800 lines
25 KiB
Python
800 lines
25 KiB
Python
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 shutil
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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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get_file_path,
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get_saveasfile_path,
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)
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from library.utilities import utilities_tab
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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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train_dir,
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image_folder,
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output_dir,
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logging_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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dataset_repeats,
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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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train_text_encoder,
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create_buckets,
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create_caption,
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save_model_as,
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caption_extension,
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):
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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:
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return original_file_path
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# Return the values of the variables as a dictionary
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variables = {
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'pretrained_model_name_or_path': pretrained_model_name_or_path,
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'v2': v2,
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'v_parameterization': v_parameterization,
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'train_dir': train_dir,
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'image_folder': image_folder,
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'output_dir': output_dir,
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'logging_dir': logging_dir,
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'max_resolution': max_resolution,
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'learning_rate': learning_rate,
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'lr_scheduler': lr_scheduler,
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'lr_warmup': lr_warmup,
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'dataset_repeats': dataset_repeats,
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'train_batch_size': train_batch_size,
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'epoch': epoch,
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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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'num_cpu_threads_per_process': num_cpu_threads_per_process,
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'train_text_encoder': train_text_encoder,
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'create_buckets': create_buckets,
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'create_caption': create_caption,
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'save_model_as': save_model_as,
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'caption_extension': caption_extension,
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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)
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return file_path
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def open_config_file(
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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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train_dir,
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image_folder,
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output_dir,
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logging_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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dataset_repeats,
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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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train_text_encoder,
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create_buckets,
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create_caption,
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save_model_as,
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caption_extension,
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):
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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 file_path != '' and file_path != None:
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print(file_path)
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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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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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# Return the values of the variables as a dictionary
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return (
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file_path,
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my_data.get(
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'pretrained_model_name_or_path', pretrained_model_name_or_path
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),
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my_data.get('v2', v2),
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my_data.get('v_parameterization', v_parameterization),
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my_data.get('train_dir', train_dir),
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my_data.get('image_folder', image_folder),
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my_data.get('output_dir', output_dir),
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my_data.get('logging_dir', logging_dir),
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my_data.get('max_resolution', max_resolution),
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my_data.get('learning_rate', learning_rate),
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my_data.get('lr_scheduler', lr_scheduler),
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my_data.get('lr_warmup', lr_warmup),
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my_data.get('dataset_repeats', dataset_repeats),
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my_data.get('train_batch_size', train_batch_size),
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my_data.get('epoch', epoch),
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my_data.get('save_every_n_epochs', save_every_n_epochs),
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my_data.get('mixed_precision', mixed_precision),
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my_data.get('save_precision', save_precision),
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my_data.get('seed', seed),
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my_data.get(
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'num_cpu_threads_per_process', num_cpu_threads_per_process
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),
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my_data.get('train_text_encoder', train_text_encoder),
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my_data.get('create_buckets', create_buckets),
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my_data.get('create_caption', create_caption),
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my_data.get('save_model_as', save_model_as),
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my_data.get('caption_extension', caption_extension),
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)
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def train_model(
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generate_caption_database,
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generate_image_buckets,
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pretrained_model_name_or_path,
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v2,
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v_parameterization,
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train_dir,
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image_folder,
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output_dir,
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logging_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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dataset_repeats,
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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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train_text_encoder,
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save_model_as,
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caption_extension,
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):
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def save_inference_file(output_dir, v2, v_parameterization):
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# Copy inference model for v2 if required
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if v2 and v_parameterization:
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print(f'Saving v2-inference-v.yaml as {output_dir}/last.yaml')
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shutil.copy(
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f'./v2_inference/v2-inference-v.yaml',
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f'{output_dir}/last.yaml',
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)
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elif v2:
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print(f'Saving v2-inference.yaml as {output_dir}/last.yaml')
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shutil.copy(
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f'./v2_inference/v2-inference.yaml',
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f'{output_dir}/last.yaml',
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)
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# create caption json file
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if generate_caption_database:
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if not os.path.exists(train_dir):
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os.mkdir(train_dir)
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run_cmd = (
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f'./venv/Scripts/python.exe finetune/merge_captions_to_metadata.py'
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)
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if caption_extension == '':
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run_cmd += f' --caption_extension=".txt"'
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else:
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run_cmd += f' --caption_extension={caption_extension}'
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run_cmd += f' {image_folder}'
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run_cmd += f' {train_dir}/meta_cap.json'
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run_cmd += f' --full_path'
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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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# create images buckets
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if generate_image_buckets:
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command = [
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'./venv/Scripts/python.exe',
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'finetune/prepare_buckets_latents.py',
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image_folder,
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'{}/meta_cap.json'.format(train_dir),
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'{}/meta_lat.json'.format(train_dir),
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pretrained_model_name_or_path,
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'--batch_size',
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'4',
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'--max_resolution',
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max_resolution,
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'--mixed_precision',
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mixed_precision,
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'--full_path',
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]
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print(command)
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# Run the command
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subprocess.run(command)
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image_num = len(
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[f for f in os.listdir(image_folder) if f.endswith('.npz')]
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)
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print(f'image_num = {image_num}')
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repeats = int(image_num) * int(dataset_repeats)
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print(f'repeats = {str(repeats)}')
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# calculate max_train_steps
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max_train_steps = int(
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math.ceil(float(repeats) / int(train_batch_size) * int(epoch))
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)
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print(f'max_train_steps = {max_train_steps}')
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lr_warmup_steps = round(
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float(int(lr_warmup) * int(max_train_steps) / 100)
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)
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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} "./fine_tune.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 train_text_encoder:
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run_cmd += ' --train_text_encoder'
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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' --in_json={train_dir}/meta_lat.json'
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run_cmd += f' --train_data_dir={image_folder}'
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run_cmd += f' --output_dir={output_dir}'
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if not logging_dir == '':
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run_cmd += f' --logging_dir={logging_dir}'
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run_cmd += f' --train_batch_size={train_batch_size}'
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run_cmd += f' --dataset_repeats={dataset_repeats}'
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run_cmd += f' --learning_rate={learning_rate}'
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run_cmd += f' --lr_scheduler={lr_scheduler}'
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run_cmd += f' --lr_warmup_steps={lr_warmup_steps}'
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run_cmd += f' --max_train_steps={max_train_steps}'
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run_cmd += f' --use_8bit_adam'
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run_cmd += f' --xformers'
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run_cmd += f' --mixed_precision={mixed_precision}'
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run_cmd += f' --save_every_n_epochs={save_every_n_epochs}'
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run_cmd += f' --seed={seed}'
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run_cmd += f' --save_precision={save_precision}'
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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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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}/last')
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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)
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def set_pretrained_model_name_or_path_input(value, v2, v_parameterization):
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# define a list of substrings to search for
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substrings_v2 = [
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'stabilityai/stable-diffusion-2-1-base',
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'stabilityai/stable-diffusion-2-base',
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]
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# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v2 list
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if str(value) in substrings_v2:
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print('SD v2 model detected. Setting --v2 parameter')
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v2 = True
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v_parameterization = False
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return value, v2, v_parameterization
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# define a list of substrings to search for v-objective
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substrings_v_parameterization = [
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'stabilityai/stable-diffusion-2-1',
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'stabilityai/stable-diffusion-2',
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]
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# check if $v2 and $v_parameterization are empty and if $pretrained_model_name_or_path contains any of the substrings in the v_parameterization list
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if str(value) in substrings_v_parameterization:
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print(
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'SD v2 v_parameterization detected. Setting --v2 parameter and --v_parameterization'
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)
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v2 = True
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v_parameterization = True
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return value, v2, v_parameterization
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# define a list of substrings to v1.x
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substrings_v1_model = [
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'CompVis/stable-diffusion-v1-4',
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'runwayml/stable-diffusion-v1-5',
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]
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if str(value) in substrings_v1_model:
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v2 = False
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v_parameterization = False
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return value, v2, v_parameterization
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if value == 'custom':
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value = ''
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v2 = False
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v_parameterization = False
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return value, v2, v_parameterization
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def remove_doublequote(file_path):
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if file_path != None:
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file_path = file_path.replace('"', '')
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return file_path
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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("Finetune"):
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finetune_tab()
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with gr.Tab("Utilities"):
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utilities_tab(enable_dreambooth_tab=False)
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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 finetune_tab():
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dummy_ft_true = gr.Label(value=True, visible=False)
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dummy_ft_false = gr.Label(value=False, visible=False)
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gr.Markdown(
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'Enter kohya finetune training parameter using this interface.'
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)
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with gr.Accordion('Configuration File Load/Save', open=False):
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with gr.Row():
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button_open_config = gr.Button(
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f'Open {folder_symbol}', elem_id='open_folder'
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)
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button_save_config = gr.Button(
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f'Save {save_style_symbol}', elem_id='open_folder'
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)
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button_save_as_config = gr.Button(
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f'Save as... {save_style_symbol}',
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elem_id='open_folder',
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)
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config_file_name = gr.Textbox(
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label='', placeholder='type file path or use buttons...'
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)
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config_file_name.change(
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remove_doublequote,
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inputs=[config_file_name],
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outputs=[config_file_name],
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)
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with gr.Tab('Source model'):
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# Define the input elements
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with gr.Row():
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pretrained_model_name_or_path_input = gr.Textbox(
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label='Pretrained model name or path',
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placeholder='enter the path to custom model or name of pretrained model',
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)
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pretrained_model_name_or_path_file = gr.Button(
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document_symbol, elem_id='open_folder_small'
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)
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pretrained_model_name_or_path_file.click(
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get_file_path,
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inputs=pretrained_model_name_or_path_input,
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outputs=pretrained_model_name_or_path_input,
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)
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pretrained_model_name_or_path_folder = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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pretrained_model_name_or_path_folder.click(
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get_folder_path,
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inputs=pretrained_model_name_or_path_input,
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outputs=pretrained_model_name_or_path_input,
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)
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model_list = gr.Dropdown(
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label='(Optional) Model Quick Pick',
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choices=[
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'custom',
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'stabilityai/stable-diffusion-2-1-base',
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'stabilityai/stable-diffusion-2-base',
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'stabilityai/stable-diffusion-2-1',
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'stabilityai/stable-diffusion-2',
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'runwayml/stable-diffusion-v1-5',
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'CompVis/stable-diffusion-v1-4',
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],
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)
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save_model_as_dropdown = gr.Dropdown(
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label='Save trained model as',
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choices=[
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'same as source model',
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'ckpt',
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'diffusers',
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'diffusers_safetensors',
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'safetensors',
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],
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value='same as source model',
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)
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with gr.Row():
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v2_input = gr.Checkbox(label='v2', value=True)
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v_parameterization_input = gr.Checkbox(
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label='v_parameterization', value=False
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)
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model_list.change(
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set_pretrained_model_name_or_path_input,
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inputs=[model_list, v2_input, v_parameterization_input],
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outputs=[
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pretrained_model_name_or_path_input,
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v2_input,
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v_parameterization_input,
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],
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)
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with gr.Tab('Directories'):
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with gr.Row():
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train_dir_input = gr.Textbox(
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label='Training config folder',
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placeholder='folder where the training configuration files will be saved',
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)
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train_dir_folder = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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train_dir_folder.click(
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get_folder_path, outputs=train_dir_input
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)
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image_folder_input = gr.Textbox(
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label='Training Image folder',
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placeholder='folder where the training images are located',
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)
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image_folder_input_folder = gr.Button(
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folder_symbol, elem_id='open_folder_small'
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)
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image_folder_input_folder.click(
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get_folder_path, outputs=image_folder_input
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)
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with gr.Row():
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|
output_dir_input = gr.Textbox(
|
|
label='Output folder',
|
|
placeholder='folder where the model will be saved',
|
|
)
|
|
output_dir_input_folder = gr.Button(
|
|
folder_symbol, elem_id='open_folder_small'
|
|
)
|
|
output_dir_input_folder.click(
|
|
get_folder_path, outputs=output_dir_input
|
|
)
|
|
|
|
logging_dir_input = gr.Textbox(
|
|
label='Logging folder',
|
|
placeholder='Optional: enable logging and output TensorBoard log to this folder',
|
|
)
|
|
logging_dir_input_folder = gr.Button(
|
|
folder_symbol, elem_id='open_folder_small'
|
|
)
|
|
logging_dir_input_folder.click(
|
|
get_folder_path, outputs=logging_dir_input
|
|
)
|
|
train_dir_input.change(
|
|
remove_doublequote,
|
|
inputs=[train_dir_input],
|
|
outputs=[train_dir_input],
|
|
)
|
|
image_folder_input.change(
|
|
remove_doublequote,
|
|
inputs=[image_folder_input],
|
|
outputs=[image_folder_input],
|
|
)
|
|
output_dir_input.change(
|
|
remove_doublequote,
|
|
inputs=[output_dir_input],
|
|
outputs=[output_dir_input],
|
|
)
|
|
with gr.Tab('Training parameters'):
|
|
with gr.Row():
|
|
learning_rate_input = gr.Textbox(
|
|
label='Learning rate', value=1e-6
|
|
)
|
|
lr_scheduler_input = gr.Dropdown(
|
|
label='LR Scheduler',
|
|
choices=[
|
|
'constant',
|
|
'constant_with_warmup',
|
|
'cosine',
|
|
'cosine_with_restarts',
|
|
'linear',
|
|
'polynomial',
|
|
],
|
|
value='constant',
|
|
)
|
|
lr_warmup_input = gr.Textbox(label='LR warmup', value=0)
|
|
with gr.Row():
|
|
dataset_repeats_input = gr.Textbox(
|
|
label='Dataset repeats', value=40
|
|
)
|
|
train_batch_size_input = gr.Slider(
|
|
minimum=1,
|
|
maximum=32,
|
|
label='Train batch size',
|
|
value=1,
|
|
step=1,
|
|
)
|
|
epoch_input = gr.Textbox(label='Epoch', value=1)
|
|
save_every_n_epochs_input = gr.Textbox(
|
|
label='Save every N epochs', value=1
|
|
)
|
|
with gr.Row():
|
|
mixed_precision_input = gr.Dropdown(
|
|
label='Mixed precision',
|
|
choices=[
|
|
'no',
|
|
'fp16',
|
|
'bf16',
|
|
],
|
|
value='fp16',
|
|
)
|
|
save_precision_input = gr.Dropdown(
|
|
label='Save precision',
|
|
choices=[
|
|
'float',
|
|
'fp16',
|
|
'bf16',
|
|
],
|
|
value='fp16',
|
|
)
|
|
num_cpu_threads_per_process_input = gr.Slider(
|
|
minimum=1,
|
|
maximum=os.cpu_count(),
|
|
step=1,
|
|
label='Number of CPU threads per process',
|
|
value=os.cpu_count(),
|
|
)
|
|
with gr.Row():
|
|
seed_input = gr.Textbox(label='Seed', value=1234)
|
|
max_resolution_input = gr.Textbox(
|
|
label='Max resolution', value='512,512'
|
|
)
|
|
with gr.Row():
|
|
caption_extention_input = gr.Textbox(
|
|
label='Caption Extension',
|
|
placeholder='(Optional) Extension for caption files. default: .txt',
|
|
)
|
|
train_text_encoder_input = gr.Checkbox(
|
|
label='Train text encoder', value=True
|
|
)
|
|
with gr.Box():
|
|
with gr.Row():
|
|
create_caption = gr.Checkbox(
|
|
label='Generate caption database', value=True
|
|
)
|
|
create_buckets = gr.Checkbox(
|
|
label='Generate image buckets', value=True
|
|
)
|
|
|
|
button_run = gr.Button('Train model')
|
|
|
|
button_run.click(
|
|
train_model,
|
|
inputs=[
|
|
create_caption,
|
|
create_buckets,
|
|
pretrained_model_name_or_path_input,
|
|
v2_input,
|
|
v_parameterization_input,
|
|
train_dir_input,
|
|
image_folder_input,
|
|
output_dir_input,
|
|
logging_dir_input,
|
|
max_resolution_input,
|
|
learning_rate_input,
|
|
lr_scheduler_input,
|
|
lr_warmup_input,
|
|
dataset_repeats_input,
|
|
train_batch_size_input,
|
|
epoch_input,
|
|
save_every_n_epochs_input,
|
|
mixed_precision_input,
|
|
save_precision_input,
|
|
seed_input,
|
|
num_cpu_threads_per_process_input,
|
|
train_text_encoder_input,
|
|
save_model_as_dropdown,
|
|
caption_extention_input,
|
|
],
|
|
)
|
|
|
|
button_open_config.click(
|
|
open_config_file,
|
|
inputs=[
|
|
config_file_name,
|
|
pretrained_model_name_or_path_input,
|
|
v2_input,
|
|
v_parameterization_input,
|
|
train_dir_input,
|
|
image_folder_input,
|
|
output_dir_input,
|
|
logging_dir_input,
|
|
max_resolution_input,
|
|
learning_rate_input,
|
|
lr_scheduler_input,
|
|
lr_warmup_input,
|
|
dataset_repeats_input,
|
|
train_batch_size_input,
|
|
epoch_input,
|
|
save_every_n_epochs_input,
|
|
mixed_precision_input,
|
|
save_precision_input,
|
|
seed_input,
|
|
num_cpu_threads_per_process_input,
|
|
train_text_encoder_input,
|
|
create_buckets,
|
|
create_caption,
|
|
save_model_as_dropdown,
|
|
caption_extention_input,
|
|
],
|
|
outputs=[
|
|
config_file_name,
|
|
pretrained_model_name_or_path_input,
|
|
v2_input,
|
|
v_parameterization_input,
|
|
train_dir_input,
|
|
image_folder_input,
|
|
output_dir_input,
|
|
logging_dir_input,
|
|
max_resolution_input,
|
|
learning_rate_input,
|
|
lr_scheduler_input,
|
|
lr_warmup_input,
|
|
dataset_repeats_input,
|
|
train_batch_size_input,
|
|
epoch_input,
|
|
save_every_n_epochs_input,
|
|
mixed_precision_input,
|
|
save_precision_input,
|
|
seed_input,
|
|
num_cpu_threads_per_process_input,
|
|
train_text_encoder_input,
|
|
create_buckets,
|
|
create_caption,
|
|
save_model_as_dropdown,
|
|
caption_extention_input,
|
|
],
|
|
)
|
|
|
|
button_save_config.click(
|
|
save_configuration,
|
|
inputs=[
|
|
dummy_ft_false,
|
|
config_file_name,
|
|
pretrained_model_name_or_path_input,
|
|
v2_input,
|
|
v_parameterization_input,
|
|
train_dir_input,
|
|
image_folder_input,
|
|
output_dir_input,
|
|
logging_dir_input,
|
|
max_resolution_input,
|
|
learning_rate_input,
|
|
lr_scheduler_input,
|
|
lr_warmup_input,
|
|
dataset_repeats_input,
|
|
train_batch_size_input,
|
|
epoch_input,
|
|
save_every_n_epochs_input,
|
|
mixed_precision_input,
|
|
save_precision_input,
|
|
seed_input,
|
|
num_cpu_threads_per_process_input,
|
|
train_text_encoder_input,
|
|
create_buckets,
|
|
create_caption,
|
|
save_model_as_dropdown,
|
|
caption_extention_input,
|
|
],
|
|
outputs=[config_file_name],
|
|
)
|
|
|
|
button_save_as_config.click(
|
|
save_configuration,
|
|
inputs=[
|
|
dummy_ft_true,
|
|
config_file_name,
|
|
pretrained_model_name_or_path_input,
|
|
v2_input,
|
|
v_parameterization_input,
|
|
train_dir_input,
|
|
image_folder_input,
|
|
output_dir_input,
|
|
logging_dir_input,
|
|
max_resolution_input,
|
|
learning_rate_input,
|
|
lr_scheduler_input,
|
|
lr_warmup_input,
|
|
dataset_repeats_input,
|
|
train_batch_size_input,
|
|
epoch_input,
|
|
save_every_n_epochs_input,
|
|
mixed_precision_input,
|
|
save_precision_input,
|
|
seed_input,
|
|
num_cpu_threads_per_process_input,
|
|
train_text_encoder_input,
|
|
create_buckets,
|
|
create_caption,
|
|
save_model_as_dropdown,
|
|
caption_extention_input,
|
|
],
|
|
outputs=[config_file_name],
|
|
)
|
|
|
|
|
|
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)
|