lora_gui.py

# v1: initial release
# v2: add open and save folder icons
# v3: Add new Utilities tab for Dreambooth folder preparation
# v3.1: Adding captionning of images to utilities

import gradio as gr

# import easygui
import json
import math
import os
import subprocess
import pathlib
import argparse
from library.common_gui import (
    get_folder_path,
    remove_doublequote,
    get_file_path,
    get_any_file_path,
    get_saveasfile_path,
    color_aug_changed,
    save_inference_file,
    gradio_advanced_training,
    run_cmd_advanced_training,
    gradio_training,
    gradio_config,
    gradio_source_model,
    run_cmd_training,
    # set_legacy_8bitadam,
    update_my_data,
    check_if_model_exist,
    output_message,
)
from library.dreambooth_folder_creation_gui import (
    gradio_dreambooth_folder_creation_tab,
)
from library.tensorboard_gui import (
    gradio_tensorboard,
    start_tensorboard,
    stop_tensorboard,
)
from library.dataset_balancing_gui import gradio_dataset_balancing_tab
from library.utilities import utilities_tab
from library.merge_lora_gui import gradio_merge_lora_tab
from library.svd_merge_lora_gui import gradio_svd_merge_lora_tab
from library.verify_lora_gui import gradio_verify_lora_tab
from library.resize_lora_gui import gradio_resize_lora_tab
from library.sampler_gui import sample_gradio_config, run_cmd_sample

# from easygui import msgbox

folder_symbol = '\U0001f4c2'  # 📂
refresh_symbol = '\U0001f504'  # 🔄
save_style_symbol = '\U0001f4be'  # 💾
document_symbol = '\U0001F4C4'   # 📄
path_of_this_folder = os.getcwd()


def save_configuration(
    save_as,
    file_path,
    pretrained_model_name_or_path,
    v2,
    v_parameterization,
    logging_dir,
    train_data_dir,
    reg_data_dir,
    output_dir,
    max_resolution,
    learning_rate,
    lr_scheduler,
    lr_warmup,
    train_batch_size,
    epoch,
    save_every_n_epochs,
    mixed_precision,
    save_precision,
    seed,
    num_cpu_threads_per_process,
    cache_latents,
    cache_latents_to_disk,
    caption_extension,
    enable_bucket,
    gradient_checkpointing,
    full_fp16,
    no_token_padding,
    stop_text_encoder_training,
    # use_8bit_adam,
    xformers,
    save_model_as,
    shuffle_caption,
    save_state,
    resume,
    prior_loss_weight,
    text_encoder_lr,
    unet_lr,
    network_dim,
    lora_network_weights,
    color_aug,
    flip_aug,
    clip_skip,
    gradient_accumulation_steps,
    mem_eff_attn,
    output_name,
    model_list,
    max_token_length,
    max_train_epochs,
    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,
    caption_dropout_every_n_epochs,
    caption_dropout_rate,
    optimizer,
    optimizer_args,
    noise_offset,
    multires_noise_iterations,
    multires_noise_discount,
    LoRA_type,
    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_dims,
    conv_alphas,
    weighted_captions,
    unit,
    save_every_n_steps,
    save_last_n_steps,
    save_last_n_steps_state,
    use_wandb,
    wandb_api_key,
):
    # Get list of function parameters and values
    parameters = list(locals().items())

    original_file_path = file_path

    save_as_bool = True if save_as.get('label') == 'True' else False

    if save_as_bool:
        print('Save as...')
        file_path = get_saveasfile_path(file_path)
    else:
        print('Save...')
        if file_path == None or file_path == '':
            file_path = get_saveasfile_path(file_path)

    # print(file_path)

    if file_path == None or file_path == '':
        return original_file_path  # In case a file_path was provided and the user decide to cancel the open action

    # Return the values of the variables as a dictionary
    variables = {
        name: value
        for name, value in parameters  # locals().items()
        if name
        not in [
            'file_path',
            'save_as',
        ]
    }

    # Extract the destination directory from the file path
    destination_directory = os.path.dirname(file_path)

    # Create the destination directory if it doesn't exist
    if not os.path.exists(destination_directory):
        os.makedirs(destination_directory)

    # Save the data to the selected file
    with open(file_path, 'w') as file:
        json.dump(variables, file, indent=2)

    return file_path


def open_configuration(
    ask_for_file,
    file_path,
    pretrained_model_name_or_path,
    v2,
    v_parameterization,
    logging_dir,
    train_data_dir,
    reg_data_dir,
    output_dir,
    max_resolution,
    learning_rate,
    lr_scheduler,
    lr_warmup,
    train_batch_size,
    epoch,
    save_every_n_epochs,
    mixed_precision,
    save_precision,
    seed,
    num_cpu_threads_per_process,
    cache_latents,
    cache_latents_to_disk,
    caption_extension,
    enable_bucket,
    gradient_checkpointing,
    full_fp16,
    no_token_padding,
    stop_text_encoder_training,
    # use_8bit_adam,
    xformers,
    save_model_as,
    shuffle_caption,
    save_state,
    resume,
    prior_loss_weight,
    text_encoder_lr,
    unet_lr,
    network_dim,
    lora_network_weights,
    color_aug,
    flip_aug,
    clip_skip,
    gradient_accumulation_steps,
    mem_eff_attn,
    output_name,
    model_list,
    max_token_length,
    max_train_epochs,
    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,
    caption_dropout_every_n_epochs,
    caption_dropout_rate,
    optimizer,
    optimizer_args,
    noise_offset,
    multires_noise_iterations,
    multires_noise_discount,
    LoRA_type,
    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_dims,
    conv_alphas,
    weighted_captions,
    unit,
    save_every_n_steps,
    save_last_n_steps,
    save_last_n_steps_state,
    use_wandb,
    wandb_api_key,
):
    # Get list of function parameters and values
    parameters = list(locals().items())

    ask_for_file = True if ask_for_file.get('label') == 'True' else False

    original_file_path = file_path

    if ask_for_file:
        file_path = get_file_path(file_path)

    if not file_path == '' and not file_path == None:
        # load variables from JSON file
        with open(file_path, 'r') as f:
            my_data = json.load(f)
            print('Loading config...')

            # Update values to fix deprecated use_8bit_adam checkbox, set appropriate optimizer if it is set to True, etc.
            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))

    # This next section is about making the LoCon parameters visible if LoRA_type = 'Standard'
    if my_data.get('LoRA_type', 'Standard') == 'LoCon':
        values.append(gr.Row.update(visible=True))
    else:
        values.append(gr.Row.update(visible=False))

    return tuple(values)


def train_model(
    headless,
    print_only,
    pretrained_model_name_or_path,
    v2,
    v_parameterization,
    logging_dir,
    train_data_dir,
    reg_data_dir,
    output_dir,
    max_resolution,
    learning_rate,
    lr_scheduler,
    lr_warmup,
    train_batch_size,
    epoch,
    save_every_n_epochs,
    mixed_precision,
    save_precision,
    seed,
    num_cpu_threads_per_process,
    cache_latents,
    cache_latents_to_disk,
    caption_extension,
    enable_bucket,
    gradient_checkpointing,
    full_fp16,
    no_token_padding,
    stop_text_encoder_training_pct,
    # use_8bit_adam,
    xformers,
    save_model_as,
    shuffle_caption,
    save_state,
    resume,
    prior_loss_weight,
    text_encoder_lr,
    unet_lr,
    network_dim,
    lora_network_weights,
    color_aug,
    flip_aug,
    clip_skip,
    gradient_accumulation_steps,
    mem_eff_attn,
    output_name,
    model_list,  # Keep this. Yes, it is unused here but required given the common list used
    max_token_length,
    max_train_epochs,
    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,
    caption_dropout_every_n_epochs,
    caption_dropout_rate,
    optimizer,
    optimizer_args,
    noise_offset,
    multires_noise_iterations,
    multires_noise_discount,
    LoRA_type,
    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_dims,
    conv_alphas,
    weighted_captions,
    unit,
    save_every_n_steps,
    save_last_n_steps,
    save_last_n_steps_state,
    use_wandb,
    wandb_api_key,
):
    print_only_bool = True if print_only.get('label') == 'True' else False
    headless_bool = True if headless.get('label') == 'True' else False

    if pretrained_model_name_or_path == '':
        output_message(
            msg='Source model information is missing', headless=headless_bool
        )
        return

    if train_data_dir == '':
        output_message(
            msg='Image folder path is missing', headless=headless_bool
        )
        return

    if not os.path.exists(train_data_dir):
        output_message(
            msg='Image folder does not exist', headless=headless_bool
        )
        return

    if reg_data_dir != '':
        if not os.path.exists(reg_data_dir):
            output_message(
                msg='Regularisation folder does not exist',
                headless=headless_bool,
            )
            return

    if output_dir == '':
        output_message(
            msg='Output folder path is missing', headless=headless_bool
        )
        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 float(noise_offset) > 0 and (
        multires_noise_iterations > 0 or multires_noise_discount > 0
    ):
        output_message(
            msg="noise offset and multires_noise can't be set at the same time. Only use one or the other.",
            title='Error',
            headless=headless_bool,
        )
        return

    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 check_if_model_exist(
        output_name, output_dir, save_model_as, headless=headless_bool
    ):
        return

    if optimizer == 'Adafactor' and lr_warmup != '0':
        output_message(
            msg="Warning: lr_scheduler is set to 'Adafactor', so 'LR warmup (% of steps)' will be considered 0.",
            title='Warning',
            headless=headless_bool,
        )
        lr_warmup = '0'

    # If string is empty set string to 0.
    if text_encoder_lr == '':
        text_encoder_lr = 0
    if unet_lr == '':
        unet_lr = 0

    # 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'))
                ]
            )

            print(f'Folder {folder}: {num_images} images found')

            # Calculate the total number of steps for this folder
            steps = repeats * num_images

            # Print the result
            print(f'Folder {folder}: {steps} steps')

            total_steps += steps

        except ValueError:
            # Handle the case where the folder name does not contain an underscore
            print(
                f"Error: '{folder}' does not contain an underscore, skipping..."
            )

    if reg_data_dir == '':
        reg_factor = 1
    else:
        print(
            '\033[94mRegularisation images are used... Will double the number of steps required...\033[0m'
        )
        reg_factor = 2

    # calculate max_train_steps
    max_train_steps = int(
        math.ceil(
            float(total_steps)
            / int(train_batch_size)
            * int(epoch)
            * int(reg_factor)
        )
    )
    print(f'max_train_steps = {max_train_steps}')

    # calculate stop encoder training
    if stop_text_encoder_training_pct == None:
        stop_text_encoder_training = 0
    else:
        stop_text_encoder_training = math.ceil(
            float(max_train_steps) / 100 * int(stop_text_encoder_training_pct)
        )
    print(f'stop_text_encoder_training = {stop_text_encoder_training}')

    lr_warmup_steps = round(float(int(lr_warmup) * int(max_train_steps) / 100))
    print(f'lr_warmup_steps = {lr_warmup_steps}')

    run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "train_network.py"'

    if v2:
        run_cmd += ' --v2'
    if v_parameterization:
        run_cmd += ' --v_parameterization'
    if enable_bucket:
        run_cmd += ' --enable_bucket'
    if no_token_padding:
        run_cmd += ' --no_token_padding'
    if weighted_captions:
        run_cmd += ' --weighted_captions'
    run_cmd += (
        f' --pretrained_model_name_or_path="{pretrained_model_name_or_path}"'
    )
    run_cmd += f' --train_data_dir="{train_data_dir}"'
    if len(reg_data_dir):
        run_cmd += f' --reg_data_dir="{reg_data_dir}"'
    run_cmd += f' --resolution={max_resolution}'
    run_cmd += f' --output_dir="{output_dir}"'
    if not logging_dir == '':
        run_cmd += f' --logging_dir="{logging_dir}"'
    run_cmd += f' --network_alpha="{network_alpha}"'
    if not training_comment == '':
        run_cmd += f' --training_comment="{training_comment}"'
    if not stop_text_encoder_training == 0:
        run_cmd += (
            f' --stop_text_encoder_training={stop_text_encoder_training}'
        )
    if not save_model_as == 'same as source model':
        run_cmd += f' --save_model_as={save_model_as}'
    if not float(prior_loss_weight) == 1.0:
        run_cmd += f' --prior_loss_weight={prior_loss_weight}'
    if LoRA_type == 'LoCon' or LoRA_type == 'LyCORIS/LoCon':
        try:
            import lycoris
        except ModuleNotFoundError:
            print(
                "\033[1;31mError:\033[0m The required module 'lycoris_lora' is not installed. Please install by running \033[33mupgrade.ps1\033[0m before running this program."
            )
            return
        run_cmd += f' --network_module=lycoris.kohya'
        run_cmd += f' --network_args "conv_dim={conv_dim}" "conv_alpha={conv_alpha}" "algo=lora"'
    if LoRA_type == 'LyCORIS/LoHa':
        try:
            import lycoris
        except ModuleNotFoundError:
            print(
                "\033[1;31mError:\033[0m The required module 'lycoris_lora' is not installed. Please install by running \033[33mupgrade.ps1\033[0m before running this program."
            )
            return
        run_cmd += f' --network_module=lycoris.kohya'
        run_cmd += f' --network_args "conv_dim={conv_dim}" "conv_alpha={conv_alpha}" "algo=loha"'

    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_dims',
            'conv_alphas',
        ]

        run_cmd += f' --network_module=networks.lora'
        kohya_lora_vars = {
            key: value
            for key, value in vars().items()
            if key in kohya_lora_var_list and value
        }

        network_args = ''
        if LoRA_type == 'Kohya LoCon':
            network_args += f' "conv_dim={conv_dim}" "conv_alpha={conv_alpha}"'

        for key, value in kohya_lora_vars.items():
            if value:
                network_args += f' {key}="{value}"'

        if network_args:
            run_cmd += f' --network_args{network_args}'

    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_dims',
            'conv_alphas',
            'unit',
        ]

        run_cmd += f' --network_module=networks.dylora'
        kohya_lora_vars = {
            key: value
            for key, value in vars().items()
            if key in kohya_lora_var_list and value
        }

        network_args = ''

        for key, value in kohya_lora_vars.items():
            if value:
                network_args += f' {key}="{value}"'

        if network_args:
            run_cmd += f' --network_args{network_args}'

    if not (float(text_encoder_lr) == 0) or not (float(unet_lr) == 0):
        if not (float(text_encoder_lr) == 0) and not (float(unet_lr) == 0):
            run_cmd += f' --text_encoder_lr={text_encoder_lr}'
            run_cmd += f' --unet_lr={unet_lr}'
        elif not (float(text_encoder_lr) == 0):
            run_cmd += f' --text_encoder_lr={text_encoder_lr}'
            run_cmd += f' --network_train_text_encoder_only'
        else:
            run_cmd += f' --unet_lr={unet_lr}'
            run_cmd += f' --network_train_unet_only'
    else:
        if float(learning_rate) == 0:
            output_message(
                msg='Please input learning rate values.',
                headless=headless_bool,
            )
            return

    run_cmd += f' --network_dim={network_dim}'

    if not lora_network_weights == '':
        run_cmd += f' --network_weights="{lora_network_weights}"'
    if int(gradient_accumulation_steps) > 1:
        run_cmd += f' --gradient_accumulation_steps={int(gradient_accumulation_steps)}'
    if not output_name == '':
        run_cmd += f' --output_name="{output_name}"'
    if not lr_scheduler_num_cycles == '':
        run_cmd += f' --lr_scheduler_num_cycles="{lr_scheduler_num_cycles}"'
    else:
        run_cmd += f' --lr_scheduler_num_cycles="{epoch}"'
    if not lr_scheduler_power == '':
        run_cmd += f' --lr_scheduler_power="{lr_scheduler_power}"'

    run_cmd += run_cmd_training(
        learning_rate=learning_rate,
        lr_scheduler=lr_scheduler,
        lr_warmup_steps=lr_warmup_steps,
        train_batch_size=train_batch_size,
        max_train_steps=max_train_steps,
        save_every_n_epochs=save_every_n_epochs,
        mixed_precision=mixed_precision,
        save_precision=save_precision,
        seed=seed,
        caption_extension=caption_extension,
        cache_latents=cache_latents,
        cache_latents_to_disk=cache_latents_to_disk,
        optimizer=optimizer,
        optimizer_args=optimizer_args,
    )

    run_cmd += run_cmd_advanced_training(
        max_train_epochs=max_train_epochs,
        max_data_loader_n_workers=max_data_loader_n_workers,
        max_token_length=max_token_length,
        resume=resume,
        save_state=save_state,
        mem_eff_attn=mem_eff_attn,
        clip_skip=clip_skip,
        flip_aug=flip_aug,
        color_aug=color_aug,
        shuffle_caption=shuffle_caption,
        gradient_checkpointing=gradient_checkpointing,
        full_fp16=full_fp16,
        xformers=xformers,
        # use_8bit_adam=use_8bit_adam,
        keep_tokens=keep_tokens,
        persistent_data_loader_workers=persistent_data_loader_workers,
        bucket_no_upscale=bucket_no_upscale,
        random_crop=random_crop,
        bucket_reso_steps=bucket_reso_steps,
        caption_dropout_every_n_epochs=caption_dropout_every_n_epochs,
        caption_dropout_rate=caption_dropout_rate,
        noise_offset=noise_offset,
        multires_noise_iterations=multires_noise_iterations,
        multires_noise_discount=multires_noise_discount,
        additional_parameters=additional_parameters,
        vae_batch_size=vae_batch_size,
        min_snr_gamma=min_snr_gamma,
        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,
        use_wandb=use_wandb,
        wandb_api_key=wandb_api_key,
    )

    run_cmd += run_cmd_sample(
        sample_every_n_steps,
        sample_every_n_epochs,
        sample_sampler,
        sample_prompts,
        output_dir,
    )

    # if not down_lr_weight == '':
    #     run_cmd += f' --down_lr_weight="{down_lr_weight}"'
    # if not mid_lr_weight == '':
    #     run_cmd += f' --mid_lr_weight="{mid_lr_weight}"'
    # if not up_lr_weight == '':
    #     run_cmd += f' --up_lr_weight="{up_lr_weight}"'
    # if not block_lr_zero_threshold == '':
    #     run_cmd += f' --block_lr_zero_threshold="{block_lr_zero_threshold}"'
    # if not block_dims == '':
    #     run_cmd += f' --block_dims="{block_dims}"'
    # if not block_alphas == '':
    #     run_cmd += f' --block_alphas="{block_alphas}"'
    # if not conv_dims == '':
    #     run_cmd += f' --conv_dims="{conv_dims}"'
    # if not conv_alphas == '':
    #     run_cmd += f' --conv_alphas="{conv_alphas}"'

    if print_only_bool:
        print(
            '\033[93m\nHere is the trainer command as a reference. It will not be executed:\033[0m\n'
        )
        print('\033[96m' + run_cmd + '\033[0m\n')
    else:
        print(run_cmd)
        # Run the command
        if os.name == 'posix':
            os.system(run_cmd)
        else:
            subprocess.run(run_cmd)

        # check if output_dir/last is a folder... therefore it is a diffuser model
        last_dir = pathlib.Path(f'{output_dir}/{output_name}')

        if not last_dir.is_dir():
            # Copy inference model for v2 if required
            save_inference_file(
                output_dir, v2, v_parameterization, output_name
            )


def lora_tab(
    train_data_dir_input=gr.Textbox(),
    reg_data_dir_input=gr.Textbox(),
    output_dir_input=gr.Textbox(),
    logging_dir_input=gr.Textbox(),
    headless=False,
):
    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)

    gr.Markdown(
        'Train a custom model using kohya train network LoRA python code...'
    )
    (
        button_open_config,
        button_save_config,
        button_save_as_config,
        config_file_name,
        button_load_config,
    ) = gradio_config(headless=headless)

    (
        pretrained_model_name_or_path,
        v2,
        v_parameterization,
        save_model_as,
        model_list,
    ) = gradio_source_model(
        save_model_as_choices=[
            'ckpt',
            'safetensors',
        ],
        headless=headless,
    )

    with gr.Tab('Folders'):
        with gr.Row():
            train_data_dir = gr.Textbox(
                label='Image folder',
                placeholder='Folder where the training folders containing the images are located',
            )
            train_data_dir_folder = gr.Button(
                '📂', elem_id='open_folder_small', visible=(not headless)
            )
            train_data_dir_folder.click(
                get_folder_path,
                outputs=train_data_dir,
                show_progress=False,
            )
            reg_data_dir = gr.Textbox(
                label='Regularisation folder',
                placeholder='(Optional) Folder where where the regularization folders containing the images are located',
            )
            reg_data_dir_folder = gr.Button(
                '📂', elem_id='open_folder_small', visible=(not headless)
            )
            reg_data_dir_folder.click(
                get_folder_path,
                outputs=reg_data_dir,
                show_progress=False,
            )
        with gr.Row():
            output_dir = gr.Textbox(
                label='Output folder',
                placeholder='Folder to output trained model',
            )
            output_dir_folder = gr.Button(
                '📂', elem_id='open_folder_small', visible=(not headless)
            )
            output_dir_folder.click(
                get_folder_path,
                outputs=output_dir,
                show_progress=False,
            )
            logging_dir = gr.Textbox(
                label='Logging folder',
                placeholder='Optional: enable logging and output TensorBoard log to this folder',
            )
            logging_dir_folder = gr.Button(
                '📂', elem_id='open_folder_small', visible=(not headless)
            )
            logging_dir_folder.click(
                get_folder_path,
                outputs=logging_dir,
                show_progress=False,
            )
        with gr.Row():
            output_name = gr.Textbox(
                label='Model output name',
                placeholder='(Name of the model to output)',
                value='last',
                interactive=True,
            )
            training_comment = gr.Textbox(
                label='Training comment',
                placeholder='(Optional) Add training comment to be included in metadata',
                interactive=True,
            )
        train_data_dir.change(
            remove_doublequote,
            inputs=[train_data_dir],
            outputs=[train_data_dir],
        )
        reg_data_dir.change(
            remove_doublequote,
            inputs=[reg_data_dir],
            outputs=[reg_data_dir],
        )
        output_dir.change(
            remove_doublequote,
            inputs=[output_dir],
            outputs=[output_dir],
        )
        logging_dir.change(
            remove_doublequote,
            inputs=[logging_dir],
            outputs=[logging_dir],
        )
    with gr.Tab('Training parameters'):
        with gr.Row():
            LoRA_type = gr.Dropdown(
                label='LoRA type',
                choices=[
                    'Kohya DyLoRA',
                    'Kohya LoCon',
                    # 'LoCon',
                    'LyCORIS/LoCon',
                    'LyCORIS/LoHa',
                    'Standard',
                ],
                value='Standard',
            )
            lora_network_weights = gr.Textbox(
                label='LoRA network weights',
                placeholder='{Optional) Path to existing LoRA network weights to resume training',
            )
            lora_network_weights_file = gr.Button(
                document_symbol,
                elem_id='open_folder_small',
                visible=(not headless),
            )
            lora_network_weights_file.click(
                get_any_file_path,
                inputs=[lora_network_weights],
                outputs=lora_network_weights,
                show_progress=False,
            )
        (
            learning_rate,
            lr_scheduler,
            lr_warmup,
            train_batch_size,
            epoch,
            save_every_n_epochs,
            mixed_precision,
            save_precision,
            num_cpu_threads_per_process,
            seed,
            caption_extension,
            cache_latents,
            cache_latents_to_disk,
            optimizer,
            optimizer_args,
        ) = gradio_training(
            learning_rate_value='0.0001',
            lr_scheduler_value='cosine',
            lr_warmup_value='10',
        )

        with gr.Row():
            text_encoder_lr = gr.Number(
                label='Text Encoder learning rate',
                value='5e-5',
                info='Optional',
            )
            unet_lr = gr.Number(
                label='Unet learning rate',
                value='0.0001',
                info='Optional',
            )
            network_dim = gr.Slider(
                minimum=1,
                maximum=1024,
                label='Network Rank (Dimension)',
                value=8,
                step=1,
                interactive=True,
            )
            network_alpha = gr.Slider(
                minimum=0.1,
                maximum=1024,
                label='Network Alpha',
                value=1,
                step=0.1,
                interactive=True,
                info='alpha for LoRA weight scaling',
            )
        with gr.Row(visible=False) as LoCon_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=1,
                maximum=512,
                value=1,
                step=1,
                label='Convolution Rank (Dimension)',
            )
            conv_alpha = gr.Slider(
                minimum=0.1,
                maximum=512,
                value=1,
                step=0.1,
                label='Convolution Alpha',
            )
        with gr.Row(visible=False) as kohya_dylora:
            unit = gr.Slider(
                minimum=1,
                maximum=64,
                label='DyLoRA Unit',
                value=1,
                step=1,
                interactive=True,
            )

        # Show of hide LoCon conv settings depending on LoRA type selection
        def update_LoRA_settings(LoRA_type):
            # Print a message when LoRA type is changed
            print('LoRA type changed...')

            # Determine if LoCon_row should be visible based on LoRA_type
            LoCon_row = LoRA_type in {
                'LoCon',
                'Kohya DyLoRA',
                'Kohya LoCon',
                'LyCORIS/LoHa',
                'LyCORIS/LoCon',
            }

            # Determine if LoRA_type_change should be visible based on LoRA_type
            LoRA_type_change = LoRA_type in {
                'Standard',
                'Kohya DyLoRA',
                'Kohya LoCon',
            }

            # Determine if kohya_dylora_visible should be visible based on LoRA_type
            kohya_dylora_visible = LoRA_type == 'Kohya DyLoRA'

            # Return the updated visibility settings for the groups
            return (
                gr.Group.update(visible=LoCon_row),
                gr.Group.update(visible=LoRA_type_change),
                gr.Group.update(visible=kohya_dylora_visible),
            )

        with gr.Row():
            max_resolution = gr.Textbox(
                label='Max resolution',
                value='512,512',
                placeholder='512,512',
                info='The maximum resolution of dataset images. W,H',
            )
            stop_text_encoder_training = gr.Slider(
                minimum=0,
                maximum=100,
                value=0,
                step=1,
                label='Stop text encoder training',
                info='After what % of steps should the text encoder stop being trained. 0 = train for all steps.',
            )
            enable_bucket = gr.Checkbox(
                label='Enable buckets',
                value=True,
                info='Allow non similar resolution dataset images to be trained on.',
            )

        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_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='Expand LoRA to Conv2d 3x3 and specify the dim (rank) of each block. Specify 25 numbers.',
                        )
                        conv_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.',
                        )
            with gr.Row():
                no_token_padding = gr.Checkbox(
                    label='No token padding', value=False
                )
                gradient_accumulation_steps = gr.Number(
                    label='Gradient accumulate steps', value='1'
                )
                weighted_captions = gr.Checkbox(
                    label='Weighted captions',
                    value=False,
                    info='Enable weighted captions in the standard style (token:1.3). No commas inside parens, or shuffle/dropout may break the decoder.',
                )
            with gr.Row():
                prior_loss_weight = gr.Number(
                    label='Prior loss weight', value=1.0
                )
                lr_scheduler_num_cycles = gr.Textbox(
                    label='LR number of cycles',
                    placeholder='(Optional) For Cosine with restart and polynomial only',
                )

                lr_scheduler_power = gr.Textbox(
                    label='LR power',
                    placeholder='(Optional) For Cosine with restart and polynomial only',
                )
            (
                # use_8bit_adam,
                xformers,
                full_fp16,
                gradient_checkpointing,
                shuffle_caption,
                color_aug,
                flip_aug,
                clip_skip,
                mem_eff_attn,
                save_state,
                resume,
                max_token_length,
                max_train_epochs,
                max_data_loader_n_workers,
                keep_tokens,
                persistent_data_loader_workers,
                bucket_no_upscale,
                random_crop,
                bucket_reso_steps,
                caption_dropout_every_n_epochs,
                caption_dropout_rate,
                noise_offset,
                multires_noise_iterations,
                multires_noise_discount,
                additional_parameters,
                vae_batch_size,
                min_snr_gamma,
                save_every_n_steps,
                save_last_n_steps,
                save_last_n_steps_state,
                use_wandb,
                wandb_api_key,
            ) = gradio_advanced_training(headless=headless)
            color_aug.change(
                color_aug_changed,
                inputs=[color_aug],
                outputs=[cache_latents],
            )

        (
            sample_every_n_steps,
            sample_every_n_epochs,
            sample_sampler,
            sample_prompts,
        ) = sample_gradio_config()

        LoRA_type.change(
            update_LoRA_settings,
            inputs=[LoRA_type],
            outputs=[LoCon_row, kohya_advanced_lora, kohya_dylora],
        )

    with gr.Tab('Tools'):
        gr.Markdown(
            'This section provide Dreambooth tools to help setup your dataset...'
        )
        gradio_dreambooth_folder_creation_tab(
            train_data_dir_input=train_data_dir,
            reg_data_dir_input=reg_data_dir,
            output_dir_input=output_dir,
            logging_dir_input=logging_dir,
            headless=headless,
        )
        gradio_dataset_balancing_tab(headless=headless)
        gradio_merge_lora_tab(headless=headless)
        gradio_svd_merge_lora_tab(headless=headless)
        gradio_resize_lora_tab(headless=headless)
        gradio_verify_lora_tab(headless=headless)

    button_run = gr.Button('Train model', variant='primary')

    button_print = gr.Button('Print training command')

    # Setup gradio tensorboard buttons
    button_start_tensorboard, button_stop_tensorboard = gradio_tensorboard()

    button_start_tensorboard.click(
        start_tensorboard,
        inputs=logging_dir,
        show_progress=False,
    )

    button_stop_tensorboard.click(
        stop_tensorboard,
        show_progress=False,
    )

    settings_list = [
        pretrained_model_name_or_path,
        v2,
        v_parameterization,
        logging_dir,
        train_data_dir,
        reg_data_dir,
        output_dir,
        max_resolution,
        learning_rate,
        lr_scheduler,
        lr_warmup,
        train_batch_size,
        epoch,
        save_every_n_epochs,
        mixed_precision,
        save_precision,
        seed,
        num_cpu_threads_per_process,
        cache_latents,
        cache_latents_to_disk,
        caption_extension,
        enable_bucket,
        gradient_checkpointing,
        full_fp16,
        no_token_padding,
        stop_text_encoder_training,
        # use_8bit_adam,
        xformers,
        save_model_as,
        shuffle_caption,
        save_state,
        resume,
        prior_loss_weight,
        text_encoder_lr,
        unet_lr,
        network_dim,
        lora_network_weights,
        color_aug,
        flip_aug,
        clip_skip,
        gradient_accumulation_steps,
        mem_eff_attn,
        output_name,
        model_list,
        max_token_length,
        max_train_epochs,
        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,
        caption_dropout_every_n_epochs,
        caption_dropout_rate,
        optimizer,
        optimizer_args,
        noise_offset,
        multires_noise_iterations,
        multires_noise_discount,
        LoRA_type,
        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_dims,
        conv_alphas,
        weighted_captions,
        unit,
        save_every_n_steps,
        save_last_n_steps,
        save_last_n_steps_state,
        use_wandb,
        wandb_api_key,
    ]

    button_open_config.click(
        open_configuration,
        inputs=[dummy_db_true, config_file_name] + settings_list,
        outputs=[config_file_name] + settings_list + [LoCon_row],
        show_progress=False,
    )

    button_load_config.click(
        open_configuration,
        inputs=[dummy_db_false, config_file_name] + settings_list,
        outputs=[config_file_name] + settings_list + [LoCon_row],
        show_progress=False,
    )

    button_save_config.click(
        save_configuration,
        inputs=[dummy_db_false, config_file_name] + settings_list,
        outputs=[config_file_name],
        show_progress=False,
    )

    button_save_as_config.click(
        save_configuration,
        inputs=[dummy_db_true, config_file_name] + settings_list,
        outputs=[config_file_name],
        show_progress=False,
    )

    button_run.click(
        train_model,
        inputs=[dummy_headless] + [dummy_db_false] + settings_list,
        show_progress=False,
    )

    button_print.click(
        train_model,
        inputs=[dummy_headless] + [dummy_db_true] + settings_list,
        show_progress=False,
    )

    return (
        train_data_dir,
        reg_data_dir,
        output_dir,
        logging_dir,
    )


def UI(**kwargs):
    css = ''

    headless = kwargs.get('headless', False)
    print(f'headless: {headless}')

    if os.path.exists('./style.css'):
        with open(os.path.join('./style.css'), 'r', encoding='utf8') as file:
            print('Load CSS...')
            css += file.read() + '\n'

    interface = gr.Blocks(
        css=css, title='Kohya_ss GUI', theme=gr.themes.Default()
    )

    with interface:
        with gr.Tab('LoRA'):
            (
                train_data_dir_input,
                reg_data_dir_input,
                output_dir_input,
                logging_dir_input,
            ) = lora_tab(headless=headless)
        with gr.Tab('Utilities'):
            utilities_tab(
                train_data_dir_input=train_data_dir_input,
                reg_data_dir_input=reg_data_dir_input,
                output_dir_input=output_dir_input,
                logging_dir_input=logging_dir_input,
                enable_copy_info_button=True,
                headless=headless,
            )

    # Show the interface
    launch_kwargs = {}
    username = kwargs.get('username')
    password = kwargs.get('password')
    server_port = kwargs.get('server_port', 0)
    inbrowser = kwargs.get('inbrowser', False)
    share = kwargs.get('share', False)
    server_name = kwargs.get('listen')

    launch_kwargs['server_name'] = server_name
    if username and password:
        launch_kwargs['auth'] = (username, password)
    if server_port > 0:
        launch_kwargs['server_port'] = server_port
    if inbrowser:
        launch_kwargs['inbrowser'] = inbrowser
    if share:
        launch_kwargs['share'] = share
    interface.launch(**launch_kwargs)


if __name__ == '__main__':
    # torch.cuda.set_per_process_memory_fraction(0.48)
    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--listen',
        type=str,
        default='127.0.0.1',
        help='IP to listen on for connections to Gradio',
    )
    parser.add_argument(
        '--username', type=str, default='', help='Username for authentication'
    )
    parser.add_argument(
        '--password', type=str, default='', help='Password for authentication'
    )
    parser.add_argument(
        '--server_port',
        type=int,
        default=0,
        help='Port to run the server listener on',
    )
    parser.add_argument(
        '--inbrowser', action='store_true', help='Open in browser'
    )
    parser.add_argument(
        '--share', action='store_true', help='Share the gradio UI'
    )
    parser.add_argument(
        '--headless', action='store_true', help='Is the server headless'
    )

    args = parser.parse_args()

    UI(
        username=args.username,
        password=args.password,
        inbrowser=args.inbrowser,
        server_port=args.server_port,
        share=args.share,
        listen=args.listen,
        headless=args.headless,
    )