import json import time from typing import Union import os import tkinter as tk from tkinter import filedialog as fd from tkinter import simpledialog as sd from tkinter import messagebox as mb import train_network import library.train_util as util import argparse class ArgStore: # Represents the entirety of all possible inputs for sd-scripts. they are ordered from most important to least def __init__(self): # Important, these are the most likely things you will modify self.base_model: str = r"" # example path, r"E:\sd\stable-diffusion-webui\models\Stable-diffusion\nai.ckpt" self.img_folder: str = r"" # is the folder path to your img folder, make sure to follow the guide here for folder setup: https://rentry.org/2chAI_LoRA_Dreambooth_guide_english#for-kohyas-script self.output_folder: str = r"" # just the folder all epochs/safetensors are output self.change_output_name: Union[str, None] = None # changes the output name of the epochs self.save_json_folder: Union[str, None] = None # OPTIONAL, saves a json folder of your config to whatever location you set here. self.load_json_path: Union[str, None] = None # OPTIONAL, loads a json file partially changes the config to match. things like folder paths do not get modified. self.json_load_skip_list: Union[list[str], None] = ["base_model", "img_folder", "output_folder", "save_json_folder", "reg_img_folder", "lora_model_for_resume", "change_output_name", "training_comment", "json_load_skip_list"] # OPTIONAL, allows the user to define what they skip when loading a json, by default it loads everything, including all paths, set it up like this ["base_model", "img_folder", "output_folder"] self.net_dim: int = 128 # network dimension, 128 is the most common, however you might be able to get lesser to work self.alpha: float = 128 # represents the scalar for training. the lower the alpha, the less gets learned per step. if you want the older way of training, set this to dim # list of schedulers: linear, cosine, cosine_with_restarts, polynomial, constant, constant_with_warmup self.scheduler: str = "cosine_with_restarts" # the scheduler for learning rate. Each does something specific self.cosine_restarts: Union[int, None] = 1 # OPTIONAL, represents the number of times it restarts. Only matters if you are using cosine_with_restarts self.scheduler_power: Union[float, None] = 1 # OPTIONAL, represents the power of the polynomial. Only matters if you are using polynomial self.warmup_lr_ratio: Union[float, None] = None # OPTIONAL, Calculates the number of warmup steps based on the ratio given. Make sure to set this if you are using constant_with_warmup, None to ignore self.learning_rate: Union[float, None] = 1e-4 # OPTIONAL, when not set, lr gets set to 1e-3 as per adamW. Personally, I suggest actually setting this as lower lr seems to be a small bit better. self.text_encoder_lr: Union[float, None] = None # OPTIONAL, Sets a specific lr for the text encoder, this overwrites the base lr I believe, None to ignore self.unet_lr: Union[float, None] = None # OPTIONAL, Sets a specific lr for the unet, this overwrites the base lr I believe, None to ignore self.num_workers: int = 1 # The number of threads that are being used to load images, lower speeds up the start of epochs, but slows down the loading of data. The assumption here is that it increases the training time as you reduce this value self.batch_size: int = 1 # The number of images that get processed at one time, this is directly proportional to your vram and resolution. with 12gb of vram, at 512 reso, you can get a maximum of 6 batch size self.num_epochs: int = 1 # The number of epochs, if you set max steps this value is ignored as it doesn't calculate steps. self.save_at_n_epochs: Union[int, None] = 1 # OPTIONAL, how often to save epochs, None to ignore self.shuffle_captions: bool = False # OPTIONAL, False to ignore self.keep_tokens: Union[int, None] = None # OPTIONAL, None to ignore self.max_steps: Union[int, None] = None # OPTIONAL, if you have specific steps you want to hit, this allows you to set it directly. None to ignore # These are the second most likely things you will modify self.train_resolution: int = 512 self.min_bucket_resolution: int = 320 self.max_bucket_resolution: int = 960 self.lora_model_for_resume: Union[str, None] = None # OPTIONAL, takes an input lora to continue training from, not exactly the way it *should* be, but it works, None to ignore self.save_state: bool = False # OPTIONAL, is the intended way to save a training state to use for continuing training, False to ignore self.load_previous_save_state: Union[str, None] = None # OPTIONAL, is the intended way to load a training state to use for continuing training, None to ignore self.training_comment: Union[str, None] = None # OPTIONAL, great way to put in things like activation tokens right into the metadata. seems to not work at this point and time # These are the least likely things you will modify self.reg_img_folder: Union[str, None] = None # OPTIONAL, None to ignore self.clip_skip: int = 2 # If you are training on a model that is anime based, keep this at 2 as most models are designed for that self.test_seed: int = 23 # this is the "reproducable seed", basically if you set the seed to this, you should be able to input a prompt from one of your training images and get a close representation of it self.prior_loss_weight: float = 1 # is the loss weight much like Dreambooth, is required for LoRA training self.gradient_checkpointing: bool = False # OPTIONAL, enables gradient checkpointing self.gradient_acc_steps: Union[int, None] = None # OPTIONAL, not sure exactly what this means self.mixed_precision: str = "fp16" # If you have the ability to use bf16, do it, it's better self.save_precision: str = "fp16" # You can also save in bf16, but because it's not universally supported, I suggest you keep saving at fp16 self.save_as: str = "safetensors" # list is pt, ckpt, safetensors self.caption_extension: str = ".txt" # the other option is .captions, but since wd1.4 tagger outputs as txt files, this is the default self.max_clip_token_length = 150 # can be 75, 150, or 225 I believe, there is no reason to go higher than 150 though self.buckets: bool = True self.xformers: bool = True self.use_8bit_adam: bool = True self.cache_latents: bool = True self.color_aug: bool = False # IMPORTANT: Clashes with cache_latents, only have one of the two on! self.flip_aug: bool = False self.vae: Union[str, None] = None # Seems to only make results worse when not using that specific vae, should probably not use self.no_meta: bool = False # This removes the metadata that now gets saved into safetensors, (you should keep this on) self.log_dir: Union[str, None] = None # output of logs, not useful to most people. # Creates the dict that is used for the rest of the code, to facilitate easier json saving and loading @staticmethod def convert_args_to_dict(): return ArgStore().__dict__ def main(): parser = argparse.ArgumentParser() setup_args(parser) arg_dict = ArgStore.convert_args_to_dict() pre_args = parser.parse_args() ret = mb.askyesno(message="Do you want to load a json config file?") if ret: load_json(ask_file("select json to load from", {"json"}), arg_dict) arg_dict = ask_elements_trunc(arg_dict) else: arg_dict = ask_elements(arg_dict) if pre_args.load_json_path or arg_dict["load_json_path"]: load_json(pre_args.load_json_path if pre_args.load_json_path else arg_dict['load_json_path'], arg_dict) if pre_args.save_json_path or arg_dict["save_json_folder"]: save_json(pre_args.save_json_path if pre_args.save_json_path else arg_dict['save_json_folder'], arg_dict) args = create_arg_space(arg_dict) args = parser.parse_args(args) train_network.train(args) def create_arg_space(args: dict) -> [str]: # This is the list of args that are to be used regardless of setup output = ["--network_module=networks.lora", f"--pretrained_model_name_or_path={args['base_model']}", f"--train_data_dir={args['img_folder']}", f"--output_dir={args['output_folder']}", f"--prior_loss_weight={args['prior_loss_weight']}", f"--caption_extension=" + args['caption_extension'], f"--resolution={args['train_resolution']}", f"--train_batch_size={args['batch_size']}", f"--mixed_precision={args['mixed_precision']}", f"--save_precision={args['save_precision']}", f"--network_dim={args['net_dim']}", f"--save_model_as={args['save_as']}", f"--clip_skip={args['clip_skip']}", f"--seed={args['test_seed']}", f"--max_token_length={args['max_clip_token_length']}", f"--lr_scheduler={args['scheduler']}", f"--network_alpha={args['alpha']}"] if not args['max_steps']: steps = find_max_steps(args) else: steps = args["max_steps"] output.append(f"--max_train_steps={steps}") output += create_optional_args(args, steps) return output def create_optional_args(args: dict, steps): output = [] if args["reg_img_folder"]: output.append(f"--reg_data_dir={args['reg_img_folder']}") if args['lora_model_for_resume']: output.append(f"--network_weights={args['lora_model_for_resume']}") if args['save_at_n_epochs']: output.append(f"--save_every_n_epochs={args['save_at_n_epochs']}") else: output.append("--save_every_n_epochs=999999") if args['shuffle_captions']: output.append("--shuffle_caption") if args['keep_tokens'] and args['keep_tokens'] > 0: output.append(f"--keep_tokens={args['keep_tokens']}") if args['buckets']: output.append("--enable_bucket") output.append(f"--min_bucket_reso={args['min_bucket_resolution']}") output.append(f"--max_bucket_reso={args['max_bucket_resolution']}") if args['use_8bit_adam']: output.append("--use_8bit_adam") if args['xformers']: output.append("--xformers") if args['color_aug']: if args['cache_latents']: print("color_aug and cache_latents conflict with one another. Please select only one") quit(1) output.append("--color_aug") if args['flip_aug']: output.append("--flip_aug") if args['cache_latents']: output.append("--cache_latents") if args['warmup_lr_ratio'] and args['warmup_lr_ratio'] > 0: warmup_steps = int(steps * args['warmup_lr_ratio']) output.append(f"--lr_warmup_steps={warmup_steps}") if args['gradient_checkpointing']: output.append("--gradient_checkpointing") if args['gradient_acc_steps'] and args['gradient_acc_steps'] > 0 and args['gradient_checkpointing']: output.append(f"--gradient_accumulation_steps={args['gradient_acc_steps']}") if args['learning_rate'] and args['learning_rate'] > 0: output.append(f"--learning_rate={args['learning_rate']}") if args['text_encoder_lr'] and args['text_encoder_lr'] > 0: output.append(f"--text_encoder_lr={args['text_encoder_lr']}") if args['unet_lr'] and args['unet_lr'] > 0: output.append(f"--unet_lr={args['unet_lr']}") if args['vae']: output.append(f"--vae={args['vae']}") if args['no_meta']: output.append("--no_metadata") if args['save_state']: output.append("--save_state") if args['load_previous_save_state']: output.append(f"--resume={args['load_previous_save_state']}") if args['change_output_name']: output.append(f"--output_name={args['change_output_name']}") if args['training_comment']: output.append(f"--training_comment={args['training_comment']}") if args['num_workers']: output.append(f"--max_data_loader_n_workers={args['num_workers']}") if args['cosine_restarts'] and args['scheduler'] == "cosine_with_restarts": output.append(f"--lr_scheduler_num_cycles={args['cosine_restarts']}") if args['scheduler_power'] and args['scheduler'] == "polynomial": output.append(f"--lr_scheduler_power={args['scheduler_power']}") return output def find_max_steps(args: dict) -> int: total_steps = 0 folders = os.listdir(args["img_folder"]) for folder in folders: if not os.path.isdir(os.path.join(args["img_folder"], folder)): continue num_repeats = folder.split("_") if len(num_repeats) < 2: print(f"folder {folder} is not in the correct format. Format is x_name. skipping") continue try: num_repeats = int(num_repeats[0]) except ValueError: print(f"folder {folder} is not in the correct format. Format is x_name. skipping") continue imgs = 0 for file in os.listdir(os.path.join(args["img_folder"], folder)): if os.path.isdir(file): continue ext = file.split(".") if ext[-1].lower() in {"png", "bmp", "gif", "jpeg", "jpg", "webp"}: imgs += 1 total_steps += (num_repeats * imgs) total_steps = int((total_steps / args["batch_size"]) * args["num_epochs"]) if quick_calc_regs(args): total_steps *= 2 return total_steps def quick_calc_regs(args: dict) -> bool: if not args["reg_img_folder"] or not os.path.exists(args["reg_img_folder"]): return False folders = os.listdir(args["reg_img_folder"]) for folder in folders: if not os.path.isdir(os.path.join(args["reg_img_folder"], folder)): continue num_repeats = folder.split("_") if len(num_repeats) < 2: continue try: num_repeats = int(num_repeats[0]) except ValueError: continue for file in os.listdir(os.path.join(args["reg_img_folder"], folder)): if os.path.isdir(file): continue ext = file.split(".") if ext[-1].lower() in {"png", "bmp", "gif", "jpeg", "jpg", "webp"}: return True return False def add_misc_args(parser): parser.add_argument("--save_json_path", type=str, default=None, help="Path to save a configuration json file to") parser.add_argument("--load_json_path", type=str, default=None, help="Path to a json file to configure things from") parser.add_argument("--no_metadata", action='store_true', help="do not save metadata in output model / メタデータを出力先モデルに保存しない") parser.add_argument("--save_model_as", type=str, default="safetensors", choices=[None, "ckpt", "pt", "safetensors"], help="format to save the model (default is .safetensors) / モデル保存時の形式（デフォルトはsafetensors）") parser.add_argument("--unet_lr", type=float, default=None, help="learning rate for U-Net / U-Netの学習率") parser.add_argument("--text_encoder_lr", type=float, default=None, help="learning rate for Text Encoder / Text Encoderの学習率") parser.add_argument("--lr_scheduler_num_cycles", type=int, default=1, help="Number of restarts for cosine scheduler with restarts / cosine with restartsスケジューラでのリスタート回数") parser.add_argument("--lr_scheduler_power", type=float, default=1, help="Polynomial power for polynomial scheduler / polynomialスケジューラでのpolynomial power") parser.add_argument("--network_weights", type=str, default=None, help="pretrained weights for network / 学習するネットワークの初期重み") parser.add_argument("--network_module", type=str, default=None, help='network module to train / 学習対象のネットワークのモジュール') parser.add_argument("--network_dim", type=int, default=None, help='network dimensions (depends on each network) / モジュールの次元数（ネットワークにより定義は異なります）') parser.add_argument("--network_alpha", type=float, default=1, help='alpha for LoRA weight scaling, default 1 (same as network_dim for same behavior as old version) / LoRaの重み調整のalpha値、デフォルト1（旧バージョンと同じ動作をするにはnetwork_dimと同じ値を指定）') parser.add_argument("--network_args", type=str, default=None, nargs='*', help='additional argmuments for network (key=value) / ネットワークへの追加の引数') parser.add_argument("--network_train_unet_only", action="store_true", help="only training U-Net part / U-Net関連部分のみ学習する") parser.add_argument("--network_train_text_encoder_only", action="store_true", help="only training Text Encoder part / Text Encoder関連部分のみ学習する") parser.add_argument("--training_comment", type=str, default=None, help="arbitrary comment string stored in metadata / メタデータに記録する任意のコメント文字列") def setup_args(parser): util.add_sd_models_arguments(parser) util.add_dataset_arguments(parser, True, True) util.add_training_arguments(parser, True) add_misc_args(parser) def ask_file(message, accepted_ext_list, file_path=None): mb.showinfo(message=message) res = "" _initialdir = "" _initialfile = "" if file_path != None: _initialdir = os.path.dirname(file_path) if os.path.exists(file_path) else "" _initialfile = os.path.basename(file_path) if os.path.exists(file_path) else "" while res == "": res = fd.askopenfilename(title=message, initialdir=_initialdir, initialfile=_initialfile) if res == "": ret = mb.askretrycancel(message="Do you want to to cancel training?") if not ret: exit() continue elif not os.path.exists(res): res = "" continue _, name = os.path.split(res) split_name = name.split(".") if split_name[-1] not in accepted_ext_list: res = "" return res def ask_dir(message, dir_path=None): mb.showinfo(message=message) res = "" _initialdir = "" if dir_path != None: _initialdir = dir_path if os.path.exists(dir_path) else "" while res == "": res = fd.askdirectory(title=message, initialdir=_initialdir) if res == "": ret = mb.askretrycancel(message="Do you want to to cancel training?") if not ret: exit() continue if not os.path.exists(res): res = "" return res def ask_elements_trunc(args: dict): args['base_model'] = ask_file("Select your base model", {"ckpt", "safetensors"}, args['base_model']) args['img_folder'] = ask_dir("Select your image folder", args['img_folder']) args['output_folder'] = ask_dir("Select your output folder", args['output_folder']) ret = mb.askyesno(message="Do you want to save a json of your configuration?") if ret: args['save_json_folder'] = ask_dir("Select the folder to save json files to", args['save_json_folder']) else: args['save_json_folder'] = None ret = mb.askyesno(message="Do you want to use regularization images?") if ret: args['reg_img_folder'] = ask_dir("Select your regularization folder", args['reg_img_folder']) else: args['reg_img_folder'] = None ret = mb.askyesno(message="Do you want to continue from an earlier version?") if ret: args['lora_model_for_resume'] = ask_file("Select your lora model", {"ckpt", "pt", "safetensors"}, args['lora_model_for_resume']) else: args['lora_model_for_resume'] = None ret = mb.askyesno(message="Do you want to change the name of output checkpoints?") if ret: ret = sd.askstring(title="output_name", prompt="What do you want your output name to be?\n" "Cancel keeps outputs the original") if ret: args['change_output_name'] = ret else: args['change_output_name'] = None ret = sd.askstring(title="comment", prompt="Do you want to set a comment that gets put into the metadata?\nA good use of this would " "be to include how to use, such as activation keywords.\nCancel will leave empty") if ret is None: args['training_comment'] = ret else: args['training_comment'] = None return args def ask_elements(args: dict): # start with file dialog args['base_model'] = ask_file("Select your base model", {"ckpt", "safetensors"}, args['base_model']) args['img_folder'] = ask_dir("Select your image folder", args['img_folder']) args['output_folder'] = ask_dir("Select your output folder", args['output_folder']) # optional file dialog ret = mb.askyesno(message="Do you want to save a json of your configuration?") if ret: args['save_json_folder'] = ask_dir("Select the folder to save json files to", args['save_json_folder']) else: args['save_json_folder'] = None ret = mb.askyesno(message="Do you want to use regularization images?") if ret: args['reg_img_folder'] = ask_dir("Select your regularization folder", args['reg_img_folder']) else: args['reg_img_folder'] = None ret = mb.askyesno(message="Do you want to continue from an earlier version?") if ret: args['lora_model_for_resume'] = ask_file("Select your lora model", {"ckpt", "pt", "safetensors"}, args['lora_model_for_resume']) else: args['lora_model_for_resume'] = None # text based required elements ret = sd.askinteger(title="batch_size", prompt="The number of images that get processed at one time, this is directly proportional to " "your vram and resolution. with 12gb of vram, at 512 reso, you can get a maximum of 6 " "batch size\nHow large is your batch size going to be?\nCancel will default to 1") if ret is None: args['batch_size'] = 1 else: args['batch_size'] = ret ret = sd.askinteger(title="num_epochs", prompt="How many epochs do you want?\nCancel will default to 1") if ret is None: args['num_epochs'] = 1 else: args['num_epochs'] = ret ret = sd.askinteger(title="network_dim", prompt="What is the dim size you want to use?\nCancel will default to 128") if ret is None: args['net_dim'] = 128 else: args['net_dim'] = ret ret = sd.askfloat(title="alpha", prompt="Alpha is the scalar of the training, generally a good starting point is " "0.5x dim size\nWhat Alpha do you want?\nCancel will default to equal to " "0.5 x network_dim") if ret is None: args['alpha'] = args['net_dim'] / 2 else: args['alpha'] = ret ret = sd.askinteger(title="resolution", prompt="How large of a resolution do you want to train at?\n" "Cancel will default to 512") if ret is None: args['train_resolution'] = 512 else: args['train_resolution'] = ret ret = sd.askfloat(title="learning_rate", prompt="What learning rate do you want to use?\n" "Cancel will default to 1e-4") if ret is None: args['learning_rate'] = 1e-4 else: args['learning_rate'] = ret ret = sd.askfloat(title="text_encoder_lr", prompt="Do you want to set the text_encoder_lr?\n" "Cancel will default to None") if ret is None: args['text_encoder_lr'] = None else: args['text_encoder_lr'] = ret ret = sd.askfloat(title="unet_lr", prompt="Do you want to set the unet_lr?\nCancel will default to None") if ret is None: args['unet_lr'] = None else: args['unet_lr'] = ret ret = sd.askstring(title="scheduler", prompt="Which scheduler do you want?\n Cancel will default " "to \"cosine_with_restarts\"") if ret is None: args['scheduler'] = "cosine_with_restarts" else: schedulers = {"linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"} while ret not in schedulers: mb.showwarning(message=f"scheduler isn't valid.\nvalid schedulers are: {schedulers}") ret = sd.askstring(title="scheduler", prompt="Which scheduler do you want?\n Cancel will default " "to \"cosine_with_restarts\"") if ret is None: args['scheduler'] = "cosine_with_restarts" if args['scheduler'] == "cosine_with_restarts": ret = sd.askinteger(title="Cycle Count", prompt="How many times do you want cosine to restart?\nThis is the entire amount of times " "it will restart for the entire training\nCancel will default to 1") if ret is None: args['cosine_restarts'] = 1 else: args['cosine_restarts'] = ret if args['scheduler'] == "polynomial": ret = sd.askfloat(title="Poly Strength", prompt="What power do you want to set your polynomial to?\nhigher power means that the " "model reduces the learning more more aggressively from initial training.\n1 = " "linear\nCancel sets to 1") if ret is None: args['scheduler_power'] = 1 else: args['scheduler_power'] = ret ret = mb.askyesno(message="Do you want to save epochs as it trains?") if ret: ret = sd.askinteger(title="save_epoch", prompt="How often do you want to save epochs?\nCancel will default to 1") if ret is None: args['save_at_n_epochs'] = 1 else: args['save_at_n_epochs'] = ret ret = mb.askyesno(message="Do you want to shuffle captions?") if ret: args['shuffle_captions'] = True else: args['shuffle_captions'] = False ret = mb.askyesno(message="Do you want to keep some tokens at the front of your captions?") if ret: ret = sd.askinteger(title="keep_tokens", prompt="How many do you want to keep at the front?" "\nCancel will default to 1") if ret is None: args['keep_tokens'] = 1 else: args['keep_tokens'] = ret ret = mb.askyesno(message="Do you want to have a warmup ratio?") if ret: ret = sd.askfloat(title="warmup_ratio", prompt="What is the ratio of steps to use as warmup " "steps?\nCancel will default to None") if ret is None: args['warmup_lr_ratio'] = None else: args['warmup_lr_ratio'] = ret ret = mb.askyesno(message="Do you want to change the name of output checkpoints?") if ret: ret = sd.askstring(title="output_name", prompt="What do you want your output name to be?\n" "Cancel keeps outputs the original") if ret: args['change_output_name'] = ret else: args['change_output_name'] = None ret = sd.askstring(title="comment", prompt="Do you want to set a comment that gets put into the metadata?\nA good use of this would " "be to include how to use, such as activation keywords.\nCancel will leave empty") if ret is None: args['training_comment'] = ret else: args['training_comment'] = None return args def save_json(path, obj: dict) -> None: fp = open(os.path.join(path, f"config-{time.time()}.json"), "w") json.dump(obj, fp=fp, indent=4) fp.close() def load_json(path, obj: dict) -> dict: with open(path) as f: json_obj = json.loads(f.read()) print("loaded json, setting variables...") ui_name_scheme = {"pretrained_model_name_or_path": "base_model", "logging_dir": "log_dir", "train_data_dir": "img_folder", "reg_data_dir": "reg_img_folder", "output_dir": "output_folder", "max_resolution": "train_resolution", "lr_scheduler": "scheduler", "lr_warmup": "warmup_lr_ratio", "train_batch_size": "batch_size", "epoch": "num_epochs", "save_every_n_epochs": "save_at_n_epochs", "num_cpu_threads_per_process": "num_workers", "enable_bucket": "buckets", "save_model_as": "save_as", "shuffle_caption": "shuffle_captions", "resume": "load_previous_save_state", "network_dim": "net_dim", "gradient_accumulation_steps": "gradient_acc_steps", "output_name": "change_output_name", "network_alpha": "alpha", "lr_scheduler_num_cycles": "cosine_restarts", "lr_scheduler_power": "scheduler_power"} for key in list(json_obj): if key in ui_name_scheme: json_obj[ui_name_scheme[key]] = json_obj[key] if ui_name_scheme[key] in {"batch_size", "num_epochs"}: try: json_obj[ui_name_scheme[key]] = int(json_obj[ui_name_scheme[key]]) except ValueError: print(f"attempting to load {key} from json failed as input isn't an integer") quit(1) for key in list(json_obj): if obj["json_load_skip_list"] and key in obj["json_load_skip_list"]: continue if key in obj: if key in {"keep_tokens", "warmup_lr_ratio"}: json_obj[key] = int(json_obj[key]) if json_obj[key] is not None else None if key in {"learning_rate", "unet_lr", "text_encoder_lr"}: json_obj[key] = float(json_obj[key]) if json_obj[key] is not None else None if obj[key] != json_obj[key]: print_change(key, obj[key], json_obj[key]) obj[key] = json_obj[key] print("completed changing variables.") return obj def print_change(value, old, new): print(f"{value} changed from {old} to {new}") root = tk.Tk() root.attributes('-topmost', True) root.withdraw() if __name__ == "__main__": main()