import gradio as gr import json import math import os from datetime import datetime from .common_gui import ( get_file_path, get_any_file_path, get_saveasfile_path, color_aug_changed, run_cmd_advanced_training, update_my_data, check_if_model_exist, output_message, SaveConfigFile, save_to_file, scriptdir, validate_paths, ) from .class_configuration_file import ConfigurationFile from .class_source_model import SourceModel from .class_basic_training import BasicTraining from .class_advanced_training import AdvancedTraining from .class_sdxl_parameters import SDXLParameters from .class_folders import Folders from .class_command_executor import CommandExecutor from .tensorboard_gui import ( gradio_tensorboard, start_tensorboard, stop_tensorboard, ) from .class_sample_images import SampleImages, run_cmd_sample from .class_lora_tab import LoRATools from .dreambooth_folder_creation_gui import ( gradio_dreambooth_folder_creation_tab, ) from .dataset_balancing_gui import gradio_dataset_balancing_tab from .custom_logging import setup_logging # Set up logging log = setup_logging() # Setup command executor executor = CommandExecutor() button_run = gr.Button("Start training", variant="primary") button_stop_training = gr.Button("Stop training") document_symbol = "\U0001F4C4" # 📄 presets_dir = rf"{scriptdir}/presets" def update_network_args_with_kohya_lora_vars( network_args: str, kohya_lora_var_list: list, vars: dict ) -> str: """ Update network arguments with Kohya LoRA variables. Args: network_args (str): The network arguments. kohya_lora_var_list (list): The list of Kohya LoRA variables. vars (dict): The dictionary of variables. Returns: str: The updated network arguments. """ # Filter out variables that are in the Kohya LoRA variable list and have a value kohya_lora_vars = { key: value for key, value in vars if key in kohya_lora_var_list and value } # Iterate over the Kohya LoRA variables and append them to the network arguments for key, value in kohya_lora_vars.items(): # Append each variable as a key-value pair to the network_args network_args += f' {key}="{value}"' return network_args def save_configuration( save_as, 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, 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, fp8_base, full_fp16, # no_token_padding, stop_text_encoder_training, min_bucket_reso, max_bucket_reso, # 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, dim_from_weights, color_aug, flip_aug, clip_skip, num_processes, num_machines, multi_gpu, gpu_ids, gradient_accumulation_steps, mem_eff_attn, output_name, model_list, max_token_length, max_train_epochs, max_train_steps, 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, v_pred_like_loss, caption_dropout_every_n_epochs, caption_dropout_rate, optimizer, optimizer_args, lr_scheduler_args, max_grad_norm, noise_offset_type, noise_offset, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, LoRA_type, factor, bypass_mode, dora_wd, use_cp, use_tucker, use_scalar, rank_dropout_scale, constrain, rescaled, train_norm, decompose_both, train_on_input, conv_dim, conv_alpha, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, vae_batch_size, min_snr_gamma, down_lr_weight, mid_lr_weight, up_lr_weight, block_lr_zero_threshold, block_dims, block_alphas, conv_block_dims, conv_block_alphas, weighted_captions, unit, save_every_n_steps, save_last_n_steps, save_last_n_steps_state, use_wandb, wandb_api_key, wandb_run_name, log_tracker_name, log_tracker_config, scale_v_pred_loss_like_noise_pred, scale_weight_norms, network_dropout, rank_dropout, module_dropout, sdxl_cache_text_encoder_outputs, sdxl_no_half_vae, full_bf16, min_timestep, max_timestep, vae, LyCORIS_preset, debiased_estimation_loss, ): # Get list of function parameters and values parameters = list(locals().items()) original_file_path = file_path # Determine whether to save as a new file or overwrite the existing file save_as_bool = True if save_as.get("label") == "True" else False # If saving as a new file, get the file path for saving if save_as_bool: log.info("Save as...") file_path = get_saveasfile_path(file_path) # If not saving as a new file, check if a file path was provided else: log.info("Save...") # If no file path was provided, get the file path for saving if file_path == None or file_path == "": file_path = get_saveasfile_path(file_path) # Log the file path for debugging purposes log.debug(file_path) # If no file path was provided, return the original 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) # Save the configuration file SaveConfigFile( parameters=parameters, file_path=file_path, exclusion=["file_path", "save_as"], ) # Return the file path of the saved configuration return file_path def open_configuration( ask_for_file, apply_preset, 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, 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, fp8_base, full_fp16, # no_token_padding, stop_text_encoder_training, min_bucket_reso, max_bucket_reso, # 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, dim_from_weights, color_aug, flip_aug, clip_skip, num_processes, num_machines, multi_gpu, gpu_ids, gradient_accumulation_steps, mem_eff_attn, output_name, model_list, max_token_length, max_train_epochs, max_train_steps, 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, v_pred_like_loss, caption_dropout_every_n_epochs, caption_dropout_rate, optimizer, optimizer_args, lr_scheduler_args, max_grad_norm, noise_offset_type, noise_offset, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, LoRA_type, factor, bypass_mode, dora_wd, use_cp, use_tucker, use_scalar, rank_dropout_scale, constrain, rescaled, train_norm, decompose_both, train_on_input, conv_dim, conv_alpha, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, vae_batch_size, min_snr_gamma, down_lr_weight, mid_lr_weight, up_lr_weight, block_lr_zero_threshold, block_dims, block_alphas, conv_block_dims, conv_block_alphas, weighted_captions, unit, save_every_n_steps, save_last_n_steps, save_last_n_steps_state, use_wandb, wandb_api_key, wandb_run_name, log_tracker_name, log_tracker_config, scale_v_pred_loss_like_noise_pred, scale_weight_norms, network_dropout, rank_dropout, module_dropout, sdxl_cache_text_encoder_outputs, sdxl_no_half_vae, full_bf16, min_timestep, max_timestep, vae, LyCORIS_preset, debiased_estimation_loss, training_preset, ): # Get list of function parameters and values parameters = list(locals().items()) # Convert 'ask_for_file' and 'apply_preset' from string to boolean based on their 'label' value # This corrects a critical oversight in the original code, where `.get("label")` method calls were # made on boolean variables instead of dictionaries ask_for_file = True if ask_for_file.get("label") == "True" else False apply_preset = True if apply_preset.get("label") == "True" else False # Determines if a preset configuration is being applied if apply_preset: if training_preset != "none": log.info(f"Applying preset {training_preset}...") file_path = rf"{presets_dir}/lora/{training_preset}.json" else: # If not applying a preset, set the `training_preset` field to an empty string # Find the index of the `training_preset` parameter using the `index()` method training_preset_index = parameters.index(("training_preset", training_preset)) # Update the value of `training_preset` by directly assigning an empty string value parameters[training_preset_index] = ("training_preset", "none") # Store the original file path for potential reuse original_file_path = file_path # Request a file path from the user if required if ask_for_file: file_path = get_file_path(file_path) # Proceed if the file path is valid (not empty or None) 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) log.info("Loading config...") # Update values to fix deprecated options, set appropriate optimizer if it is set to True, etc. my_data = update_my_data(my_data) else: # Reset the file path to the original if the operation was cancelled or invalid file_path = original_file_path # In case a file_path was provided and the user decides to cancel the open action my_data = {} # Initialize an empty dict if no data was loaded values = [file_path] # Iterate over parameters to set their values from `my_data` or use default if not found for key, value in parameters: if not key in ["ask_for_file", "apply_preset", "file_path"]: json_value = my_data.get(key) # Append the value from JSON if present; otherwise, use the parameter's default value values.append(json_value if json_value is not None else value) # Display LoCon parameters based on the 'LoRA_type' from the loaded data # This section dynamically adjusts visibility of certain parameters in the UI if my_data.get("LoRA_type", "Standard") in { "LoCon", "Kohya DyLoRA", "Kohya LoCon", "LoRA-FA", "LyCORIS/Diag-OFT", "LyCORIS/DyLoRA", "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/LoCon", "LyCORIS/GLoRA", }: values.append(gr.Row(visible=True)) else: values.append(gr.Row(visible=False)) 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, 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, fp8_base, full_fp16, # no_token_padding, stop_text_encoder_training_pct, min_bucket_reso, max_bucket_reso, # 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, dim_from_weights, color_aug, flip_aug, clip_skip, num_processes, num_machines, multi_gpu, gpu_ids, 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_train_steps, 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, v_pred_like_loss, caption_dropout_every_n_epochs, caption_dropout_rate, optimizer, optimizer_args, lr_scheduler_args, max_grad_norm, noise_offset_type, noise_offset, adaptive_noise_scale, multires_noise_iterations, multires_noise_discount, LoRA_type, factor, bypass_mode, dora_wd, use_cp, use_tucker, use_scalar, rank_dropout_scale, constrain, rescaled, train_norm, decompose_both, train_on_input, conv_dim, conv_alpha, sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, additional_parameters, vae_batch_size, min_snr_gamma, down_lr_weight, mid_lr_weight, up_lr_weight, block_lr_zero_threshold, block_dims, block_alphas, conv_block_dims, conv_block_alphas, weighted_captions, unit, save_every_n_steps, save_last_n_steps, save_last_n_steps_state, use_wandb, wandb_api_key, wandb_run_name, log_tracker_name, log_tracker_config, scale_v_pred_loss_like_noise_pred, scale_weight_norms, network_dropout, rank_dropout, module_dropout, sdxl_cache_text_encoder_outputs, sdxl_no_half_vae, full_bf16, min_timestep, max_timestep, vae, LyCORIS_preset, debiased_estimation_loss, ): # Get list of function parameters and values parameters = list(locals().items()) global command_running print_only_bool = True if print_only.get("label") == "True" else False log.info(f"Start training LoRA {LoRA_type} ...") headless_bool = True if headless.get("label") == "True" else False if not validate_paths( output_dir=output_dir, pretrained_model_name_or_path=pretrained_model_name_or_path, train_data_dir=train_data_dir, reg_data_dir=reg_data_dir, headless=headless_bool, logging_dir=logging_dir, log_tracker_config=log_tracker_config, resume=resume, vae=vae, lora_network_weights=lora_network_weights, dataset_config=dataset_config, ): return if int(bucket_reso_steps) < 1: output_message( msg="Bucket resolution steps need to be greater than 0", headless=headless_bool, ) return if noise_offset == "": noise_offset = 0 if float(noise_offset) > 1 or float(noise_offset) < 0: output_message( msg="Noise offset need to be a value between 0 and 1", headless=headless_bool, ) return if output_dir != "": if not os.path.exists(output_dir): os.makedirs(output_dir) if stop_text_encoder_training_pct > 0: output_message( msg='Output "stop text encoder training" is not yet supported. Ignoring', headless=headless_bool, ) stop_text_encoder_training_pct = 0 if not print_only_bool and check_if_model_exist( output_name, output_dir, save_model_as, headless=headless_bool ): return # If string is empty set string to 0. if text_encoder_lr == "": text_encoder_lr = 0 if unet_lr == "": unet_lr = 0 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..." ) else: # 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]) # 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}: {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"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(f"Regulatization factor: {reg_factor}") if max_train_steps == "" or 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) ) ) log.info( f"max_train_steps ({total_steps} / {train_batch_size} / {gradient_accumulation_steps} * {epoch} * {reg_factor}) = {max_train_steps}" ) # calculate stop encoder training if stop_text_encoder_training_pct == None or ( not max_train_steps == "" or not max_train_steps == "0" ): stop_text_encoder_training = 0 else: stop_text_encoder_training = math.ceil( float(max_train_steps) / 100 * int(stop_text_encoder_training_pct) ) log.info(f"stop_text_encoder_training = {stop_text_encoder_training}") if not max_train_steps == "": lr_warmup_steps = round(float(int(lr_warmup) * int(max_train_steps) / 100)) else: lr_warmup_steps = 0 log.info(f"lr_warmup_steps = {lr_warmup_steps}") run_cmd = "accelerate launch" run_cmd += run_cmd_advanced_training( num_processes=num_processes, num_machines=num_machines, multi_gpu=multi_gpu, gpu_ids=gpu_ids, num_cpu_threads_per_process=num_cpu_threads_per_process, ) if sdxl: run_cmd += rf' "{scriptdir}/sd-scripts/sdxl_train_network.py"' else: run_cmd += rf' "{scriptdir}/sd-scripts/train_network.py"' network_args = "" if LoRA_type == "LyCORIS/BOFT": network_module = "lycoris.kohya" network_args = f' preset="{LyCORIS_preset}" conv_dim="{conv_dim}" conv_alpha="{conv_alpha}" module_dropout="{module_dropout}" use_tucker="{use_tucker}" use_scalar="{use_scalar}" rank_dropout="{rank_dropout}" rank_dropout_scale="{rank_dropout_scale}" constrain="{constrain}" rescaled="{rescaled}" algo="boft" train_norm="{train_norm}"' if LoRA_type == "LyCORIS/Diag-OFT": network_module = "lycoris.kohya" network_args = f' preset="{LyCORIS_preset}" conv_dim="{conv_dim}" conv_alpha="{conv_alpha}" module_dropout="{module_dropout}" use_tucker="{use_tucker}" use_scalar="{use_scalar}" rank_dropout="{rank_dropout}" rank_dropout_scale="{rank_dropout_scale}" constrain="{constrain}" rescaled="{rescaled}" algo="diag-oft" train_norm="{train_norm}"' if LoRA_type == "LyCORIS/DyLoRA": network_module = "lycoris.kohya" network_args = f' preset="{LyCORIS_preset}" conv_dim="{conv_dim}" conv_alpha="{conv_alpha}" use_tucker="{use_tucker}" block_size="{unit}" rank_dropout="{rank_dropout}" module_dropout="{module_dropout}" algo="dylora" train_norm="{train_norm}"' if LoRA_type == "LyCORIS/GLoRA": network_module = "lycoris.kohya" network_args = f' preset="{LyCORIS_preset}" conv_dim="{conv_dim}" conv_alpha="{conv_alpha}" rank_dropout="{rank_dropout}" module_dropout="{module_dropout}" rank_dropout_scale="{rank_dropout_scale}" algo="glora" train_norm="{train_norm}"' if LoRA_type == "LyCORIS/iA3": network_module = "lycoris.kohya" network_args = f' preset="{LyCORIS_preset}" conv_dim="{conv_dim}" conv_alpha="{conv_alpha}" train_on_input="{train_on_input}" algo="ia3"' if LoRA_type == "LoCon" or LoRA_type == "LyCORIS/LoCon": network_module = "lycoris.kohya" network_args = f' preset="{LyCORIS_preset}" conv_dim="{conv_dim}" conv_alpha="{conv_alpha}" rank_dropout="{rank_dropout}" bypass_mode="{bypass_mode}" dora_wd="{dora_wd}" module_dropout="{module_dropout}" use_tucker="{use_tucker}" use_scalar="{use_scalar}" rank_dropout_scale="{rank_dropout_scale}" algo="locon" train_norm="{train_norm}"' if LoRA_type == "LyCORIS/LoHa": network_module = "lycoris.kohya" network_args = f' preset="{LyCORIS_preset}" conv_dim="{conv_dim}" conv_alpha="{conv_alpha}" rank_dropout="{rank_dropout}" bypass_mode="{bypass_mode}" dora_wd="{dora_wd}" module_dropout="{module_dropout}" use_tucker="{use_tucker}" use_scalar="{use_scalar}" rank_dropout_scale="{rank_dropout_scale}" algo="loha" train_norm="{train_norm}"' if LoRA_type == "LyCORIS/LoKr": network_module = "lycoris.kohya" network_args = f' preset="{LyCORIS_preset}" conv_dim="{conv_dim}" conv_alpha="{conv_alpha}" rank_dropout="{rank_dropout}" bypass_mode="{bypass_mode}" dora_wd="{dora_wd}" module_dropout="{module_dropout}" factor="{factor}" use_cp="{use_cp}" use_scalar="{use_scalar}" decompose_both="{decompose_both}" rank_dropout_scale="{rank_dropout_scale}" algo="lokr" train_norm="{train_norm}"' if LoRA_type == "LyCORIS/Native Fine-Tuning": network_module = "lycoris.kohya" network_args = f' preset="{LyCORIS_preset}" rank_dropout="{rank_dropout}" module_dropout="{module_dropout}" use_tucker="{use_tucker}" use_scalar="{use_scalar}" rank_dropout_scale="{rank_dropout_scale}" algo="full" train_norm="{train_norm}"' if LoRA_type in ["Kohya LoCon", "Standard"]: kohya_lora_var_list = [ "down_lr_weight", "mid_lr_weight", "up_lr_weight", "block_lr_zero_threshold", "block_dims", "block_alphas", "conv_block_dims", "conv_block_alphas", "rank_dropout", "module_dropout", ] network_module = "networks.lora" network_args += update_network_args_with_kohya_lora_vars( network_args=network_args, kohya_lora_var_list=kohya_lora_var_list, vars=vars().items(), ) if LoRA_type in ["LoRA-FA"]: kohya_lora_var_list = [ "down_lr_weight", "mid_lr_weight", "up_lr_weight", "block_lr_zero_threshold", "block_dims", "block_alphas", "conv_block_dims", "conv_block_alphas", "rank_dropout", "module_dropout", ] network_module = "networks.lora_fa" network_args += update_network_args_with_kohya_lora_vars( network_args=network_args, kohya_lora_var_list=kohya_lora_var_list, vars=vars().items(), ) if LoRA_type in ["Kohya DyLoRA"]: kohya_lora_var_list = [ "conv_dim", "conv_alpha", "down_lr_weight", "mid_lr_weight", "up_lr_weight", "block_lr_zero_threshold", "block_dims", "block_alphas", "conv_block_dims", "conv_block_alphas", "rank_dropout", "module_dropout", "unit", ] network_module = "networks.dylora" network_args += update_network_args_with_kohya_lora_vars( network_args=network_args, kohya_lora_var_list=kohya_lora_var_list, vars=vars().items(), ) # Convert learning rates to float once and store the result for re-use learning_rate = float(learning_rate) if learning_rate is not None else 0.0 text_encoder_lr_float = float(text_encoder_lr) if text_encoder_lr is not None else 0.0 unet_lr_float = float(unet_lr) if unet_lr is not None else 0.0 # Determine the training configuration based on learning rate values # Sets flags for training specific components based on the provided learning rates. if float(learning_rate) == unet_lr_float == text_encoder_lr_float == 0: output_message( msg="Please input learning rate values.", headless=headless_bool ) return # Flag to train text encoder only if its learning rate is non-zero and unet's is zero. network_train_text_encoder_only = text_encoder_lr_float != 0 and unet_lr_float == 0 # Flag to train unet only if its learning rate is non-zero and text encoder's is zero. network_train_unet_only = text_encoder_lr_float == 0 and unet_lr_float != 0 # Define a dictionary of parameters run_cmd_params = { "adaptive_noise_scale": adaptive_noise_scale, "bucket_no_upscale": bucket_no_upscale, "bucket_reso_steps": bucket_reso_steps, "cache_latents": cache_latents, "cache_latents_to_disk": cache_latents_to_disk, "cache_text_encoder_outputs": ( True if sdxl and sdxl_cache_text_encoder_outputs else None ), "caption_dropout_every_n_epochs": caption_dropout_every_n_epochs, "caption_dropout_rate": caption_dropout_rate, "caption_extension": caption_extension, "clip_skip": clip_skip, "color_aug": color_aug, "dataset_config": dataset_config, "debiased_estimation_loss": debiased_estimation_loss, "dim_from_weights": dim_from_weights, "enable_bucket": enable_bucket, "epoch": epoch, "flip_aug": flip_aug, "fp8_base": fp8_base, "full_bf16": full_bf16, "full_fp16": full_fp16, "gradient_accumulation_steps": gradient_accumulation_steps, "gradient_checkpointing": gradient_checkpointing, "keep_tokens": keep_tokens, "learning_rate": learning_rate, "logging_dir": logging_dir, "log_tracker_name": log_tracker_name, "log_tracker_config": log_tracker_config, "lora_network_weights": lora_network_weights, "lr_scheduler": lr_scheduler, "lr_scheduler_args": lr_scheduler_args, "lr_scheduler_num_cycles": lr_scheduler_num_cycles, "lr_scheduler_power": lr_scheduler_power, "lr_warmup_steps": lr_warmup_steps, "max_bucket_reso": max_bucket_reso, "max_data_loader_n_workers": max_data_loader_n_workers, "max_grad_norm": max_grad_norm, "max_resolution": max_resolution, "max_timestep": max_timestep, "max_token_length": max_token_length, "max_train_epochs": max_train_epochs, "max_train_steps": max_train_steps, "mem_eff_attn": mem_eff_attn, "min_bucket_reso": min_bucket_reso, "min_snr_gamma": min_snr_gamma, "min_timestep": min_timestep, "mixed_precision": mixed_precision, "multires_noise_discount": multires_noise_discount, "multires_noise_iterations": multires_noise_iterations, "network_alpha": network_alpha, "network_args": network_args, "network_dim": network_dim, "network_dropout": network_dropout, "network_module": network_module, "network_train_unet_only": network_train_unet_only, "network_train_text_encoder_only": network_train_text_encoder_only, "no_half_vae": True if sdxl and sdxl_no_half_vae else None, "noise_offset": noise_offset, "noise_offset_type": noise_offset_type, "optimizer": optimizer, "optimizer_args": optimizer_args, "output_dir": output_dir, "output_name": output_name, "persistent_data_loader_workers": 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, "resume": resume, "save_every_n_epochs": save_every_n_epochs, "save_every_n_steps": save_every_n_steps, "save_last_n_steps": save_last_n_steps, "save_last_n_steps_state": save_last_n_steps_state, "save_model_as": save_model_as, "save_precision": save_precision, "save_state": save_state, "scale_v_pred_loss_like_noise_pred": scale_v_pred_loss_like_noise_pred, "scale_weight_norms": scale_weight_norms, "seed": seed, "shuffle_caption": shuffle_caption, "stop_text_encoder_training": stop_text_encoder_training, "text_encoder_lr": text_encoder_lr, "train_batch_size": train_batch_size, "train_data_dir": train_data_dir, "training_comment": training_comment, "unet_lr": unet_lr, "use_wandb": use_wandb, "v2": v2, "v_parameterization": v_parameterization, "v_pred_like_loss": v_pred_like_loss, "vae": vae, "vae_batch_size": vae_batch_size, "wandb_api_key": wandb_api_key, "wandb_run_name": wandb_run_name, "weighted_captions": weighted_captions, "xformers": xformers, "additional_parameters": additional_parameters, } # Use the ** syntax to unpack the dictionary when calling the function run_cmd += run_cmd_advanced_training(**run_cmd_params) run_cmd += run_cmd_sample( sample_every_n_steps, sample_every_n_epochs, sample_sampler, sample_prompts, output_dir, ) if print_only_bool: log.warning( "Here is the trainer command as a reference. It will not be executed:\n" ) print(run_cmd) save_to_file(run_cmd) 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 = os.environ.copy() env["PYTHONPATH"] = ( rf"{scriptdir}{os.pathsep}{scriptdir}/sd-scripts{os.pathsep}{env.get('PYTHONPATH', '')}" ) # Run the command executor.execute_command(run_cmd=run_cmd, env=env) def lora_tab( train_data_dir_input=gr.Dropdown(), reg_data_dir_input=gr.Dropdown(), output_dir_input=gr.Dropdown(), logging_dir_input=gr.Dropdown(), headless=False, config: dict = {}, ): dummy_db_true = gr.Label(value=True, visible=False) dummy_db_false = gr.Label(value=False, visible=False) dummy_headless = gr.Label(value=headless, visible=False) with gr.Tab("Training"), gr.Column(variant="compact") as tab: gr.Markdown( "Train a custom model using kohya train network LoRA python code..." ) with gr.Column(): source_model = SourceModel( save_model_as_choices=[ "ckpt", "safetensors", ], headless=headless, config=config, ) with gr.Accordion("Folders", open=False), gr.Group(): folders = Folders(headless=headless, config=config) with gr.Accordion("Parameters", open=False), gr.Column(): def list_presets(path): json_files = [] # Insert an empty string at the beginning json_files.insert(0, "none") for file in os.listdir(path): if file.endswith(".json"): json_files.append(os.path.splitext(file)[0]) user_presets_path = os.path.join(path, "user_presets") if os.path.isdir(user_presets_path): for file in os.listdir(user_presets_path): if file.endswith(".json"): preset_name = os.path.splitext(file)[0] json_files.append(os.path.join("user_presets", preset_name)) return json_files training_preset = gr.Dropdown( label="Presets", choices=[""] + list_presets(rf"{presets_dir}/lora"), elem_id="myDropdown", value="none", ) with gr.Group(elem_id="basic_tab"): with gr.Row(): LoRA_type = gr.Dropdown( label="LoRA type", choices=[ "Kohya DyLoRA", "Kohya LoCon", "LoRA-FA", "LyCORIS/iA3", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/DyLoRA", "LyCORIS/GLoRA", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/Native Fine-Tuning", "Standard", ], value="Standard", ) LyCORIS_preset = gr.Dropdown( label="LyCORIS Preset", choices=[ "attn-mlp", "attn-only", "full", "full-lin", "unet-transformer-only", "unet-convblock-only", ], value="full", visible=False, interactive=True, # info="https://github.com/KohakuBlueleaf/LyCORIS/blob/0006e2ffa05a48d8818112d9f70da74c0cd30b99/docs/Preset.md" ) with gr.Group(): with gr.Row(): lora_network_weights = gr.Textbox( label="LoRA network weights", placeholder="(Optional)", info="Path to an existing LoRA network weights to resume training from", ) lora_network_weights_file = gr.Button( document_symbol, elem_id="open_folder_small", elem_classes=["tool"], visible=(not headless), ) lora_network_weights_file.click( get_any_file_path, inputs=[lora_network_weights], outputs=lora_network_weights, show_progress=False, ) dim_from_weights = gr.Checkbox( label="DIM from weights", value=False, info="Automatically determine the dim(rank) from the weight file.", ) basic_training = BasicTraining( learning_rate_value="0.0001", lr_scheduler_value="cosine", lr_warmup_value="10", sdxl_checkbox=source_model.sdxl_checkbox, ) with gr.Row(): text_encoder_lr = gr.Number( label="Text Encoder learning rate", value="0.0001", info="(Optional)", minimum=0, maximum=1, ) unet_lr = gr.Number( label="Unet learning rate", value="0.0001", info="(Optional)", minimum=0, maximum=1, ) # Add SDXL Parameters sdxl_params = SDXLParameters(source_model.sdxl_checkbox) # LyCORIS Specific parameters with gr.Accordion("LyCORIS", visible=False) as lycoris_accordion: with gr.Row(): factor = gr.Slider( label="LoKr factor", value=-1, minimum=-1, maximum=64, step=1, visible=False, ) bypass_mode = gr.Checkbox( value=False, label="Bypass mode", info="Designed for bnb 8bit/4bit linear layer. (QLyCORIS)", visible=False, ) dora_wd = gr.Checkbox( value=False, label="DoRA Weight Decompose", info="Enable the DoRA method for these algorithms", visible=False, ) use_cp = gr.Checkbox( value=False, label="Use CP decomposition", info="A two-step approach utilizing tensor decomposition and fine-tuning to accelerate convolution layers in large neural networks, resulting in significant CPU speedups with minor accuracy drops.", visible=False, ) use_tucker = gr.Checkbox( value=False, label="Use Tucker decomposition", info="Efficiently decompose tensor shapes, resulting in a sequence of convolution layers with varying dimensions and Hadamard product implementation through multiplication of two distinct tensors.", visible=False, ) use_scalar = gr.Checkbox( value=False, label="Use Scalar", info="Train an additional scalar in front of the weight difference, use a different weight initialization strategy.", visible=False, ) with gr.Row(): rank_dropout_scale = gr.Checkbox( value=False, label="Rank Dropout Scale", info="Adjusts the scale of the rank dropout to maintain the average dropout rate, ensuring more consistent regularization across different layers.", visible=False, ) constrain = gr.Number( value="0.0", label="Constrain OFT", info="Limits the norm of the oft_blocks, ensuring that their magnitude does not exceed a specified threshold, thus controlling the extent of the transformation applied.", visible=False, ) rescaled = gr.Checkbox( value=False, label="Rescaled OFT", info="applies an additional scaling factor to the oft_blocks, allowing for further adjustment of their impact on the model's transformations.", visible=False, ) train_norm = gr.Checkbox( value=False, label="Train Norm", info="Selects trainable layers in a network, but trains normalization layers identically across methods as they lack matrix decomposition.", visible=False, ) decompose_both = gr.Checkbox( value=False, label="LoKr decompose both", info="Controls whether both input and output dimensions of the layer's weights are decomposed into smaller matrices for reparameterization.", visible=False, ) train_on_input = gr.Checkbox( value=True, label="iA3 train on input", info="Set if we change the information going into the system (True) or the information coming out of it (False).", visible=False, ) with gr.Row() as network_row: network_dim = gr.Slider( minimum=1, maximum=512, label="Network Rank (Dimension)", value=8, step=1, interactive=True, ) network_alpha = gr.Slider( minimum=0.00001, maximum=1024, label="Network Alpha", value=1, step=0.00001, interactive=True, info="alpha for LoRA weight scaling", ) with gr.Row(visible=False) as convolution_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=0, maximum=512, value=1, step=1, label="Convolution Rank (Dimension)", ) conv_alpha = gr.Slider( minimum=0, maximum=512, value=1, step=1, label="Convolution Alpha", ) with gr.Row(): scale_weight_norms = gr.Slider( label="Scale weight norms", value=0, minimum=0, maximum=10, step=0.01, info="Max Norm Regularization is a technique to stabilize network training by limiting the norm of network weights. It may be effective in suppressing overfitting of LoRA and improving stability when used with other LoRAs. See PR #545 on kohya_ss/sd_scripts repo for details. Recommended setting: 1. Higher is weaker, lower is stronger.", interactive=True, ) network_dropout = gr.Slider( label="Network dropout", value=0, minimum=0, maximum=1, step=0.01, info="Is a normal probability dropout at the neuron level. In the case of LoRA, it is applied to the output of down. Recommended range 0.1 to 0.5", ) rank_dropout = gr.Slider( label="Rank dropout", value=0, minimum=0, maximum=1, step=0.01, info="can specify `rank_dropout` to dropout each rank with specified probability. Recommended range 0.1 to 0.3", ) module_dropout = gr.Slider( label="Module dropout", value=0.0, minimum=0.0, maximum=1.0, step=0.01, info="can specify `module_dropout` to dropout each rank with specified probability. Recommended range 0.1 to 0.3", ) with gr.Row(visible=False): unit = gr.Slider( minimum=1, maximum=64, label="DyLoRA Unit / Block size", value=1, step=1, interactive=True, ) # Show or hide LoCon conv settings depending on LoRA type selection def update_LoRA_settings( LoRA_type, conv_dim, network_dim, ): log.info("LoRA type changed...") lora_settings_config = { "network_row": { "gr_type": gr.Row, "update_params": { "visible": LoRA_type in { "Kohya DyLoRA", "Kohya LoCon", "LoRA-FA", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/DyLoRA", "LyCORIS/GLoRA", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", "Standard", }, }, }, "convolution_row": { "gr_type": gr.Row, "update_params": { "visible": LoRA_type in { "LoCon", "Kohya DyLoRA", "Kohya LoCon", "LoRA-FA", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/DyLoRA", "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/LoCon", "LyCORIS/GLoRA", }, }, }, "kohya_advanced_lora": { "gr_type": gr.Row, "update_params": { "visible": LoRA_type in { "Standard", "Kohya DyLoRA", "Kohya LoCon", "LoRA-FA", }, }, }, "lora_network_weights": { "gr_type": gr.Textbox, "update_params": { "visible": LoRA_type in { "Standard", "LoCon", "Kohya DyLoRA", "Kohya LoCon", "LoRA-FA", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/DyLoRA", "LyCORIS/GLoRA", "LyCORIS/LoHa", "LyCORIS/LoCon", "LyCORIS/LoKr", }, }, }, "lora_network_weights_file": { "gr_type": gr.Button, "update_params": { "visible": LoRA_type in { "Standard", "LoCon", "Kohya DyLoRA", "Kohya LoCon", "LoRA-FA", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/DyLoRA", "LyCORIS/GLoRA", "LyCORIS/LoHa", "LyCORIS/LoCon", "LyCORIS/LoKr", }, }, }, "dim_from_weights": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in { "Standard", "LoCon", "Kohya DyLoRA", "Kohya LoCon", "LoRA-FA", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/DyLoRA", "LyCORIS/GLoRA", "LyCORIS/LoHa", "LyCORIS/LoCon", "LyCORIS/LoKr", } }, }, "factor": { "gr_type": gr.Slider, "update_params": { "visible": LoRA_type in { "LyCORIS/LoKr", }, }, }, "conv_dim": { "gr_type": gr.Slider, "update_params": { "maximum": ( 100000 if LoRA_type in { "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", } else 512 ), "value": conv_dim, # if conv_dim > 512 else conv_dim, }, }, "network_dim": { "gr_type": gr.Slider, "update_params": { "maximum": ( 100000 if LoRA_type in { "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", } else 512 ), "value": network_dim, # if network_dim > 512 else network_dim, }, }, "bypass_mode": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in { "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", }, }, }, "dora_wd": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in { "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", }, }, }, "use_cp": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in { "LyCORIS/LoKr", }, }, }, "use_tucker": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in { "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/DyLoRA", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/Native Fine-Tuning", }, }, }, "use_scalar": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in { "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/Native Fine-Tuning", }, }, }, "rank_dropout_scale": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in { "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/GLoRA", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/Native Fine-Tuning", }, }, }, "constrain": { "gr_type": gr.Number, "update_params": { "visible": LoRA_type in { "LyCORIS/BOFT", "LyCORIS/Diag-OFT", }, }, }, "rescaled": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in { "LyCORIS/BOFT", "LyCORIS/Diag-OFT", }, }, }, "train_norm": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in { "LyCORIS/DyLoRA", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/GLoRA", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/Native Fine-Tuning", }, }, }, "decompose_both": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in {"LyCORIS/LoKr"}, }, }, "train_on_input": { "gr_type": gr.Checkbox, "update_params": { "visible": LoRA_type in {"LyCORIS/iA3"}, }, }, "scale_weight_norms": { "gr_type": gr.Slider, "update_params": { "visible": LoRA_type in { "LoCon", "Kohya DyLoRA", "Kohya LoCon", "LoRA-FA", "LyCORIS/DyLoRA", "LyCORIS/GLoRA", "LyCORIS/LoHa", "LyCORIS/LoCon", "LyCORIS/LoKr", "Standard", }, }, }, "network_dropout": { "gr_type": gr.Slider, "update_params": { "visible": LoRA_type in { "LoCon", "Kohya DyLoRA", "Kohya LoCon", "LoRA-FA", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/DyLoRA", "LyCORIS/GLoRA", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/Native Fine-Tuning", "Standard", }, }, }, "rank_dropout": { "gr_type": gr.Slider, "update_params": { "visible": LoRA_type in { "LoCon", "Kohya DyLoRA", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/GLoRA", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKR", "Kohya LoCon", "LoRA-FA", "LyCORIS/Native Fine-Tuning", "Standard", }, }, }, "module_dropout": { "gr_type": gr.Slider, "update_params": { "visible": LoRA_type in { "LoCon", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "Kohya DyLoRA", "LyCORIS/GLoRA", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKR", "Kohya LoCon", "LyCORIS/Native Fine-Tuning", "LoRA-FA", "Standard", }, }, }, "LyCORIS_preset": { "gr_type": gr.Dropdown, "update_params": { "visible": LoRA_type in { "LyCORIS/DyLoRA", "LyCORIS/iA3", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/GLoRA", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/Native Fine-Tuning", }, }, }, "unit": { "gr_type": gr.Slider, "update_params": { "visible": LoRA_type in { "Kohya DyLoRA", "LyCORIS/DyLoRA", }, }, }, "lycoris_accordion": { "gr_type": gr.Accordion, "update_params": { "visible": LoRA_type in { "LyCORIS/DyLoRA", "LyCORIS/iA3", "LyCORIS/BOFT", "LyCORIS/Diag-OFT", "LyCORIS/GLoRA", "LyCORIS/LoCon", "LyCORIS/LoHa", "LyCORIS/LoKr", "LyCORIS/Native Fine-Tuning", }, }, }, } results = [] for attr, settings in lora_settings_config.items(): update_params = settings["update_params"] results.append(settings["gr_type"](**update_params)) return tuple(results) with gr.Accordion("Advanced", open=False, elem_id="advanced_tab"): # with gr.Accordion('Advanced Configuration', open=False): with gr.Row(visible=True) as kohya_advanced_lora: with gr.Tab(label="Weights"): with gr.Row(visible=True): down_lr_weight = gr.Textbox( label="Down LR weights", placeholder="(Optional) eg: 0,0,0,0,0,0,1,1,1,1,1,1", info="Specify the learning rate weight of the down blocks of U-Net.", ) mid_lr_weight = gr.Textbox( label="Mid LR weights", placeholder="(Optional) eg: 0.5", info="Specify the learning rate weight of the mid block of U-Net.", ) up_lr_weight = gr.Textbox( label="Up LR weights", placeholder="(Optional) eg: 0,0,0,0,0,0,1,1,1,1,1,1", info="Specify the learning rate weight of the up blocks of U-Net. The same as down_lr_weight.", ) block_lr_zero_threshold = gr.Textbox( label="Blocks LR zero threshold", placeholder="(Optional) eg: 0.1", info="If the weight is not more than this value, the LoRA module is not created. The default is 0.", ) with gr.Tab(label="Blocks"): with gr.Row(visible=True): block_dims = gr.Textbox( label="Block dims", placeholder="(Optional) eg: 2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2", info="Specify the dim (rank) of each block. Specify 25 numbers.", ) block_alphas = gr.Textbox( label="Block alphas", placeholder="(Optional) eg: 2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2", info="Specify the alpha of each block. Specify 25 numbers as with block_dims. If omitted, the value of network_alpha is used.", ) with gr.Tab(label="Conv"): with gr.Row(visible=True): conv_block_dims = gr.Textbox( label="Conv dims", placeholder="(Optional) eg: 2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2", info="Extend LoRA to Conv2d 3x3 and specify the dim (rank) of each block. Specify 25 numbers.", ) conv_block_alphas = gr.Textbox( label="Conv alphas", placeholder="(Optional) eg: 2,2,2,2,4,4,4,4,6,6,6,6,8,6,6,6,6,4,4,4,4,2,2,2,2", info="Specify the alpha of each block when expanding LoRA to Conv2d 3x3. Specify 25 numbers. If omitted, the value of conv_alpha is used.", ) advanced_training = AdvancedTraining( headless=headless, training_type="lora", 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() LoRA_type.change( update_LoRA_settings, inputs=[ LoRA_type, conv_dim, network_dim, ], outputs=[ network_row, convolution_row, kohya_advanced_lora, lora_network_weights, lora_network_weights_file, dim_from_weights, factor, conv_dim, network_dim, bypass_mode, dora_wd, use_cp, use_tucker, use_scalar, rank_dropout_scale, constrain, rescaled, train_norm, decompose_both, train_on_input, scale_weight_norms, network_dropout, rank_dropout, module_dropout, LyCORIS_preset, unit, lycoris_accordion, ], ) 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, ) gradio_dataset_balancing_tab(headless=headless) # Setup Configuration Files Gradio with gr.Accordion("Configuration", open=False): configuration = ConfigurationFile(headless=headless, config=config) with gr.Column(), gr.Group(): with gr.Row(): button_run = gr.Button("Start training", variant="primary") button_stop_training = gr.Button("Stop training") button_print = gr.Button("Print training command") # Setup gradio tensorboard buttons with gr.Column(), gr.Group(): ( button_start_tensorboard, button_stop_tensorboard, ) = gradio_tensorboard() button_start_tensorboard.click( start_tensorboard, inputs=[dummy_headless, folders.logging_dir], show_progress=False, ) button_stop_tensorboard.click( stop_tensorboard, show_progress=False, ) 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.lr_scheduler, basic_training.lr_warmup, basic_training.train_batch_size, basic_training.epoch, basic_training.save_every_n_epochs, basic_training.mixed_precision, source_model.save_precision, basic_training.seed, basic_training.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.fp8_base, advanced_training.full_fp16, # 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.resume, advanced_training.prior_loss_weight, text_encoder_lr, unet_lr, network_dim, lora_network_weights, dim_from_weights, advanced_training.color_aug, advanced_training.flip_aug, advanced_training.clip_skip, advanced_training.num_processes, advanced_training.num_machines, advanced_training.multi_gpu, advanced_training.gpu_ids, advanced_training.gradient_accumulation_steps, advanced_training.mem_eff_attn, source_model.output_name, source_model.model_list, advanced_training.max_token_length, basic_training.max_train_epochs, basic_training.max_train_steps, advanced_training.max_data_loader_n_workers, network_alpha, source_model.training_comment, 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, basic_training.max_grad_norm, advanced_training.noise_offset_type, advanced_training.noise_offset, advanced_training.adaptive_noise_scale, advanced_training.multires_noise_iterations, advanced_training.multires_noise_discount, LoRA_type, factor, bypass_mode, dora_wd, use_cp, use_tucker, use_scalar, rank_dropout_scale, constrain, rescaled, train_norm, decompose_both, train_on_input, conv_dim, conv_alpha, sample.sample_every_n_steps, sample.sample_every_n_epochs, sample.sample_sampler, sample.sample_prompts, advanced_training.additional_parameters, advanced_training.vae_batch_size, advanced_training.min_snr_gamma, down_lr_weight, mid_lr_weight, up_lr_weight, block_lr_zero_threshold, block_dims, block_alphas, conv_block_dims, conv_block_alphas, advanced_training.weighted_captions, unit, advanced_training.save_every_n_steps, advanced_training.save_last_n_steps, advanced_training.save_last_n_steps_state, advanced_training.use_wandb, 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, scale_weight_norms, network_dropout, rank_dropout, module_dropout, sdxl_params.sdxl_cache_text_encoder_outputs, sdxl_params.sdxl_no_half_vae, advanced_training.full_bf16, advanced_training.min_timestep, advanced_training.max_timestep, advanced_training.vae, LyCORIS_preset, advanced_training.debiased_estimation_loss, ] configuration.button_open_config.click( open_configuration, inputs=[dummy_db_true, dummy_db_false, configuration.config_file_name] + settings_list + [training_preset], outputs=[configuration.config_file_name] + settings_list + [training_preset, convolution_row], show_progress=False, ) configuration.button_load_config.click( open_configuration, inputs=[dummy_db_false, dummy_db_false, configuration.config_file_name] + settings_list + [training_preset], outputs=[configuration.config_file_name] + settings_list + [training_preset, convolution_row], show_progress=False, ) training_preset.input( open_configuration, inputs=[dummy_db_false, dummy_db_true, configuration.config_file_name] + settings_list + [training_preset], outputs=[gr.Textbox(visible=False)] + settings_list + [training_preset, convolution_row], 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, ) # config.button_save_as_config.click( # save_configuration, # inputs=[dummy_db_true, config.config_file_name] + settings_list, # outputs=[config.config_file_name], # show_progress=False, # ) button_run.click( train_model, inputs=[dummy_headless] + [dummy_db_false] + settings_list, show_progress=False, ) button_stop_training.click(executor.kill_command) button_print.click( train_model, inputs=[dummy_headless] + [dummy_db_true] + settings_list, show_progress=False, ) with gr.Tab("Tools"): lora_tools = LoRATools(headless=headless) with gr.Tab("Guides"): gr.Markdown("This section provide Various LoRA guides and information...") if os.path.exists(rf"{scriptdir}/docs/LoRA/top_level.md"): with open( os.path.join(rf"{scriptdir}/docs/LoRA/top_level.md"), "r", encoding="utf8", ) as file: guides_top_level = file.read() + "\n" gr.Markdown(guides_top_level) return ( source_model.train_data_dir, folders.reg_data_dir, folders.output_dir, folders.logging_dir, )