782 lines
22 KiB
Python
782 lines
22 KiB
Python
# v1: initial release
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# v2: add open and save folder icons
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# v3: Add new Utilities tab for Dreambooth folder preparation
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# v3.1: Adding captionning of images to utilities
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import gradio as gr
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import json
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import math
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import os
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import subprocess
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import pathlib
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import argparse
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from library.common_gui import (
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get_folder_path,
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remove_doublequote,
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get_file_path,
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get_any_file_path,
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get_saveasfile_path,
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color_aug_changed,
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save_inference_file,
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gradio_advanced_training,
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run_cmd_advanced_training,
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gradio_training,
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gradio_config,
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gradio_source_model,
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run_cmd_training,
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)
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from library.dreambooth_folder_creation_gui import (
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gradio_dreambooth_folder_creation_tab,
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)
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from library.dataset_balancing_gui import gradio_dataset_balancing_tab
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from library.utilities import utilities_tab
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from library.merge_lora_gui import gradio_merge_lora_tab
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from library.verify_lora_gui import gradio_verify_lora_tab
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from easygui import msgbox
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folder_symbol = '\U0001f4c2' # 📂
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refresh_symbol = '\U0001f504' # 🔄
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save_style_symbol = '\U0001f4be' # 💾
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document_symbol = '\U0001F4C4' # 📄
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def save_configuration(
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save_as,
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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epoch,
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save_every_n_epochs,
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mixed_precision,
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save_precision,
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seed,
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num_cpu_threads_per_process,
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cache_latents,
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caption_extension,
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enable_bucket,
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gradient_checkpointing,
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full_fp16,
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no_token_padding,
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stop_text_encoder_training,
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use_8bit_adam,
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xformers,
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save_model_as,
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shuffle_caption,
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save_state,
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resume,
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prior_loss_weight,
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text_encoder_lr,
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unet_lr,
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network_dim,
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lora_network_weights,
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color_aug,
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flip_aug,
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clip_skip,
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gradient_accumulation_steps,
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mem_eff_attn,
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output_name,
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model_list,
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max_token_length,
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max_train_epochs,
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max_data_loader_n_workers,
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network_alpha,
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training_comment,
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):
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# Get list of function parameters and values
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parameters = list(locals().items())
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original_file_path = file_path
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save_as_bool = True if save_as.get('label') == 'True' else False
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if save_as_bool:
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print('Save as...')
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file_path = get_saveasfile_path(file_path)
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else:
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print('Save...')
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if file_path == None or file_path == '':
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file_path = get_saveasfile_path(file_path)
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# print(file_path)
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if file_path == None or file_path == '':
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return original_file_path # In case a file_path was provided and the user decide to cancel the open action
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# Return the values of the variables as a dictionary
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variables = {
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name: value
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for name, value in parameters # locals().items()
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if name
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not in [
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'file_path',
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'save_as',
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]
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}
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# Save the data to the selected file
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with open(file_path, 'w') as file:
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json.dump(variables, file, indent=2)
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return file_path
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def open_configuration(
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file_path,
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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epoch,
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save_every_n_epochs,
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mixed_precision,
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save_precision,
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seed,
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num_cpu_threads_per_process,
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cache_latents,
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caption_extension,
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enable_bucket,
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gradient_checkpointing,
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full_fp16,
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no_token_padding,
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stop_text_encoder_training,
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use_8bit_adam,
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xformers,
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save_model_as_dropdown,
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shuffle_caption,
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save_state,
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resume,
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prior_loss_weight,
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text_encoder_lr,
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unet_lr,
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network_dim,
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lora_network_weights,
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color_aug,
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flip_aug,
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clip_skip,
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gradient_accumulation_steps,
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mem_eff_attn,
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output_name,
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model_list,
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max_token_length,
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max_train_epochs,
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max_data_loader_n_workers,
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network_alpha,
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training_comment,
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):
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# Get list of function parameters and values
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parameters = list(locals().items())
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original_file_path = file_path
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file_path = get_file_path(file_path)
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if not file_path == '' and not file_path == None:
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# load variables from JSON file
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with open(file_path, 'r') as f:
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my_data = json.load(f)
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print('Loading config...')
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else:
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file_path = original_file_path # In case a file_path was provided and the user decide to cancel the open action
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my_data = {}
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values = [file_path]
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for key, value in parameters:
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# Set the value in the dictionary to the corresponding value in `my_data`, or the default value if not found
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if not key in ['file_path']:
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values.append(my_data.get(key, value))
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return tuple(values)
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def train_model(
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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logging_dir,
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train_data_dir,
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reg_data_dir,
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output_dir,
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max_resolution,
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learning_rate,
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lr_scheduler,
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lr_warmup,
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train_batch_size,
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epoch,
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save_every_n_epochs,
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mixed_precision,
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save_precision,
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seed,
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num_cpu_threads_per_process,
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cache_latents,
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caption_extension,
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enable_bucket,
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gradient_checkpointing,
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full_fp16,
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no_token_padding,
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stop_text_encoder_training_pct,
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use_8bit_adam,
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xformers,
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save_model_as,
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shuffle_caption,
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save_state,
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resume,
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prior_loss_weight,
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text_encoder_lr,
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unet_lr,
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network_dim,
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lora_network_weights,
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color_aug,
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flip_aug,
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clip_skip,
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gradient_accumulation_steps,
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mem_eff_attn,
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output_name,
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model_list, # Keep this. Yes, it is unused here but required given the common list used
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max_token_length,
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max_train_epochs,
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max_data_loader_n_workers,
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network_alpha,
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training_comment,
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):
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if pretrained_model_name_or_path == '':
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msgbox('Source model information is missing')
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return
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if train_data_dir == '':
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msgbox('Image folder path is missing')
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return
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if not os.path.exists(train_data_dir):
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msgbox('Image folder does not exist')
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return
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if reg_data_dir != '':
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if not os.path.exists(reg_data_dir):
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msgbox('Regularisation folder does not exist')
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return
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if output_dir == '':
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msgbox('Output folder path is missing')
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return
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if not os.path.exists(output_dir):
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os.makedirs(output_dir)
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if stop_text_encoder_training_pct > 0:
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msgbox('Output "stop text encoder training" is not yet supported. Ignoring')
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stop_text_encoder_training_pct = 0
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# If string is empty set string to 0.
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if text_encoder_lr == '':
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text_encoder_lr = 0
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if unet_lr == '':
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unet_lr = 0
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if (float(text_encoder_lr) == 0) and (float(unet_lr) == 0):
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msgbox(
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'At least one Learning Rate value for "Text encoder" or "Unet" need to be provided'
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)
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return
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# Get a list of all subfolders in train_data_dir
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subfolders = [
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f
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for f in os.listdir(train_data_dir)
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if os.path.isdir(os.path.join(train_data_dir, f))
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]
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total_steps = 0
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# Loop through each subfolder and extract the number of repeats
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for folder in subfolders:
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# Extract the number of repeats from the folder name
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repeats = int(folder.split('_')[0])
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# Count the number of images in the folder
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num_images = len(
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[
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f
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for f in os.listdir(os.path.join(train_data_dir, folder))
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if f.endswith('.jpg')
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or f.endswith('.jpeg')
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or f.endswith('.png')
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or f.endswith('.webp')
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]
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)
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# Calculate the total number of steps for this folder
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steps = repeats * num_images
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total_steps += steps
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# Print the result
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print(f'Folder {folder}: {steps} steps')
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# calculate max_train_steps
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max_train_steps = int(
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math.ceil(
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float(total_steps)
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/ int(train_batch_size)
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* int(epoch)
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# * int(reg_factor)
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)
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)
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print(f'max_train_steps = {max_train_steps}')
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# calculate stop encoder training
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if stop_text_encoder_training_pct == None:
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stop_text_encoder_training = 0
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else:
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stop_text_encoder_training = math.ceil(
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float(max_train_steps) / 100 * int(stop_text_encoder_training_pct)
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)
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print(f'stop_text_encoder_training = {stop_text_encoder_training}')
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lr_warmup_steps = round(float(int(lr_warmup) * int(max_train_steps) / 100))
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print(f'lr_warmup_steps = {lr_warmup_steps}')
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run_cmd = f'accelerate launch --num_cpu_threads_per_process={num_cpu_threads_per_process} "train_network.py"'
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if v2:
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run_cmd += ' --v2'
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if v_parameterization:
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run_cmd += ' --v_parameterization'
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if enable_bucket:
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run_cmd += ' --enable_bucket'
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if no_token_padding:
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run_cmd += ' --no_token_padding'
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run_cmd += (
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f' --pretrained_model_name_or_path="{pretrained_model_name_or_path}"'
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)
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run_cmd += f' --train_data_dir="{train_data_dir}"'
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if len(reg_data_dir):
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run_cmd += f' --reg_data_dir="{reg_data_dir}"'
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run_cmd += f' --resolution={max_resolution}'
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run_cmd += f' --output_dir="{output_dir}"'
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run_cmd += f' --logging_dir="{logging_dir}"'
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run_cmd += f' --network_alpha="{network_alpha}"'
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if not training_comment == '':
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run_cmd += f' --training_comment="{training_comment}"'
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if not stop_text_encoder_training == 0:
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run_cmd += (
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f' --stop_text_encoder_training={stop_text_encoder_training}'
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)
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if not save_model_as == 'same as source model':
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run_cmd += f' --save_model_as={save_model_as}'
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if not float(prior_loss_weight) == 1.0:
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run_cmd += f' --prior_loss_weight={prior_loss_weight}'
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run_cmd += f' --network_module=networks.lora'
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if not float(text_encoder_lr) == 0:
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run_cmd += f' --text_encoder_lr={text_encoder_lr}'
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else:
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run_cmd += f' --network_train_unet_only'
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if not float(unet_lr) == 0:
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run_cmd += f' --unet_lr={unet_lr}'
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else:
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run_cmd += f' --network_train_text_encoder_only'
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run_cmd += f' --network_dim={network_dim}'
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if not lora_network_weights == '':
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run_cmd += f' --network_weights="{lora_network_weights}"'
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if int(gradient_accumulation_steps) > 1:
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run_cmd += f' --gradient_accumulation_steps={int(gradient_accumulation_steps)}'
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if not output_name == '':
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run_cmd += f' --output_name="{output_name}"'
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run_cmd += run_cmd_training(
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learning_rate=learning_rate,
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lr_scheduler=lr_scheduler,
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lr_warmup_steps=lr_warmup_steps,
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train_batch_size=train_batch_size,
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max_train_steps=max_train_steps,
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save_every_n_epochs=save_every_n_epochs,
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mixed_precision=mixed_precision,
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save_precision=save_precision,
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seed=seed,
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caption_extension=caption_extension,
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cache_latents=cache_latents,
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)
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run_cmd += run_cmd_advanced_training(
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max_train_epochs=max_train_epochs,
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max_data_loader_n_workers=max_data_loader_n_workers,
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max_token_length=max_token_length,
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resume=resume,
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save_state=save_state,
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mem_eff_attn=mem_eff_attn,
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clip_skip=clip_skip,
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flip_aug=flip_aug,
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color_aug=color_aug,
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shuffle_caption=shuffle_caption,
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gradient_checkpointing=gradient_checkpointing,
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full_fp16=full_fp16,
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xformers=xformers,
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use_8bit_adam=use_8bit_adam,
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)
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print(run_cmd)
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# Run the command
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subprocess.run(run_cmd)
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# check if output_dir/last is a folder... therefore it is a diffuser model
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last_dir = pathlib.Path(f'{output_dir}/{output_name}')
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if not last_dir.is_dir():
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# Copy inference model for v2 if required
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save_inference_file(output_dir, v2, v_parameterization, output_name)
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def UI(username, password):
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css = ''
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if os.path.exists('./style.css'):
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with open(os.path.join('./style.css'), 'r', encoding='utf8') as file:
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print('Load CSS...')
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css += file.read() + '\n'
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interface = gr.Blocks(css=css)
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with interface:
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with gr.Tab('LoRA'):
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(
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train_data_dir_input,
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reg_data_dir_input,
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output_dir_input,
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logging_dir_input,
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) = lora_tab()
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with gr.Tab('Utilities'):
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utilities_tab(
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train_data_dir_input=train_data_dir_input,
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reg_data_dir_input=reg_data_dir_input,
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output_dir_input=output_dir_input,
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logging_dir_input=logging_dir_input,
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enable_copy_info_button=True,
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)
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# Show the interface
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if not username == '':
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interface.launch(auth=(username, password))
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else:
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interface.launch()
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def lora_tab(
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train_data_dir_input=gr.Textbox(),
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reg_data_dir_input=gr.Textbox(),
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output_dir_input=gr.Textbox(),
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logging_dir_input=gr.Textbox(),
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):
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dummy_db_true = gr.Label(value=True, visible=False)
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dummy_db_false = gr.Label(value=False, visible=False)
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gr.Markdown(
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'Train a custom model using kohya train network LoRA python code...'
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)
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(
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button_open_config,
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button_save_config,
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button_save_as_config,
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config_file_name,
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) = gradio_config()
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(
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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save_model_as,
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model_list,
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) = gradio_source_model()
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with gr.Tab('Folders'):
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with gr.Row():
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train_data_dir = gr.Textbox(
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label='Image folder',
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placeholder='Folder where the training folders containing the images are located',
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)
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train_data_dir_folder = gr.Button('📂', elem_id='open_folder_small')
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train_data_dir_folder.click(
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get_folder_path, outputs=train_data_dir
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)
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reg_data_dir = gr.Textbox(
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label='Regularisation folder',
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placeholder='(Optional) Folder where where the regularization folders containing the images are located',
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)
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reg_data_dir_folder = gr.Button('📂', elem_id='open_folder_small')
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reg_data_dir_folder.click(get_folder_path, outputs=reg_data_dir)
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with gr.Row():
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output_dir = gr.Textbox(
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label='Output folder',
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placeholder='Folder to output trained model',
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)
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output_dir_folder = gr.Button('📂', elem_id='open_folder_small')
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output_dir_folder.click(get_folder_path, outputs=output_dir)
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logging_dir = gr.Textbox(
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label='Logging folder',
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placeholder='Optional: enable logging and output TensorBoard log to this folder',
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)
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logging_dir_folder = gr.Button('📂', elem_id='open_folder_small')
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logging_dir_folder.click(get_folder_path, outputs=logging_dir)
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with gr.Row():
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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_network_weights = gr.Textbox(
|
|
label='LoRA network weights',
|
|
placeholder='{Optional) Path to existing LoRA network weights to resume training',
|
|
)
|
|
lora_network_weights_file = gr.Button(
|
|
document_symbol, elem_id='open_folder_small'
|
|
)
|
|
lora_network_weights_file.click(
|
|
get_any_file_path,
|
|
inputs=[lora_network_weights],
|
|
outputs=lora_network_weights,
|
|
)
|
|
(
|
|
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,
|
|
) = gradio_training(
|
|
learning_rate_value='1e-5',
|
|
lr_scheduler_value='cosine',
|
|
lr_warmup_value='10',
|
|
)
|
|
with gr.Row():
|
|
text_encoder_lr = gr.Textbox(
|
|
label='Text Encoder learning rate',
|
|
value='5e-5',
|
|
placeholder='Optional',
|
|
)
|
|
unet_lr = gr.Textbox(
|
|
label='Unet learning rate',
|
|
value='1e-3',
|
|
placeholder='Optional',
|
|
)
|
|
network_dim = gr.Slider(
|
|
minimum=1,
|
|
maximum=128,
|
|
label='Network Rank (Dimension)',
|
|
value=8,
|
|
step=1,
|
|
interactive=True,
|
|
)
|
|
network_alpha = gr.Slider(
|
|
minimum=1,
|
|
maximum=128,
|
|
label='Network Alpha',
|
|
value=1,
|
|
step=1,
|
|
interactive=True,
|
|
)
|
|
with gr.Row():
|
|
max_resolution = gr.Textbox(
|
|
label='Max resolution',
|
|
value='512,512',
|
|
placeholder='512,512',
|
|
)
|
|
stop_text_encoder_training = gr.Slider(
|
|
minimum=0,
|
|
maximum=100,
|
|
value=0,
|
|
step=1,
|
|
label='Stop text encoder training',
|
|
)
|
|
enable_bucket = gr.Checkbox(label='Enable buckets', value=True)
|
|
with gr.Accordion('Advanced Configuration', open=False):
|
|
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'
|
|
)
|
|
with gr.Row():
|
|
prior_loss_weight = gr.Number(
|
|
label='Prior loss weight', value=1.0
|
|
)
|
|
(
|
|
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,
|
|
) = gradio_advanced_training()
|
|
color_aug.change(
|
|
color_aug_changed,
|
|
inputs=[color_aug],
|
|
outputs=[cache_latents],
|
|
)
|
|
|
|
with gr.Tab('Tools'):
|
|
gr.Markdown(
|
|
'This section provide Dreambooth tools to help setup your dataset...'
|
|
)
|
|
gradio_dreambooth_folder_creation_tab(
|
|
train_data_dir_input=train_data_dir,
|
|
reg_data_dir_input=reg_data_dir,
|
|
output_dir_input=output_dir,
|
|
logging_dir_input=logging_dir,
|
|
)
|
|
gradio_dataset_balancing_tab()
|
|
gradio_merge_lora_tab()
|
|
gradio_verify_lora_tab()
|
|
|
|
|
|
button_run = gr.Button('Train model')
|
|
|
|
settings_list = [
|
|
pretrained_model_name_or_path,
|
|
v2,
|
|
v_parameterization,
|
|
logging_dir,
|
|
train_data_dir,
|
|
reg_data_dir,
|
|
output_dir,
|
|
max_resolution,
|
|
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,
|
|
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,
|
|
]
|
|
|
|
button_open_config.click(
|
|
open_configuration,
|
|
inputs=[config_file_name] + settings_list,
|
|
outputs=[config_file_name] + settings_list,
|
|
)
|
|
|
|
button_save_config.click(
|
|
save_configuration,
|
|
inputs=[dummy_db_false, config_file_name] + settings_list,
|
|
outputs=[config_file_name],
|
|
)
|
|
|
|
button_save_as_config.click(
|
|
save_configuration,
|
|
inputs=[dummy_db_true, config_file_name] + settings_list,
|
|
outputs=[config_file_name],
|
|
)
|
|
|
|
button_run.click(
|
|
train_model,
|
|
inputs=settings_list,
|
|
)
|
|
|
|
return (
|
|
train_data_dir,
|
|
reg_data_dir,
|
|
output_dir,
|
|
logging_dir,
|
|
)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
# torch.cuda.set_per_process_memory_fraction(0.48)
|
|
parser = argparse.ArgumentParser()
|
|
parser.add_argument(
|
|
'--username', type=str, default='', help='Username for authentication'
|
|
)
|
|
parser.add_argument(
|
|
'--password', type=str, default='', help='Password for authentication'
|
|
)
|
|
|
|
args = parser.parse_args()
|
|
|
|
UI(username=args.username, password=args.password)
|