found") # 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")) ] ) log.info(f"Folder {folder}: {num_images} images found") # Calculate the total number of steps for this folder steps = repeats * num_images # log.info the result log.info(f"Folder {folder}: {num_images} * {repeats} = {steps} steps") total_steps += steps except ValueError: # Handle the case where the folder name does not contain an underscore log.info( f"Error: '{folder}' does not contain an underscore, skipping..." ) if reg_data_dir == "": reg_factor = 1 else: log.warning( "Regularisation images are used... Will double the number of steps required..." ) reg_factor = 2 log.info(f"Regulatization factor: {reg_factor}") if max_train_steps == 0: # calculate max_train_steps max_train_steps = int( math.ceil( float(total_steps) / int(train_batch_size) / int(gradient_accumulation_steps) * int(epoch) * int(reg_factor) ) ) max_train_steps_info = f"max_train_steps ({total_steps} / {train_batch_size} / {gradient_accumulation_steps} * {epoch} * {reg_factor}) = {max_train_steps}" else: if max_train_steps == 0: max_train_steps_info = f"Max train steps: 0. sd-scripts will therefore default to 1600. Please specify a different value if required." else: max_train_steps_info = f"Max train steps: {max_train_steps}" if lr_warmup != 0: lr_warmup_steps = round(float(int(lr_warmup) * int(max_train_steps) / 100)) else: lr_warmup_steps = 0 log.info(f"Total steps: {total_steps}") log.info(f"Train batch size: {train_batch_size}") log.info(f"Gradient accumulation steps: {gradient_accumulation_steps}") log.info(f"Epoch: {epoch}") log.info(max_train_steps_info) log.info(f"lr_warmup_steps = {lr_warmup_steps}") accelerate_path = get_executable_path("accelerate") if accelerate_path == "": log.error("accelerate not found") return TRAIN_BUTTON_VISIBLE run_cmd = [rf'{accelerate_path}', "launch"] run_cmd = AccelerateLaunch.run_cmd( run_cmd=run_cmd, dynamo_backend=dynamo_backend, dynamo_mode=dynamo_mode, dynamo_use_fullgraph=dynamo_use_fullgraph, dynamo_use_dynamic=dynamo_use_dynamic, num_processes=num_processes, num_machines=num_machines, multi_gpu=multi_gpu, gpu_ids=gpu_ids, main_process_port=main_process_port, num_cpu_threads_per_process=num_cpu_threads_per_process, mixed_precision=mixed_precision, extra_accelerate_launch_args=extra_accelerate_launch_args, ) if sdxl: run_cmd.append(rf'{scriptdir}/sd-scripts/sdxl_train.py') else: run_cmd.append(rf"{scriptdir}/sd-scripts/train_db.py") if max_data_loader_n_workers == "" or None: max_data_loader_n_workers = 0 else: max_data_loader_n_workers = int(max_data_loader_n_workers) if max_train_steps == "" or None: max_train_steps = 0 else: max_train_steps = int(max_train_steps) # def save_huggingface_to_toml(self, toml_file_path: str): config_toml_data = { # Update the values in the TOML data "adaptive_noise_scale": adaptive_noise_scale if not 0 else None, "async_upload": async_upload, "bucket_no_upscale": bucket_no_upscale, "bucket_reso_steps": bucket_reso_steps, "cache_latents": cache_latents, "cache_latents_to_disk": cache_latents_to_disk, "caption_dropout_every_n_epochs": int(caption_dropout_every_n_epochs), "caption_dropout_rate": caption_dropout_rate, "caption_extension": caption_extension, "clip_skip": clip_skip if clip_skip != 0 else None, "color_aug": color_aug, "dataset_config": dataset_config, "debiased_estimation_loss": debiased_estimation_loss, "dynamo_backend": dynamo_backend, "enable_bucket": enable_bucket, "epoch": int(epoch), "flip_aug": flip_aug, "full_bf16": full_bf16, "full_fp16": full_fp16, "gradient_accumulation_steps": int(gradient_accumulation_steps), "gradient_checkpointing": gradient_checkpointing, "huber_c": huber_c, "huber_schedule": huber_schedule, "huggingface_path_in_repo": huggingface_path_in_repo, "huggingface_repo_id": huggingface_repo_id, "huggingface_repo_type": huggingface_repo_type, "huggingface_repo_visibility": huggingface_repo_visibility, "huggingface_token": huggingface_token, "ip_noise_gamma": ip_noise_gamma if ip_noise_gamma != 0 else None, "ip_noise_gamma_random_strength": ip_noise_gamma_random_strength, "keep_tokens": int(keep_tokens), "learning_rate": learning_rate, # both for sd1.5 and sdxl "learning_rate_te": ( learning_rate_te if not sdxl and not 0 else None ), # only for sd1.5 and not 0 "learning_rate_te1": ( learning_rate_te1 if sdxl and not 0 else None ), # only for sdxl and not 0 "learning_rate_te2": ( learning_rate_te2 if sdxl and not 0 else None ), # only for sdxl and not 0 "logging_dir": logging_dir, "log_tracker_config": log_tracker_config, "log_tracker_name": log_tracker_name, "log_with": log_with, "loss_type": loss_type, "lr_scheduler": lr_scheduler, "lr_scheduler_args": str(lr_scheduler_args).replace('"', "").split(), "lr_scheduler_num_cycles": ( int(lr_scheduler_num_cycles) if lr_scheduler_num_cycles != "" else int(epoch) ), "lr_scheduler_power": lr_scheduler_power, "lr_warmup_steps": lr_warmup_steps, "masked_loss": masked_loss, "max_bucket_reso": max_bucket_reso, "max_timestep": max_timestep if max_timestep != 0 else None, "max_token_length": int(max_token_length), "max_train_epochs": int(max_train_epochs) if int(max_train_epochs) != 0 else None, "max_train_steps": int(max_train_steps) if int(max_train_steps) != 0 else None, "mem_eff_attn": mem_eff_attn, "metadata_author": metadata_author, "metadata_description": metadata_description, "metadata_license": metadata_license, "metadata_tags": metadata_tags, "metadata_title": metadata_title, "min_bucket_reso": int(min_bucket_reso), "min_snr_gamma": min_snr_gamma if min_snr_gamma != 0 else None, "min_timestep": min_timestep if min_timestep != 0 else None, "mixed_precision": mixed_precision, "multires_noise_discount": multires_noise_discount, "multires_noise_iterations": multires_noise_iterations if not 0 else None, "no_token_padding": no_token_padding, "noise_offset": noise_offset if not 0 else None, "noise_offset_random_strength": noise_offset_random_strength, "noise_offset_type": noise_offset_type, "optimizer_args": ( str(optimizer_args).replace('"', "").split() if optimizer_args != "" else None ), "optimizer_type": optimizer, "output_dir": output_dir, "output_name": output_name, "persistent_data_loader_workers": int(persistent_data_loader_workers), "pretrained_model_name_or_path": pretrained_model_name_or_path, "prior_loss_weight": prior_loss_weight, "random_crop": random_crop, "reg_data_dir": reg_data_dir, "resolution": max_resolution, "resume": resume, "resume_from_huggingface": resume_from_huggingface, "sample_every_n_epochs": ( sample_every_n_epochs if sample_every_n_epochs != 0 else None ), "sample_every_n_steps": ( sample_every_n_steps if sample_every_n_steps != 0 else None ), "sample_prompts": create_prompt_file(sample_prompts, output_dir), "sample_sampler": sample_sampler, "save_every_n_epochs": ( save_every_n_epochs if save_every_n_epochs != 0 else None ), "save_every_n_steps": save_every_n_steps if save_every_n_steps != 0 else None, "save_last_n_steps": save_last_n_steps if save_last_n_steps != 0 else None, "save_last_n_steps_state": ( save_last_n_steps_state if save_last_n_steps_state != 0 else None ), "save_model_as": save_model_as, "save_precision": save_precision, "save_state": save_state, "save_state_on_train_end": save_state_on_train_end, "save_state_to_huggingface": save_state_to_huggingface, "scale_v_pred_loss_like_noise_pred": scale_v_pred_loss_like_noise_pred, "sdpa": True if xformers == "sdpa" else None, "seed": int(seed) if int(seed) != 0 else None, "shuffle_caption": shuffle_caption, "stop_text_encoder_training": ( stop_text_encoder_training if stop_text_encoder_training != 0 else None ), "train_batch_size": train_batch_size, "train_data_dir": train_data_dir, "v2": v2, "v_parameterization": v_parameterization, "v_pred_like_loss": v_pred_like_loss if v_pred_like_loss != 0 else None, "vae": vae, "vae_batch_size": vae_batch_size if vae_batch_size != 0 else None, "wandb_api_key": wandb_api_key, "wandb_run_name": wandb_run_name, "weighted_captions": weighted_captions, "xformers": True if xformers == "xformers" else None, } # Given dictionary `config_toml_data` # Remove all values = "" and values = False config_toml_data = { key: value for key, value in config_toml_data.items() if value not in ["", False, None] } config_toml_data["max_data_loader_n_workers"] = int(max_data_loader_n_workers) # Sort the dictionary by keys config_toml_data = dict(sorted(config_toml_data.items())) current_datetime = datetime.now() formatted_datetime = current_datetime.strftime("%Y%m%d-%H%M%S") tmpfilename = fr"{output_dir}/config_dreambooth-{formatted_datetime}.toml" # Save the updated TOML data back to the file with open(tmpfilename, "w", encoding="utf-8") as toml_file: toml.dump(config_toml_data, toml_file) if not os.path.exists(toml_file.name): log.error(f"Failed to write TOML file: {toml_file.name}") run_cmd.append(f"--config_file") run_cmd.append(rf'{tmpfilename}') # Initialize a dictionary with always-included keyword arguments kwargs_for_training = { "additional_parameters": additional_parameters, } # Pass the dynamically constructed keyword arguments to the function run_cmd = run_cmd_advanced_training(run_cmd=run_cmd, **kwargs_for_training) if print_only: print_command_and_toml(run_cmd, tmpfilename) else: # Saving config file for model current_datetime = datetime.now() formatted_datetime = current_datetime.strftime("%Y%m%d-%H%M%S") # config_dir = os.path.dirname(os.path.dirname(train_data_dir)) file_path = os.path.join(output_dir, f"{output_name}_{formatted_datetime}.json") log.info(f"Saving training config to {file_path}...") SaveConfigFile( parameters=parameters, file_path=file_path, exclusion=["file_path", "save_as", "headless", "print_only"], ) # log.info(run_cmd) env = setup_environment() # Run the command executor.execute_command(run_cmd=run_cmd, env=env) train_state_value = time.time() return ( gr.Button(visible=False or headless), gr.Button(visible=True), gr.Textbox(value=train_state_value), ) def dreambooth_tab( # train_data_dir=gr.Textbox(), # reg_data_dir=gr.Textbox(), # output_dir=gr.Textbox(), # logging_dir=gr.Textbox(), headless=False, config: KohyaSSGUIConfig = {}, use_shell_flag: bool = False, ): dummy_db_true = gr.Checkbox(value=True, visible=False) dummy_db_false = gr.Checkbox(value=False, visible=False) dummy_headless = gr.Checkbox(value=headless, visible=False) global use_shell use_shell = use_shell_flag with gr.Tab("Training"), gr.Column(variant="compact"): gr.Markdown("Train a custom model using kohya dreambooth python code...") # Setup Configuration Files Gradio with gr.Accordion("Configuration", open=False): configuration = ConfigurationFile(headless=headless, config=config) with gr.Accordion("Accelerate launch", open=False), gr.Column(): accelerate_launch = AccelerateLaunch(config=config) with gr.Column(): source_model = SourceModel(headless=headless, config=config) with gr.Accordion("Folders", open=False), gr.Group(): folders = Folders(headless=headless, config=config) with gr.Accordion("Metadata", open=False), gr.Group(): metadata = MetaData(config=config) with gr.Accordion("Dataset Preparation", open=False): gr.Markdown( "This section provide Dreambooth tools to help setup your dataset..." ) gradio_dreambooth_folder_creation_tab( train_data_dir_input=source_model.train_data_dir, reg_data_dir_input=folders.reg_data_dir, output_dir_input=folders.output_dir, logging_dir_input=folders.logging_dir, headless=headless, config=config, ) gradio_dataset_balancing_tab(headless=headless) with gr.Accordion("Parameters", open=False), gr.Column(): with gr.Accordion("Basic", open="True"): with gr.Group(elem_id="basic_tab"): basic_training = BasicTraining( learning_rate_value=1e-5, lr_scheduler_value="cosine", lr_warmup_value=10, dreambooth=True, sdxl_checkbox=source_model.sdxl_checkbox, config=config, ) with gr.Accordion("Advanced", open=False, elem_id="advanced_tab"): advanced_training = AdvancedTraining(headless=headless, config=config) advanced_training.color_aug.change( color_aug_changed, inputs=[advanced_training.color_aug], outputs=[basic_training.cache_latents], ) with gr.Accordion("Samples", open=False, elem_id="samples_tab"): sample = SampleImages(config=config) global huggingface with gr.Accordion("HuggingFace", open=False): huggingface = HuggingFace(config=config) global executor executor = CommandExecutor(headless=headless) with gr.Column(), gr.Group(): with gr.Row(): button_print = gr.Button("Print training command") # Setup gradio tensorboard buttons TensorboardManager(headless=headless, logging_dir=folders.logging_dir) settings_list = [ source_model.pretrained_model_name_or_path, source_model.v2, source_model.v_parameterization, source_model.sdxl_checkbox, folders.logging_dir, source_model.train_data_dir, folders.reg_data_dir, folders.output_dir, source_model.dataset_config, basic_training.max_resolution, basic_training.learning_rate, basic_training.learning_rate_te, basic_training.learning_rate_te1, basic_training.learning_rate_te2, basic_training.lr_scheduler, basic_training.lr_warmup, basic_training.train_batch_size, basic_training.epoch, basic_training.save_every_n_epochs, accelerate_launch.mixed_precision, source_model.save_precision, basic_training.seed, accelerate_launch.num_cpu_threads_per_process, basic_training.cache_latents, basic_training.cache_latents_to_disk, basic_training.caption_extension, basic_training.enable_bucket, advanced_training.gradient_checkpointing, advanced_training.full_fp16, advanced_training.full_bf16, advanced_training.no_token_padding, basic_training.stop_text_encoder_training, basic_training.min_bucket_reso, basic_training.max_bucket_reso, advanced_training.xformers, source_model.save_model_as, advanced_training.shuffle_caption, advanced_training.save_state, advanced_training.save_state_on_train_end, advanced_training.resume, advanced_training.prior_loss_weight, advanced_training.color_aug, advanced_training.flip_aug, advanced_training.masked_loss, advanced_training.clip_skip, advanced_training.vae, accelerate_launch.dynamo_backend, accelerate_launch.dynamo_mode, accelerate_launch.dynamo_use_fullgraph, accelerate_launch.dynamo_use_dynamic, accelerate_launch.extra_accelerate_launch_args, accelerate_launch.num_processes, accelerate_launch.num_machines, accelerate_launch.multi_gpu, accelerate_launch.gpu_ids, accelerate_launch.main_process_port, source_model.output_name, advanced_training.max_token_length, basic_training.max_train_epochs, basic_training.max_train_steps, advanced_training.max_data_loader_n_workers, advanced_training.mem_eff_attn, advanced_training.gradient_accumulation_steps, source_model.model_list, advanced_training.keep_tokens, basic_training.lr_scheduler_num_cycles, basic_training.lr_scheduler_power, advanced_training.persistent_data_loader_workers, advanced_training.bucket_no_upscale, advanced_training.random_crop, advanced_training.bucket_reso_steps, advanced_training.v_pred_like_loss, advanced_training.caption_dropout_every_n_epochs, advanced_training.caption_dropout_rate, basic_training.optimizer, basic_training.optimizer_args, basic_training.lr_scheduler_args, advanced_training.noise_offset_type, advanced_training.noise_offset, advanced_training.noise_offset_random_strength, advanced_training.adaptive_noise_scale, advanced_training.multires_noise_iterations, advanced_training.multires_noise_discount, advanced_training.ip_noise_gamma, advanced_training.ip_noise_gamma_random_strength, sample.sample_every_n_steps, sample.sample_every_n_epochs, sample.sample_sampler, sample.sample_prompts, advanced_training.additional_parameters, advanced_training.loss_type, advanced_training.huber_schedule, advanced_training.huber_c, advanced_training.vae_batch_size, advanced_training.min_snr_gamma, advanced_training.weighted_captions, advanced_training.save_every_n_steps, advanced_training.save_last_n_steps, advanced_training.save_last_n_steps_state, advanced_training.log_with, advanced_training.wandb_api_key, advanced_training.wandb_run_name, advanced_training.log_tracker_name, advanced_training.log_tracker_config, advanced_training.scale_v_pred_loss_like_noise_pred, advanced_training.min_timestep, advanced_training.max_timestep, advanced_training.debiased_estimation_loss, huggingface.huggingface_repo_id, huggingface.huggingface_token, huggingface.huggingface_repo_type, huggingface.huggingface_repo_visibility, huggingface.huggingface_path_in_repo, huggingface.save_state_to_huggingface, huggingface.resume_from_huggingface, huggingface.async_upload, metadata.metadata_author, metadata.metadata_description, metadata.metadata_license, metadata.metadata_tags, metadata.metadata_title, ] configuration.button_open_config.click( open_configuration, inputs=[dummy_db_true, configuration.config_file_name] + settings_list, outputs=[configuration.config_file_name] + settings_list, show_progress=False, ) configuration.button_load_config.click( open_configuration, inputs=[dummy_db_false, configuration.config_file_name] + settings_list, outputs=[configuration.config_file_name] + settings_list, show_progress=False, ) configuration.button_save_config.click( save_configuration, inputs=[dummy_db_false, configuration.config_file_name] + settings_list, outputs=[configuration.config_file_name], show_progress=False, ) run_state = gr.Textbox(value=train_state_value, visible=False) run_state.change( fn=executor.wait_for_training_to_end, outputs=[executor.button_run, executor.button_stop_training], ) executor.button_run.click( train_model, inputs=[dummy_headless] + [dummy_db_false] + settings_list, outputs=[executor.button_run, executor.button_stop_training, run_state], show_progress=False, ) executor.button_stop_training.click( executor.kill_command, outputs=[executor.button_run, executor.button_stop_training], ) button_print.click( train_model, inputs=[dummy_headless] + [dummy_db_true] + settings_list, show_progress=False, ) return ( source_model.train_data_dir, folders.reg_data_dir, folders.output_dir, folders.logging_dir, )