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callbacks.py
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callbacks.py
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import math
import os, webbrowser, subprocess, random, time, winreg
import dearpygui.dearpygui as gui
from ast import literal_eval
current_version = "0.27"
default_script = "ltg_default.ini"
app_width = 1000
app_height = 750
lora_tab_instances = 0
active_tab = ""
list_settings = ["pretrained_model_name_or_path", "v_parameterization", "v2", "use_vae", "sdxl", "unet_only_sdxl",
"vae", "train_data_dir", "use_reg_data", "reg_data_dir",
"output_dir", "output_name", "training_duration_method", "max_train_epochs",
"train_time", "train_speed", "train_speed_type", "max_train_steps", "train_batch_size",
"save_every_n_epochs", "save_last_n_epochs", "use_separate_lr",
"learning_rate", "unet_lr", "text_encoder_lr",
"use_custom_scheduler", "scheduler_name", "scheduler_name_string", "lr_scheduler", "scheduler_args",
"lr_warmup_ratio", "resolution", "clip_skip",
"network_dim", "network_alpha", "shuffle_caption", "max_token_length",
"keep_tokens", "seed", "gradient_checkpointing", "gradient_accumulation_steps",
"max_data_loader_n_workers", "save_precision", "mixed_precision", "optimizer_type", "_optimizer_type", "optimizer_args",
"use_custom_optimizer", "optimizer_name", "optimizer_name_string",
"logging_dir", "use_custom_log_prefix", "log_prefix",
"LoCON", "locon_dim", "locon_dim_string", "locon_alpha", "locon_alpha_string",
"LoHA", "loha_dim", "loha_dim_string", "loha_alpha", "loha_alpha_string",
"DyLoRA", "dylora_unit", "dylora_unit_string", "dylora_dim", "dylora_dim_string", "dylora_alpha", "dylora_alpha_string",
"LoKR", "lokr_dim", "lokr_dim_string", "lokr_alpha", "lokr_alpha_string",
"min_snr_gamma", "_offset_noise", "offset_noise", "scale_weight_normals",
"_noise_amount", "noise_amount", "_noise_discount", "noise_discount", "noise_iterations", "_noise_iterations",
"cache_latents", "cache_latents_to_disk", "network_dropout",
"additional_parameters"]
def _help(message):
last_item = gui.last_item()
group = gui.add_group(horizontal = True)
gui.move_item(last_item, parent = group)
gui.capture_next_item(lambda s: gui.move_item(s, parent = group))
t = gui.add_text("(?)", color = [0, 255, 0])
with gui.tooltip(t):
gui.add_text(message)
def _info_popup(message):
if message is not None:
with gui.window(no_move = True, no_collapse = True, no_resize = True, width = app_width / 2,
height = app_height / 2, pos = [200, 137.5], modal = True):
gui.add_text(message)
def import_popup():
gui.show_item("popup_import")
def export_settings():
if get_active_tab() == 0:
_info_popup("Невозможно экспортировать: нет активных\n"
"вкладок.")
return
suffix = append_caller_instance(get_active_tab())
print(get_active_tab())
settings_file = ["version=" + current_version + "\n", "\n", "##### SETTINGS #####\n", "\n"]
for setting in list_settings:
setting_value = gui.get_value(setting + suffix)
if setting_value:
settings_file.append(setting + ": " + str(setting_value) + "\n")
filename = str(gui.get_value("output_name" + suffix)) + "_settings.ini"
with open(filename, "w") as file:
print("Записываем настройки в файл:", filename)
file.writelines(settings_file)
_info_popup("Настройки экспортированы в файл\n"
"" + filename)
def str2bool(v):
return v.lower() in ("yes", "true", "t", "1")
def import_settings(data, destination):
if not get_active_tab():
return
suffix = append_caller_instance(append_instance_number("tab_lora"))
try:
settings_file = open(data, "r").readlines()
except Exception as error:
print("Ошибка при открытии файла:", error)
return
settings_file_version = settings_file[0].strip('\n').split("=")[-1]
try:
if float(settings_file_version) < float(current_version):
print("Файл настроек для версии", settings_file_version)
except ValueError:
pass
print("Импортируем настройки")
is_any_setting_imported = False
for line in settings_file:
if line.count(':'):
line_split = line.split(": ", 1)
else:
continue
item = line_split[0].strip() + suffix
value = line_split[-1].strip()
# print(line_split[0], '=', value)
if gui.does_item_exist(item):
is_any_setting_imported = True
item_type = type(gui.get_value(item))
# print(item, item_type, value)
if item_type == dict:
gui.set_value(item, literal_eval(value))
elif item_type == int:
gui.set_value(item, int(value))
elif item_type == float:
gui.set_value(item, float(value))
elif item_type == str:
if value.lower == 'true' or value.lower == 'false':
gui.set_value(item, str2bool(value))
else:
gui.set_value(item, value)
elif item_type == bool:
gui.set_value(item, str2bool(value))
else:
print("Несовместимая переменная:", item, "=", value, "типа", type(value))
if not is_any_setting_imported:
print("Не удалось импортировать")
def import_from_default_ini(destination):
if os.path.isfile(default_script):
if lora_tab_instances == 1:
print("Найден файл", default_script)
import_settings(default_script, destination)
else:
if lora_tab_instances == 1:
print("Файл", default_script, "не найден")
def sd_scripts_path_to_registry():
input_field = "sd_scripts_path"
software_key = winreg.OpenKeyEx(winreg.HKEY_CURRENT_USER, r"SOFTWARE\\")
key = winreg.CreateKey(software_key, "LoRATrainGUI")
winreg.SetValueEx(key, input_field, 0, winreg.REG_SZ, gui.get_value(input_field))
if key:
winreg.CloseKey(key)
def get_sd_scripts_path_from_registry():
input_field = "sd_scripts_path"
try:
lora_train_gui_key = winreg.OpenKeyEx(winreg.HKEY_CURRENT_USER, r"SOFTWARE\\LoRATrainGUI\\")
except FileNotFoundError:
return
lora_train_gui_key = winreg.OpenKeyEx(winreg.HKEY_CURRENT_USER, r"SOFTWARE\\LoRATrainGUI\\")
gui.set_value(input_field, winreg.QueryValueEx(lora_train_gui_key, input_field)[0])
if lora_train_gui_key:
winreg.CloseKey(lora_train_gui_key)
def path_button(tag, path_type, label):
if path_type == "folder":
callback = path_dialog_show
if path_type == "file":
callback = file_dialog_show
return gui.add_button(tag = tag, label = label, callback = callback)
def calculate_total_images(caller):
suffix = append_caller_instance(caller)
main_image_folder = gui.get_value("train_data_dir" + suffix)
try:
image_dirs = os.listdir(main_image_folder)
total_images = 0
for dir in image_dirs:
parts = dir.split('_', 1)
repeats = int(parts[0])
images_count = 0
for training_file in os.listdir(main_image_folder + "\\" + dir):
if training_file.split('.')[-1] == "jpg" or training_file.split('.')[-1] == \
"jpeg" or training_file.split('.')[-1] == "png" or training_file.split('.')[-1] == "webp" or \
training_file.split('.')[-1] == "bmp":
images_count += 1
total_images += repeats * images_count
return total_images
except FileNotFoundError:
# print("Укажите папку с изображениями!")
return 0
except ValueError:
return -1
def append_instance_number(tag_string):
return tag_string + "_" + str(lora_tab_instances)
def get_caller_instance(caller_string):
# наворотил хуйни
# return re.split(r'^[a-zA-Z_]+', caller_string)[-1]
return str(caller_string).split("_")[-1]
def append_caller_instance(caller_instance):
return "_" + get_caller_instance(caller_instance)
def lora_tab_update_name(caller):
suffix = append_caller_instance(caller)
gui.set_item_label("tab_lora" + suffix, gui.get_value("output_name" + suffix))
if gui.get_value("output_name" + suffix) == "":
gui.set_item_label("tab_lora" + suffix, "anon (" + get_caller_instance(caller) + ")")
def calculate_lora_tab_count():
i = 0
lora_tab_name_list = []
for lora_tab in gui.get_item_children("tab_bar_main_panel", 1):
if not gui.is_item_shown(lora_tab) and gui.get_item_info(lora_tab)["container"]:
gui.delete_item(lora_tab)
elif not gui.get_item_info(lora_tab)["container"]:
pass
else:
i += 1
gui.configure_item("combo_lora_list", items = lora_tab_name_list)
return i
def remove_trailing_slashes(path):
return path.rstrip('\\/')
def separate_lr(caller):
suffix = append_caller_instance(caller)
if gui.get_value("use_separate_lr" + suffix):
gui.show_item("group_custom_lr" + suffix)
gui.hide_item("group_main_lr" + suffix)
if not gui.get_value("use_separate_lr" + suffix):
gui.show_item("group_main_lr" + suffix)
gui.hide_item("group_custom_lr" + suffix)
def custom_scheduler_name(caller):
suffix = append_caller_instance(caller)
if gui.get_value("use_custom_scheduler" + suffix):
gui.show_item("scheduler_name" + suffix)
gui.hide_item("scheduler" + suffix)
if not gui.get_value("use_custom_scheduler" + suffix):
gui.show_item("scheduler" + suffix)
gui.hide_item("scheduler_name" + suffix)
gui.set_value("scheduler_name_string" + suffix, "")
def custom_optimizer_name(caller):
suffix = append_caller_instance(caller)
if gui.get_value("use_custom_optimizer" + suffix):
gui.show_item("optimizer_name" + suffix)
gui.hide_item("_optimizer_type" + suffix)
gui.set_value("optimizer_type" + suffix, "")
if not gui.get_value("use_custom_optimizer" + suffix):
gui.show_item("_optimizer_type" + suffix)
gui.hide_item("optimizer_name" + suffix)
gui.set_value("optimizer_name_string" + suffix, "")
def locon(caller):
suffix = append_caller_instance(caller)
if gui.get_value("LoCON" + suffix):
gui.show_item("locon_dim" + suffix)
gui.show_item("locon_alpha" + suffix)
gui.set_value("DyLoRA" + suffix, False)
dylora(suffix)
gui.set_value("LoHA" + suffix, False)
loha(suffix)
gui.set_value("LoKR" + suffix, False)
lokr(suffix)
if not gui.get_value("LoCON" + suffix):
gui.hide_item("locon_dim" + suffix)
gui.hide_item("locon_alpha" + suffix)
gui.set_value("locon_dim_string" + suffix, "")
gui.set_value("locon_alpha_string" + suffix, "")
def loha(caller):
suffix = append_caller_instance(caller)
if gui.get_value("LoHA" + suffix):
gui.show_item("loha_dim" + suffix)
gui.show_item("loha_alpha" + suffix)
gui.set_value("DyLoRA" + suffix, False)
dylora(suffix)
gui.set_value("LoCON" + suffix, False)
locon(suffix)
gui.set_value("LoKR" + suffix, False)
lokr(suffix)
if not gui.get_value("LoHA" + suffix):
gui.hide_item("loha_dim" + suffix)
gui.hide_item("loha_alpha" + suffix)
gui.set_value("loha_dim_string" + suffix, "")
gui.set_value("loha_alpha_string" + suffix, "")
def dylora(caller):
suffix = append_caller_instance(caller)
if gui.get_value("DyLoRA" + suffix):
gui.show_item("dylora_dim" + suffix)
gui.show_item("dylora_alpha" + suffix)
gui.show_item("dylora_unit" + suffix)
gui.set_value("LoCON" + suffix, False)
locon(suffix)
gui.set_value("LoHA" + suffix, False)
loha(suffix)
gui.set_value("LoKR" + suffix, False)
lokr(suffix)
if not gui.get_value("DyLoRA" + suffix):
gui.hide_item("dylora_dim" + suffix)
gui.hide_item("dylora_alpha" + suffix)
gui.hide_item("dylora_unit" + suffix)
gui.set_value("dylora_dim_string" + suffix, "")
gui.set_value("dylora_alpha_string" + suffix, "")
gui.set_value("dylora_unit_string" + suffix, "")
def lokr(caller):
suffix = append_caller_instance(caller)
if gui.get_value("LoKR" + suffix):
gui.show_item("lokr_dim" + suffix)
gui.show_item("lokr_alpha" + suffix)
gui.set_value("DyLoRA" + suffix, False)
dylora(suffix)
gui.set_value("LoCON" + suffix, False)
locon(suffix)
gui.set_value("LoHA" + suffix, False)
loha(suffix)
gui.set_value("scale_weight_normals" + suffix, "")
if not gui.get_value("LoKR" + suffix):
gui.hide_item("lokr_dim" + suffix)
gui.hide_item("lokr_alpha" + suffix)
gui.set_value("lokr_dim_string" + suffix, "")
gui.set_value("lokr_alpha_string" + suffix, "")
def offset_noise(caller):
suffix = append_caller_instance(caller)
if gui.get_value("offset_noise" + suffix) == "None":
gui.hide_item("_noise_amount" + suffix)
gui.hide_item("_noise_discount" + suffix)
gui.hide_item("_noise_iterations" + suffix)
gui.set_value("noise_amount" + suffix, "")
gui.set_value("noise_discount" + suffix, "")
gui.set_value("noise_iterations" + suffix, "")
elif gui.get_value("offset_noise" + suffix) == "Normal":
gui.show_item("_noise_amount" + suffix)
gui.set_value("noise_amount" + suffix, "0.05")
gui.hide_item("_noise_discount" + suffix)
gui.set_value("noise_discount" + suffix, "")
gui.hide_item("_noise_iterations" + suffix)
gui.set_value("noise_iterations" + suffix, "")
elif gui.get_value("offset_noise" + suffix) == "Pyramid":
gui.hide_item("_noise_amount" + suffix)
gui.show_item("_noise_discount" + suffix)
gui.show_item("_noise_iterations" + suffix)
gui.set_value("noise_amount" + suffix, "")
gui.set_value("noise_discount" + suffix, "0.15")
gui.set_value("noise_iterations" + suffix, "6")
def network_module(caller):
suffix = append_caller_instance(caller)
if gui.get_value('locon_dim_string' + suffix) or gui.get_value('loha_dim_string' + suffix) or gui.get_value('lokr_dim_string' + suffix):
return f" \"--network_module=lycoris.kohya\" "
elif gui.get_value('dylora_dim_string' + suffix):
return f" \"--network_module=networks.dylora\" "
def additional_network_args(caller):
suffix = append_caller_instance(caller)
all_args = ""
if gui.get_value('locon_dim_string' + suffix):
locon_dim = gui.get_value('locon_dim_string' + suffix)
locon_alpha = gui.get_value('locon_alpha_string' + suffix)
all_args += f"\"conv_dim={locon_dim}\" "
all_args += f"\"conv_alpha={locon_alpha}\" "
all_args += f"\"algo=locon\" "
elif gui.get_value('dylora_dim_string' + suffix):
dylora_dim = gui.get_value('dylora_dim_string' + suffix)
dylora_alpha = gui.get_value('dylora_alpha_string' + suffix)
dylora_unit = gui.get_value('dylora_unit_string' + suffix)
all_args += f"\"conv_dim={dylora_dim}\" "
all_args += f"\"conv_alpha={dylora_alpha}\" "
all_args += f"\"unit={dylora_unit}\" "
elif gui.get_value('loha_dim_string' + suffix):
loha_dim = gui.get_value('loha_dim_string' + suffix)
loha_alpha = gui.get_value('loha_alpha_string' + suffix)
all_args += f"\"conv_dim={loha_dim}\" "
all_args += f"\"conv_alpha={loha_alpha}\" "
all_args += f"\"algo=loha\" "
elif gui.get_value('lokr_dim_string' + suffix):
lokr_dim = gui.get_value('lokr_dim_string' + suffix)
lokr_alpha = gui.get_value('lokr_alpha_string' + suffix)
all_args += f"\"conv_dim={lokr_dim}\" "
all_args += f"\"conv_alpha={lokr_alpha}\" "
all_args += f"\"algo=lokr\" "
return all_args
def combo_loras():
active_tab = get_active_tab()
lora_tab_name_list = []
lora_tab_instance_list = []
for lora_tab in gui.get_item_children("tab_bar_main_panel", 1):
tab_instance = gui.get_item_alias(lora_tab).split('_')[-1]
if gui.is_item_shown(lora_tab) and gui.get_item_info(lora_tab)["container"]:
if not ("tab_lora_" + tab_instance) == active_tab:
lora_tab_name_list.append(gui.get_item_label(lora_tab))
lora_tab_instance_list.append("_" + tab_instance)
gui.set_item_user_data("combo_lora_list", lora_tab_instance_list)
# print(gui.get_item_user_data("combo_lora_list"))
gui.configure_item("combo_lora_list", items = lora_tab_name_list)
# print(gui.get_item_configuration("combo_lora_list")["items"])
def show_fonts():
gui.show_font_manager()
def show_info():
pass
def open_link_guide():
webbrowser.open_new_tab("https://rentry.org/2chAI_LoRA_Dreambooth_guide")
def path_dialog_show(sender):
item = "path_dialog"
send_to = sender.replace("button_", "input_", 1)
gui.show_item(item)
gui.set_item_user_data(item, send_to)
def file_dialog_show(sender):
item = "file_dialog"
send_to = sender.replace("button_", "input_", 1)
gui.show_item(item)
gui.set_item_user_data(item, send_to)
def file_dialog_ok(sender, app_data, user_data):
print("Sending ", app_data["file_path_name"], " to ", user_data)
gui.set_value(user_data, app_data["file_path_name"])
def file_dialog_cancel():
pass
def sd_2x(caller):
suffix = append_caller_instance(caller)
if gui.get_value("v2" + suffix):
gui.show_item("v_parameterization" + suffix)
else:
gui.set_value("v_parameterization" + suffix, False)
gui.hide_item("v_parameterization" + suffix)
def sd_xl(caller):
suffix = append_caller_instance(caller)
if gui.get_value("sdxl" + suffix):
gui.show_item("unet_only_sdxl" + suffix)
else:
gui.set_value("unet_only_sdxl" + suffix, False)
gui.hide_item("unet_only_sdxl" + suffix)
def reg_images(caller):
suffix = append_caller_instance(caller)
if gui.get_value("use_reg_data" + suffix):
gui.show_item("group_reg_images" + suffix)
else:
gui.hide_item("group_reg_images" + suffix)
gui.set_value("reg_data_dir" + suffix, "")
def use_vae(caller):
suffix = append_caller_instance(caller)
if gui.get_value("use_vae" + suffix):
gui.show_item("group_vae_path" + suffix)
else:
gui.hide_item("group_vae_path" + suffix)
gui.set_value("vae" + suffix, "")
def custom_log_prefix(caller):
suffix = append_caller_instance(caller)
if gui.get_value("use_custom_log_prefix" + suffix):
gui.show_item("group_custom_log_prefix" + suffix)
else:
gui.hide_item("group_custom_log_prefix" + suffix)
gui.set_value("log_prefix" + suffix, "")
def cache_latents(caller):
suffix = append_caller_instance(caller)
if gui.get_value("cache_latents" + suffix):
gui.show_item("cache_latents_to_disk" + suffix)
else:
gui.hide_item("cache_latents_to_disk" + suffix)
gui.set_value("cache_latents_to_disk" + suffix, False)
def scheduler(caller):
suffix = append_caller_instance(caller)
if not gui.get_value("lr_scheduler" + suffix) == "constant":
gui.show_item("group_warmup_ratio" + suffix)
else:
gui.hide_item("group_warmup_ratio" + suffix)
def copy_settings_to_another_tab(user_data):
i = 0
active_tab = get_active_tab()
send_to_suffix = ""
for lora in gui.get_item_configuration("combo_lora_list")["items"]:
if gui.get_value("combo_lora_list") == lora:
send_to_suffix = gui.get_item_user_data("combo_lora_list")[i]
i += 1
print("Копируем настройки из:", get_active_tab())
print("В:", "tab_lora" + send_to_suffix)
for setting in list_settings:
# print(setting + send_to_suffix, " -> ", gui.get_value(setting + "_" + active_tab.split('_')[-1]))
try:
gui.set_value(setting + send_to_suffix, gui.get_value(setting + "_" + active_tab.split('_')[-1]))
except SystemError:
print("##### Error in: ", setting + send_to_suffix, " -> ",
gui.get_value(setting + "_" + active_tab.split('_')[-1]))
gui.set_value("output_name" + send_to_suffix, gui.get_value("output_name" + send_to_suffix) + "_copy")
gui.set_value("combo_lora_list", "")
def get_active_tab():
if tab_number == 1 and gui.get_value("tab_bar_main_panel") == 0:
return "lora_tab_1"
return gui.get_value("tab_bar_main_panel")
def close_modal_run():
gui.hide_item("modal_run")
def training_duration_method(caller):
suffix = append_caller_instance(caller)
methods = { "Использовать эпохи": "group_epochs_number" + suffix,
"Обучать в течении времени": "group_training_time" + suffix,
"Своё количество шагов": "group_custom_steps" + suffix }
gui.show_item(methods.get(gui.get_value("training_duration_method" + suffix)))
del methods[gui.get_value("training_duration_method" + suffix)]
for method in methods:
gui.hide_item(methods.get(method))
gui.set_value("text_max_train_steps" + suffix, train_steps(caller, "value"))
def train_steps(caller, request):
suffix = append_caller_instance(caller)
max_train_steps = 0
if gui.get_value("training_duration_method" + suffix) == "Использовать эпохи":
max_train_epochs = int(gui.get_value('max_train_epochs' + suffix))
if gui.get_value("use_reg_data" + suffix):
max_train_steps *= 2
total_images = calculate_total_images(suffix)
if not total_images:
return "нет изображений"
elif total_images < 0:
return "неверная папка"
else:
max_train_steps = calculate_total_images(suffix) / \
int(gui.get_value("train_batch_size" + suffix)) * max_train_epochs
if request == "value":
return int(max_train_steps)
if request == "arg":
return f" --max_train_epochs={gui.get_value('max_train_epochs' + suffix)}"
elif gui.get_value("training_duration_method" + suffix) == "Обучать в течении времени":
training_speed = float(gui.get_value("train_speed" + suffix))
if gui.get_value("train_speed_type" + suffix) == "s/it":
training_speed = 1 / training_speed
train_time_dict = gui.get_value("train_time" + suffix)
training_time_minutes = train_time_dict['min'] + train_time_dict['sec'] / 60 + train_time_dict['hour'] * 60
max_train_steps = training_speed * 60 * int(training_time_minutes)
# if gui.get_value("use_reg_data" + suffix) == True:
# max_train_steps *= 2
if request == "value":
return int(max_train_steps)
if request == "arg":
return f" --max_train_steps={int(max_train_steps)}"
else:
max_train_steps = gui.get_value('max_train_steps' + suffix)
if request == "value":
return max_train_steps
if request == "arg":
return f" --max_train_steps={int(max_train_steps)}"
def lora_resize():
gui.show_item("modal_training")
resize_args = {'new_rank': gui.get_value('new_rank_value'), 'dynamic_method': gui.get_value('dynamic_method_value'),
'dynamic_param': gui.get_value('dynamic_param_value'), 'lora_path': gui.get_value('lora_path_value'),
'output_path': gui.get_value('output_path_value'), 'save_precision': gui.get_value('save_precision_value'),
'device': gui.get_value('device_value')}
commands = "[console]::OutputEncoding = [text.encoding]::UTF8\n"
commands += "$env:PYTHONIOENCODING = 'utf-8'\n"
commands += f"Set-Location \"{gui.get_value('sd_scripts_path')}\"\n"
commands += ".\\venv\Scripts\\activate\n"
if resize_args['lora_path'].endswith('.safetensors'):
lora_name = resize_args['lora_path'].rsplit('\\', 1)
lora_without_suffix = lora_name[1].removesuffix('.safetensors')
commands += f"python networks\\resize_lora.py --save_precision {resize_args['save_precision']} --new_rank {resize_args['new_rank']}" \
f" --save_to \"{resize_args['output_path']}\{lora_without_suffix}-{resize_args['new_rank']}rank.safetensors\" " \
f" --model \"{resize_args['lora_path']}\" --device {resize_args['device']}" \
f" --dynamic_method {resize_args['dynamic_method']} --dynamic_param {resize_args['dynamic_param']}"
proc = subprocess.Popen("powershell", stdin=subprocess.PIPE).communicate(input=commands.encode())
else:
loras = os.listdir(resize_args['lora_path'])
for i in loras:
if i.endswith('.safetensors'):
commands = "[console]::OutputEncoding = [text.encoding]::UTF8\n"
commands += "$env:PYTHONIOENCODING = 'utf-8'\n"
commands += f"Set-Location \"{gui.get_value('sd_scripts_path')}\"\n"
commands += ".\\venv\Scripts\\activate\n"
commands += f"python networks\\resize_lora.py --save_precision {resize_args['save_precision']} --new_rank {resize_args['new_rank']}" \
f" --save_to \"{resize_args['output_path']}\{i.removesuffix('.safetensors')}-{resize_args['new_rank']}rank.safetensors\" " \
f" --model \"{resize_args['lora_path']}\{i}\" --device {resize_args['device']}" \
f" --dynamic_method {resize_args['dynamic_method']} --dynamic_param {resize_args['dynamic_param']}"
proc = subprocess.Popen("powershell", stdin=subprocess.PIPE).communicate(input=commands.encode())
else:
continue
return gui.hide_item("modal_training")
def start_tensorboard():
log_dir = gui.get_value('log_path_value')
commands = "[console]::OutputEncoding = [text.encoding]::UTF8\n"
commands += "$env:PYTHONIOENCODING = 'utf-8'\n"
commands += f"Set-Location \"{gui.get_value('sd_scripts_path')}\"\n"
commands += ".\\venv\Scripts\\activate\n"
commands += f"Start-Process -NoNewWindow tensorboard -ArgumentList \"--logdir {log_dir}\""
proc = subprocess.Popen("powershell", stdin=subprocess.PIPE).communicate(input=commands.encode())
time.sleep(10)
webbrowser.open_new_tab(url="http://localhost:6006")
return
def tensor_check():
lora_path = gui.get_value('lora_path_for_tensorcheck_value')
output_dir = gui.get_value('path_for_tensorchecker_output')
lora_name = lora_path.rsplit('\\', 1)
lora_without_suffix = lora_name[1].removesuffix('.safetensors')
commands = "[console]::OutputEncoding = [text.encoding]::UTF8\n"
commands += "$env:PYTHONIOENCODING = 'utf-8'\n"
commands += f"Set-Location \"{gui.get_value('sd_scripts_path')}\"\n"
commands += ".\\venv\Scripts\\activate\n"
commands += f"python networks\\check_lora_weights.py \"{lora_path}\" > \"{output_dir}\\{lora_without_suffix}.txt\""
proc = subprocess.Popen("powershell", stdin=subprocess.PIPE).communicate(input=commands.encode())
return
def RUN():
sd_scripts_path_to_registry()
tab_count = calculate_lora_tab_count()
gui.hide_item("modal_run")
time.sleep(0.5)
if tab_count < 1:
_info_popup("Нечего обучать!")
return
for i in range(1, tab_count + 1):
commands = ""
suffix = "_" + str(i)
gui.set_value("text_training_number", "Обучение сети: " + str(i) + "/" + str(tab_count))
gui.set_value("text_training_path", "...\\" + gui.get_value('output_name' + suffix) + ".safetensors")
gui.show_item("modal_training")
commands = "[console]::OutputEncoding = [text.encoding]::UTF8\n"
# блять jfs ебать ты чед
commands += "$env:PYTHONIOENCODING = 'utf-8'\n"
commands += f"Set-Location \"{gui.get_value('sd_scripts_path')}\"\n"
commands += ".\\venv\Scripts\\activate\n"
if gui.get_value('sdxl' + suffix):
train_script = "sdxl_train_network.py"
if gui.get_value('unet_only_sdxl' + suffix):
train_script += " --network_train_unet_only"
else:
train_script = "train_network.py"
# \"{ gui.get_value('' + suffix) }\"
commands += f"accelerate launch --num_cpu_threads_per_process {gui.get_value('max_data_loader_n_workers' + suffix)}" \
f" {train_script} --pretrained_model_name_or_path=\"{remove_trailing_slashes(gui.get_value('pretrained_model_name_or_path' + suffix))}\"" \
f" --train_data_dir=\"{remove_trailing_slashes(gui.get_value('train_data_dir' + suffix))}\"" \
f" --output_dir=\"{remove_trailing_slashes(gui.get_value('output_dir' + suffix))}\"" \
f" --output_name=\"{gui.get_value('output_name' + suffix)}\"" \
f" --save_every_n_epochs={gui.get_value('save_every_n_epochs' + suffix)}" \
f" --save_last_n_epochs={gui.get_value('save_last_n_epochs' + suffix)}" \
f" --train_batch_size={gui.get_value('train_batch_size' + suffix)}" \
f" --resolution=\"{gui.get_value('resolution' + suffix)}\"" \
f" --network_dim={gui.get_value('network_dim' + suffix)}" \
f" --gradient_accumulation_steps={gui.get_value('gradient_accumulation_steps' + suffix)}" \
f" --max_data_loader_n_workers={gui.get_value('max_data_loader_n_workers' + suffix)}" \
f" --save_precision={gui.get_value('save_precision' + suffix)}" \
f" --mixed_precision={gui.get_value('mixed_precision' + suffix)}"
# f" --learning_rate={round(gui.get_value('learning_rate' + suffix), 8)}" \
# f" --unet_lr={round(gui.get_value('unet_lr' + suffix), 8)}" \
# f" --text_encoder_lr={round(gui.get_value('text_encoder_lr' + suffix), 8)}"
if gui.get_value('scheduler_name_string' + suffix):
commands += f" --lr_scheduler_type={gui.get_value('scheduler_name_string' + suffix)}"
else:
commands += f" --lr_scheduler={gui.get_value('lr_scheduler' + suffix)}"
if gui.get_value('scheduler_args' + suffix):
scheduler_args = gui.get_value('scheduler_args' + suffix)
if "--lr_scheduler_num_cycles" in scheduler_args:
commands += f" {gui.get_value('scheduler_args' + suffix)}"
elif "--lr_scheduler_power" in scheduler_args:
commands += f" {gui.get_value('scheduler_args' + suffix)}"
else:
commands += f" --lr_scheduler_args {gui.get_value('scheduler_args' + suffix)}"
if gui.get_value('optimizer_name_string' + suffix):
optimizer_type = (gui.get_value('optimizer_name_string' + suffix)) # https://github.com/kozistr/pytorch_optimizer
commands += f" --optimizer_type={optimizer_type}"
else:
optimizer_type = (gui.get_value('optimizer_type' + suffix))
if optimizer_type == "Old_version": # old version compatibility
commands += f" --use_8bit_adam"
elif optimizer_type != "Old_version":
commands += f" --optimizer_type={optimizer_type}" # https://github.com/kohya-ss/sd-scripts/releases/tag/v0.4.4
# pip install lion-pytorch dadaptation в венве с сд-скриптс, чтобы юзать новые оптимайзеры
if gui.get_value('optimizer_args' + suffix):
commands += f" --optimizer_args {gui.get_value('optimizer_args' + suffix)}"
if gui.get_value("use_separate_lr" + suffix):
if optimizer_type == "DAdaptation" or optimizer_type == "DAdaptAdam": # pizdos fix obsera
commands += f" --learning_rate={gui.get_value('unet_lr' + suffix)}"
# commands += f" --unet_lr={gui.get_value('unet_lr' + suffix)}"
commands += f" --text_encoder_lr={gui.get_value('text_encoder_lr' + suffix)}"
else:
commands += f" --unet_lr={gui.get_value('unet_lr' + suffix)}"
commands += f" --text_encoder_lr={gui.get_value('text_encoder_lr' + suffix)}"
else:
commands += f" --learning_rate={gui.get_value('learning_rate' + suffix)}"
if gui.get_value("v2" + suffix):
commands += " --v2"
if gui.get_value("v_parameterization" + suffix):
commands += " --v_parameterization"
if gui.get_value("use_reg_data" + suffix):
commands += f" --reg_data_dir=\"{remove_trailing_slashes(gui.get_value('reg_data_dir' + suffix))}\""
if gui.get_value("use_vae" + suffix):
commands += f" --vae=\"{remove_trailing_slashes(gui.get_value('vae' + suffix))}\""
max_train_steps = train_steps(suffix, "value")
commands += train_steps(suffix, "arg")
if not gui.get_value('lr_scheduler' + suffix) == 'constant':
try:
commands += " --lr_warmup_steps=" \
f"{int(gui.get_value('lr_warmup_ratio' + suffix) / 100 * int(max_train_steps))}"
except ValueError:
print('Не удалось расчитать количество шагов lr_warmup_steps: ошибка в папке с изображениями')
continue
if not gui.get_value('clip_skip' + suffix) == "1":
commands += f" --clip_skip={gui.get_value('clip_skip' + suffix)}"
network_alpha = gui.get_value('network_alpha' + suffix)
if network_alpha:
commands += f" --network_alpha={network_alpha}"
if gui.get_value('shuffle_caption' + suffix):
commands += " --shuffle_caption"
if not gui.get_value('max_token_length' + suffix) == "75":
commands += f" --max_token_length={gui.get_value('max_token_length' + suffix)}"
seed = gui.get_value('seed' + suffix)
if gui.get_value('seed' + suffix) == "-1":
seed = random.randint(1, 2147483647)
commands += f" --seed={seed}"
if gui.get_value('gradient_checkpointing' + suffix):
commands += " --gradient_checkpointing"
if not gui.get_value('logging_dir' + suffix) == "":
commands += f" --logging_dir=\"{remove_trailing_slashes(gui.get_value('logging_dir' + suffix))}\""
log_prefix = gui.get_value('output_name' + suffix) + "_"
if gui.get_value('use_custom_log_prefix' + suffix):
log_prefix = gui.get_value('log_prefix' + suffix)
commands += f" --log_prefix=\"{log_prefix}\""
if gui.get_value('min_snr_gamma' + suffix):
min_snr_gamma = gui.get_value('min_snr_gamma' + suffix)
commands += f" --min_snr_gamma={min_snr_gamma}"
if gui.get_value("offset_noise" + suffix) == "None":
pass
elif gui.get_value("offset_noise" + suffix) == "Normal":
noise_offset = gui.get_value('noise_amount' + suffix)
commands += f" --noise_offset={noise_offset}"
elif gui.get_value("offset_noise" + suffix) == "Pyramid":
# commands += f" --adaptive_noise_scale"
# noise_offset = gui.get_value('noise_amount' + suffix)
# commands += f" --noise_offset={noise_offset}"
noise_discount = gui.get_value('noise_discount' + suffix)
commands += f" --multires_noise_discount={noise_discount}"
noise_iterations = gui.get_value('noise_iterations' + suffix)
commands += f" --multires_noise_iterations={noise_iterations}"
if gui.get_value("scale_weight_normals" + suffix):
scale_weight_normals = gui.get_value('scale_weight_normals' + suffix)
commands += f" --scale_weight_norms={scale_weight_normals}" # не работает с LoKR
if gui.get_value('LoCON' + suffix) or gui.get_value('LoHA' + suffix) or gui.get_value('DyLoRA' + suffix) or gui.get_value('LoKR' + suffix):
commands += network_module(suffix)
additional_network_arguments = additional_network_args(suffix)
if additional_network_arguments:
commands += f" --network_args {additional_network_arguments}"
else:
commands += f" --network_module=networks.lora"
if gui.get_value('keep_tokens' + suffix):
keep_tokens_amount = gui.get_value('keep_tokens' + suffix)
commands += f" --keep_tokens={keep_tokens_amount}"
if gui.get_value('cache_latents' + suffix):
commands += f" --cache_latents"
if gui.get_value('cache_latents_to_disk' + suffix):
commands += f" --cache_latents_to_disk"
if gui.get_value('network_dropout' + suffix):
network_dropout = gui.get_value('network_dropout' + suffix)
commands += f" --network_dropout={network_dropout}"
commands += f" {gui.get_value('additional_parameters' + suffix)}\n"
proc = subprocess.Popen("powershell", stdin = subprocess.PIPE).communicate(input = commands.encode())
# proc
gui.hide_item("modal_training")
def calculate_scheduler_plot_data():
sindatax = []; sindatay = []
scheduler = gui.get_value("lr_scheduler")
if scheduler == "linear":
sindatax.append([0, gui.get_value("lr_warmup_ratio") / 100, 1])
sindatay.append([0, 1, 0])
gui.get_item_configuration("plot_line_series")
tab_number = 0
def add_lora_tab():
global lora_tab_instances
lora_tab_instances += 1
global tab_number
tab_number += 1
with gui.item_handler_registry(tag = append_instance_number("handler_checkbox")):
gui.add_item_visible_handler(callback = sd_2x, tag = append_instance_number("visibility_handler_sd_2x"))
gui.add_item_visible_handler(callback = sd_xl, tag = append_instance_number("visibility_handler_sdxl"))
gui.add_item_visible_handler(callback = cache_latents, tag=append_instance_number("visibility_handler_cache_latents"))
gui.add_item_visible_handler(callback = use_vae, tag = append_instance_number("visibility_handler_use_vae"))
gui.add_item_visible_handler(callback = reg_images,
tag = append_instance_number("visibility_handler_reg_images"))
gui.add_item_visible_handler(callback = custom_log_prefix,
tag = append_instance_number("visibility_handler_custom_log_prefix"))
gui.add_item_visible_handler(callback = separate_lr,
tag = append_instance_number("visibility_handler_separate_lr"))
gui.add_item_visible_handler(callback=custom_scheduler_name,
tag=append_instance_number("visibility_handler_custom_lr"))
gui.add_item_visible_handler(callback=custom_optimizer_name,
tag=append_instance_number("visibility_handler_custom_optimizer"))
gui.add_item_visible_handler(callback=locon,
tag=append_instance_number("visibility_handler_locon"))
gui.add_item_visible_handler(callback=loha,
tag=append_instance_number("visibility_handler_loha"))
gui.add_item_visible_handler(callback=dylora,
tag=append_instance_number("visibility_handler_dylora"))
gui.add_item_visible_handler(callback=lokr,
tag=append_instance_number("visibility_handler_lokr"))
gui.add_item_visible_handler(callback=offset_noise,
tag=append_instance_number("visibility_handler_offset_noise"))
with gui.item_handler_registry(tag = append_instance_number("handler_radio")):
gui.add_item_visible_handler(callback = training_duration_method,
tag = append_instance_number("visibility_handler_training_method"))
with gui.item_handler_registry(tag = append_instance_number("handler_combo")):
gui.add_item_visible_handler(callback = scheduler, tag = append_instance_number("visibility_handler_scheduler"))
with gui.item_handler_registry(tag = append_instance_number("handler_tab")):
gui.add_item_visible_handler(callback = lora_tab_update_name, tag = append_instance_number("visibility_handler_tab_name"))
with gui.tab(tag = append_instance_number("tab_lora"), before = "tab_button_add_lora_tab",
label = "network_" + str(tab_number), closable = True):
calculate_lora_tab_count()
with gui.tab_bar(tag = append_instance_number("tab_bar_lora")):
gui.bind_item_handler_registry(append_instance_number("tab_lora"), append_instance_number("handler_tab"))
with gui.tab(label = "Пути"):
# ckpt
with gui.group(horizontal = True):
path_button(tag = append_instance_number("button_ckpt_path"), path_type = "file",
label = "SD чекпоинт")
gui.add_input_text(tag = append_instance_number("pretrained_model_name_or_path"),
hint = "X:\models\checkpoint.safetensors", width = -1)
# sd 2.x checkboxes
with gui.group(horizontal = True):
gui.add_checkbox(tag = append_instance_number("v_parameterization"), label = "768-v",
show = False)
gui.add_checkbox(tag = append_instance_number("v2"),
label = "Stable Diffusion 2.x",
before = append_instance_number("v_parameterization"))
gui.bind_item_handler_registry(append_instance_number("v2"),
append_instance_number("handler_checkbox"))
with gui.group(horizontal = True):
gui.add_checkbox(tag = append_instance_number("sdxl"), label = "SDXL",
show = True, default_value=False, callback=sd_xl)
gui.add_checkbox(tag=append_instance_number("unet_only_sdxl"),
label="Train UNet only", default_value=False)
# gui.bind_item_handler_registry(append_instance_number("sdxl"),
# append_instance_number("handler_checkbox")) # Какой то баг посел добавления локра с кликабельностью кнопок из меню "Пути"
# vae checkbox
gui.add_checkbox(tag = append_instance_number("use_vae"), label = "Использовать VAE",
callback = use_vae)
# vae
with gui.group(tag = append_instance_number("group_vae_path"), horizontal = True, show = False):
path_button(tag = append_instance_number("button_vae_path"), path_type = "file",
label = "VAE чекпоинт")
gui.add_input_text(tag = append_instance_number("vae"), hint = "X:\models\\vae.pt",
width = -1)
gui.bind_item_handler_registry(append_instance_number("use_vae"),
append_instance_number("handler_checkbox"))
# images dir
with gui.group(horizontal = True):
path_button(tag = append_instance_number("button_img_path"), path_type = "folder",
label = "Папка с изображениями")
gui.add_input_text(tag = append_instance_number("train_data_dir"), hint = "X:\\training_data\img",
width = -1)
# use reg images
gui.add_checkbox(tag = append_instance_number("use_reg_data"),
label = "Использовать рег. изображения",
callback = reg_images)
gui.bind_item_handler_registry(append_instance_number("use_reg_data"),
append_instance_number("handler_checkbox"))
# reg images dir
with gui.group(tag = append_instance_number("group_reg_images"), horizontal = True, show = False):
path_button(tag = append_instance_number("button_reg_img_path"),
path_type = "folder",
label = "Папка с рег. изображениями")
gui.add_input_text(tag = append_instance_number("reg_data_dir"),
hint = "X:\\training_data\img", width = -1)
# output dir
with gui.group(horizontal = True):
path_button(tag = append_instance_number("button_output_path"),
path_type = "folder",
label = "Папка сохранения файла")
gui.add_input_text(tag = append_instance_number("output_dir"), hint = "X:\LoRA",
width = -1)
# output name
with gui.group(horizontal = True):
gui.add_text("Имя файла")
gui.add_input_text(tag = append_instance_number("output_name"), hint = "my_LoRA_network",
width = -1,
default_value = gui.get_item_label(append_instance_number("tab_lora")))
with gui.tab(label = "Длительность"):
with gui.table(header_row = False):
gui.add_table_column()
gui.add_table_column()
with gui.table_row():
gui.add_text("Метод расчёта шагов обучения")
with gui.group(horizontal = True):
gui.add_text("Количество шагов: ")
gui.add_text("", tag = append_instance_number("text_max_train_steps"))
gui.add_radio_button(tag = append_instance_number("training_duration_method"),
items = ["Использовать эпохи", "Обучать в течении времени",
"Своё количество шагов"], default_value = "Использовать эпохи",
horizontal = True, callback = training_duration_method)