import gradio as gr import json import math import os import subprocess 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_source_model import SourceModel from .class_basic_training import BasicTraining from .class_advanced_training import AdvancedTraining from .class_folders import Folders from .class_sdxl_parameters import SDXLParameters from .class_command_executor import CommandExecutor from .class_tensorboard import TensorboardManager from .class_sample_images import SampleImages, create_prompt_file from .class_huggingface import HuggingFace from .class_metadata import MetaData from .class_gui_config import KohyaSSGUIConfig 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() folder_symbol = "\U0001f4c2" # 📂 refresh_symbol = "\U0001f504" # 🔄 save_style_symbol = "\U0001f4be" # 💾 document_symbol = "\U0001F4C4" # 📄 PYTHON = sys.executable presets_dir = rf"{scriptdir}/presets" def save_configuration( save_as_bool, file_path, pretrained_model_name_or_path, v2, v_parameterization, sdxl_checkbox, train_dir, image_folder, output_dir, dataset_config, logging_dir, max_resolution, min_bucket_reso, max_bucket_reso, batch_size, flip_aug, masked_loss, caption_metadata_filename, latent_metadata_filename, full_path, learning_rate, lr_scheduler, lr_warmup, dataset_repeats, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, learning_rate_te, learning_rate_te1, learning_rate_te2, train_text_encoder, full_bf16, create_caption, create_buckets, save_model_as, caption_extension, # use_8bit_adam, xformers, clip_skip, 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, save_state, save_state_on_train_end, resume, gradient_checkpointing, gradient_accumulation_steps, block_lr, mem_eff_attn, shuffle_caption, output_name, max_token_length, max_train_epochs, max_train_steps, max_data_loader_n_workers, full_fp16, color_aug, model_list, cache_latents, cache_latents_to_disk, use_latent_files, keep_tokens, 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, sdxl_cache_text_encoder_outputs, sdxl_no_half_vae, 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) # log.info(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, apply_preset, file_path, pretrained_model_name_or_path, v2, v_parameterization, sdxl_checkbox, train_dir, image_folder, output_dir, dataset_config, logging_dir, max_resolution, min_bucket_reso, max_bucket_reso, batch_size, flip_aug, masked_loss, caption_metadata_filename, latent_metadata_filename, full_path, learning_rate, lr_scheduler, lr_warmup, dataset_repeats, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, learning_rate_te, learning_rate_te1, learning_rate_te2, train_text_encoder, full_bf16, create_caption, create_buckets, save_model_as, caption_extension, # use_8bit_adam, xformers, clip_skip, 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, save_state, save_state_on_train_end, resume, gradient_checkpointing, gradient_accumulation_steps, block_lr, mem_eff_attn, shuffle_caption, output_name, max_token_length, max_train_epochs, max_train_steps, max_data_loader_n_workers, full_fp16, color_aug, model_list, cache_latents, cache_latents_to_disk, use_latent_files, keep_tokens, 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, sdxl_cache_text_encoder_outputs, sdxl_no_half_vae, 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, training_preset, ): # Get list of function parameters and values parameters = list(locals().items()) # Check if we are "applying" a preset or a config if apply_preset: log.info(f"Applying preset {training_preset}...") file_path = rf"{presets_dir}/finetune/{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", "") 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: json_value = my_data.get(key) # 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", "apply_preset", "file_path"]: values.append(json_value if json_value is not None else value) return tuple(values) def train_model( headless, print_only, pretrained_model_name_or_path, v2, v_parameterization, sdxl_checkbox, train_dir, image_folder, output_dir, dataset_config, logging_dir, max_resolution, min_bucket_reso, max_bucket_reso, batch_size, flip_aug, masked_loss, caption_metadata_filename, latent_metadata_filename, full_path, learning_rate, lr_scheduler, lr_warmup, dataset_repeats, train_batch_size, epoch, save_every_n_epochs, mixed_precision, save_precision, seed, num_cpu_threads_per_process, learning_rate_te, learning_rate_te1, learning_rate_te2, train_text_encoder, full_bf16, generate_caption_database, generate_image_buckets, save_model_as, caption_extension, # use_8bit_adam, xformers, clip_skip, 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, save_state, save_state_on_train_end, resume, gradient_checkpointing, gradient_accumulation_steps, block_lr, mem_eff_attn, shuffle_caption, output_name, max_token_length, max_train_epochs, max_train_steps, max_data_loader_n_workers, full_fp16, color_aug, model_list, # Keep this. Yes, it is unused here but required given the common list used cache_latents, cache_latents_to_disk, use_latent_files, keep_tokens, 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, sdxl_cache_text_encoder_outputs, sdxl_no_half_vae, 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.debug(f"headless = {headless} ; print_only = {print_only}") log.info(f"Start Finetuning...") 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 if train_dir != "" and not os.path.exists(train_dir): os.mkdir(train_dir) # # Validate paths # if not validate_file_path(dataset_config): return TRAIN_BUTTON_VISIBLE if not validate_folder_path(image_folder): 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(resume): return TRAIN_BUTTON_VISIBLE # # End of path validation # # if not validate_paths( # dataset_config=dataset_config, # finetune_image_folder=image_folder, # 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, # resume=resume, # ): # return TRAIN_BUTTON_VISIBLE if not print_only and check_if_model_exist( output_name, output_dir, save_model_as, headless ): return TRAIN_BUTTON_VISIBLE if dataset_config: log.info( "Dataset config toml file used, skipping caption json file, image buckets, total_steps, train_batch_size, gradient_accumulation_steps, epoch, reg_factor, max_train_steps creation..." ) 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: # create caption json file if generate_caption_database: # Define the command components run_cmd = [ PYTHON, rf"{scriptdir}/sd-scripts/finetune/merge_captions_to_metadata.py", ] # Add the caption extension run_cmd.append("--caption_extension") if caption_extension == "": run_cmd.append(".caption") # Default extension else: run_cmd.append(caption_extension) # Add paths for the image folder and the caption metadata file run_cmd.append(rf"{image_folder}") run_cmd.append(rf"{os.path.join(train_dir, caption_metadata_filename)}") # Include the full path flag if specified if full_path: run_cmd.append("--full_path") # Log the built command log.info(" ".join(run_cmd)) # Prepare environment variables env = setup_environment() # Execute the command if not just for printing if not print_only: subprocess.run(run_cmd, env=env) # create images buckets if generate_image_buckets: # Build the command to run the preparation script run_cmd = [ PYTHON, rf"{scriptdir}/sd-scripts/finetune/prepare_buckets_latents.py", rf"{image_folder}", rf"{os.path.join(train_dir, caption_metadata_filename)}", rf"{os.path.join(train_dir, latent_metadata_filename)}", rf"{pretrained_model_name_or_path}", "--batch_size", str(batch_size), "--max_resolution", str(max_resolution), "--min_bucket_reso", str(min_bucket_reso), "--max_bucket_reso", str(max_bucket_reso), "--mixed_precision", str(mixed_precision), ] # Conditional flags if full_path: run_cmd.append("--full_path") if sdxl_checkbox and sdxl_no_half_vae: log.info( "Using mixed_precision = no because no half vae is selected..." ) # Ensure 'no' is correctly handled without extra quotes that might be interpreted literally in command line run_cmd.append("--mixed_precision=no") # Log the complete command as a string for clarity log.info(" ".join(run_cmd)) # Copy and modify environment variables env = setup_environment() # Execute the command if not just for printing if not print_only: subprocess.run(run_cmd, env=env) if image_folder == "": log.error("Image folder dir is empty") return TRAIN_BUTTON_VISIBLE image_num = len( [ f for f, lower_f in ( (file, file.lower()) for file in os.listdir(image_folder) ) if lower_f.endswith((".jpg", ".jpeg", ".png", ".webp")) ] ) log.info(f"image_num = {image_num}") repeats = int(image_num) * int(dataset_repeats) log.info(f"repeats = {str(repeats)}") if max_train_steps == 0: # calculate max_train_steps max_train_steps = int( math.ceil( float(repeats) / int(train_batch_size) / int(gradient_accumulation_steps) * int(epoch) ) ) # Divide by two because flip augmentation create two copied of the source images if flip_aug and max_train_steps: max_train_steps = int(math.ceil(float(max_train_steps) / 2)) 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}" log.info(max_train_steps_info) if max_train_steps != 0: 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}") 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_checkbox: run_cmd.append(rf"{scriptdir}/sd-scripts/sdxl_train.py") else: run_cmd.append(rf"{scriptdir}/sd-scripts/fine_tune.py") in_json = ( f"{train_dir}/{latent_metadata_filename}" if use_latent_files == "Yes" else f"{train_dir}/{caption_metadata_filename}" ) cache_text_encoder_outputs = sdxl_checkbox and sdxl_cache_text_encoder_outputs no_half_vae = sdxl_checkbox and sdxl_no_half_vae 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) config_toml_data = { # Update the values in the TOML data "adaptive_noise_scale": ( adaptive_noise_scale if adaptive_noise_scale != 0 else None ), "async_upload": async_upload, "block_lr": block_lr, "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": cache_text_encoder_outputs, "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, "dataset_repeats": int(dataset_repeats), "debiased_estimation_loss": debiased_estimation_loss, "dynamo_backend": dynamo_backend, "enable_bucket": True, "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_repo_id": huggingface_repo_id, "huggingface_token": huggingface_token, "huggingface_repo_type": huggingface_repo_type, "huggingface_repo_visibility": huggingface_repo_visibility, "huggingface_path_in_repo": huggingface_path_in_repo, "in_json": in_json, "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_checkbox else None ), # only for sd1.5 "learning_rate_te1": ( learning_rate_te1 if sdxl_checkbox else None ), # only for sdxl "learning_rate_te2": ( learning_rate_te2 if sdxl_checkbox else None ), # only for sdxl "logging_dir": logging_dir, "log_tracker_name": log_tracker_name, "log_tracker_config": log_tracker_config, "loss_type": loss_type, "lr_scheduler": lr_scheduler, "lr_scheduler_args": str(lr_scheduler_args).replace('"', "").split(), "lr_warmup_steps": lr_warmup_steps, "masked_loss": masked_loss, "max_bucket_reso": int(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 multires_noise_iterations != 0 else None ), "no_half_vae": no_half_vae, "noise_offset": noise_offset if noise_offset != 0 else None, "noise_offset_random_strength": noise_offset_random_strength, "noise_offset_type": noise_offset_type, "optimizer_type": optimizer, "optimizer_args": str(optimizer_args).replace('"', "").split(), "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, "random_crop": random_crop, "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, "train_batch_size": train_batch_size, "train_data_dir": image_folder, "train_text_encoder": train_text_encoder, "log_with": log_with, "v2": v2, "v_parameterization": v_parameterization, "v_pred_like_loss": v_pred_like_loss if v_pred_like_loss != 0 else None, "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 = "" 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_finetune-{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("--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 finetune_tab( 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 finetune 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, finetuning=True, config=config ) image_folder = source_model.train_data_dir output_name = source_model.output_name with gr.Accordion("Folders", open=False), gr.Group(): folders = Folders(headless=headless, finetune=True, config=config) output_dir = folders.output_dir logging_dir = folders.logging_dir train_dir = folders.reg_data_dir with gr.Accordion("Metadata", open=False), gr.Group(): metadata = MetaData(config=config) with gr.Accordion("Dataset Preparation", open=False): with gr.Row(): max_resolution = gr.Textbox( label="Resolution (width,height)", value="512,512" ) min_bucket_reso = gr.Textbox(label="Min bucket resolution", value="256") max_bucket_reso = gr.Textbox( label="Max bucket resolution", value="1024" ) batch_size = gr.Textbox(label="Batch size", value="1") with gr.Row(): create_caption = gr.Checkbox( label="Generate caption metadata", value=True ) create_buckets = gr.Checkbox( label="Generate image buckets metadata", value=True ) use_latent_files = gr.Dropdown( label="Use latent files", choices=[ "No", "Yes", ], value="Yes", ) with gr.Accordion("Advanced parameters", open=False): with gr.Row(): caption_metadata_filename = gr.Textbox( label="Caption metadata filename", value="meta_cap.json", ) latent_metadata_filename = gr.Textbox( label="Latent metadata filename", value="meta_lat.json" ) with gr.Row(): full_path = gr.Checkbox(label="Use full path", value=True) weighted_captions = gr.Checkbox( label="Weighted captions", value=False ) with gr.Accordion("Parameters", open=False), gr.Column(): def list_presets(path): json_files = [] 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=["none"] + list_presets(f"{presets_dir}/finetune"), # elem_id="myDropdown", value="none", ) with gr.Accordion("Basic", open="True"): with gr.Group(elem_id="basic_tab"): basic_training = BasicTraining( learning_rate_value=1e-5, finetuning=True, sdxl_checkbox=source_model.sdxl_checkbox, config=config, ) # Add SDXL Parameters sdxl_params = SDXLParameters( source_model.sdxl_checkbox, config=config ) with gr.Row(): dataset_repeats = gr.Textbox(label="Dataset repeats", value=40) train_text_encoder = gr.Checkbox( label="Train text encoder", value=True ) with gr.Accordion("Advanced", open=False, elem_id="advanced_tab"): with gr.Row(): gradient_accumulation_steps = gr.Slider( label="Gradient accumulate steps", info="Number of updates steps to accumulate before performing a backward/update pass", value=config.get("advanced.gradient_accumulation_steps", 1), minimum=1, maximum=120, step=1, ) block_lr = gr.Textbox( label="Block LR (SDXL)", placeholder="(Optional)", info="Specify the different learning rates for each U-Net block. Specify 23 values separated by commas like 1e-3,1e-3 ... 1e-3", ) advanced_training = AdvancedTraining( headless=headless, finetuning=True, config=config ) advanced_training.color_aug.change( color_aug_changed, inputs=[advanced_training.color_aug], outputs=[ basic_training.cache_latents ], # Not applicable to fine_tune.py ) 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") 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, train_dir, image_folder, output_dir, source_model.dataset_config, logging_dir, max_resolution, min_bucket_reso, max_bucket_reso, batch_size, advanced_training.flip_aug, advanced_training.masked_loss, caption_metadata_filename, latent_metadata_filename, full_path, basic_training.learning_rate, basic_training.lr_scheduler, basic_training.lr_warmup, dataset_repeats, 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.learning_rate_te, basic_training.learning_rate_te1, basic_training.learning_rate_te2, train_text_encoder, advanced_training.full_bf16, create_caption, create_buckets, source_model.save_model_as, basic_training.caption_extension, advanced_training.xformers, advanced_training.clip_skip, 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, advanced_training.save_state, advanced_training.save_state_on_train_end, advanced_training.resume, advanced_training.gradient_checkpointing, gradient_accumulation_steps, block_lr, advanced_training.mem_eff_attn, advanced_training.shuffle_caption, 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.full_fp16, advanced_training.color_aug, source_model.model_list, basic_training.cache_latents, basic_training.cache_latents_to_disk, use_latent_files, advanced_training.keep_tokens, 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, 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, sdxl_params.sdxl_cache_text_encoder_outputs, sdxl_params.sdxl_no_half_vae, 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, dummy_db_false, configuration.config_file_name] + settings_list + [training_preset], outputs=[configuration.config_file_name] + settings_list + [training_preset], show_progress=False, ) # config.button_open_config.click( # open_configuration, # inputs=[dummy_db_true, dummy_db_false, config.config_file_name] + settings_list, # outputs=[config.config_file_name] + settings_list, # 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], 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], 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, ) 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, # ) with gr.Tab("Guides"): gr.Markdown("This section provide Various Finetuning guides and information...") top_level_path = rf'"{scriptdir}/docs/Finetuning/top_level.md"' if os.path.exists(top_level_path): with open(os.path.join(top_level_path), "r", encoding="utf-8") as file: guides_top_level = file.read() + "\n" gr.Markdown(guides_top_level)