wan_lora_trainer_gui.py

import tkinter as tk
from tkinter import ttk, filedialog, messagebox, Menu
import subprocess
import threading
import json
import os
import sys
import signal

# Dark theme color scheme
BG_COLOR = "#2C3E50"       # Main background (dark gray with blue tint)
FG_COLOR = "#ECF0F1"       # Light text
ACCENT_COLOR = "#2980B9"   # Blue accent for tabs
ENTRY_BG = "#1B2A38"       # Entry field background (darker than main)
BUTTON_ACTIVE = "#1B2A38"  # Active button background
BORDER_COLOR = "#333333"   # Dark border color
ACTIVE_ENTRY_BG = "white"  # Background color for active entry field
ACTIVE_ENTRY_FG = "black"  # Text color for active entry field

class LoRATrainerGUI:
    def __init__(self, master):
        self.master = master
        master.title("Wan 2.1 LoRA Trainer")
        master.geometry("900x1024")
        master.configure(bg=BG_COLOR)

        self.current_process = None
        self.training_thread = None
        self.process_group_id = None
        self.user_scrolled = False  # Flag for manual console scrolling

        # Initialize settings with default values, including conversion settings
        self.settings = {
            "DATASET_CONFIG": "dataset/dataset_example.toml",
            "VAE_MODEL": "Models/Wan/Wan2.1_VAE.pth",
            "CLIP_MODEL": "Models/Wan/models_clip_open-clip-xlm-roberta-large-vit-huge-14.pth",
            "T5_MODEL": "Models/Wan/models_t5_umt5-xxl-enc-bf16.pth",
            "DIT_MODEL": "Models/Wan/wan2.1_i2v_720p_14B_fp8_e4m3fn.safetensors",
            "LORA_OUTPUT_DIR": "Output_LoRAs/",
            "LORA_NAME": "My_Best_Lora_v1",
            "MODEL_TYPE": "i2v-14B",
            "FLOW_SHIFT": 3.0,
            "LEARNING_RATE": 2e-5,
            "LORA_LR_RATIO": 4,
            "NETWORK_DIM": 32,
            "NETWORK_ALPHA": 4,
            "MAX_TRAIN_EPOCHS": 70,
            "SAVE_EVERY_N_EPOCHS": 10,
            "SEED": 1234,
            "BLOCKS_SWAP": 16,
            "RESUME_TRAINING": "",
            "OPTIMIZER_TYPE": "adamw8bit",
            "OPTIMIZER_ARGS": "",
            "ATTENTION_MECHANISM": "none",
            "LOGGING_DIR": "",
            "LOG_WITH": "none",
            "LOG_PREFIX": "",
            "IMG_IN_TXT_IN_OFFLOADING": False,
            "LR_SCHEDULER": "constant",
            "LR_WARMUP_STEPS": "",
            "LR_DECAY_STEPS": "",
            "TIMESTEP_SAMPLING": "shift",
            "DISCRETE_FLOW_SHIFT": "3.0",
            "WEIGHTING_SCHEME": "none",
            "METADATA_TITLE": "",
            "METADATA_AUTHOR": "",
            "METADATA_DESCRIPTION": "",
            "METADATA_LICENSE": "",
            "METADATA_TAGS": "",
            "INPUT_LORA": "",
            "OUTPUT_DIR": "",
            "CONVERTED_LORA_NAME": "",
            "FP8": True,  # Default FP8 setting
            "SCALED": False  # Default Scaled setting
        }

        self.model_types = ["t2v-1.3B", "t2v-14B", "i2v-14B", "t2i-14B"]
        self.optimizer_types = ["adamw", "adamw8bit", "adafactor", "torch.optim.AdamW", "bitsandbytes.optim.AdEMAMix8bit", "bitsandbytes.optim.PagedAdEMAMix8bit", "came"]

        self.setup_styles()

        # Create notebook and tabs
        self.notebook = ttk.Notebook(master)
        self.notebook.pack(fill=tk.BOTH, expand=True, padx=10, pady=10)

        # Создание вкладок с привязкой события клика мыши
        self.training_tab = ttk.Frame(self.notebook)
        self.training_tab.bind("<Button-1>", self.remove_focus)  # Привязка клика для снятия фокуса
        self.notebook.add(self.training_tab, text="Training settings")

        self.advanced_tab = ttk.Frame(self.notebook)
        self.advanced_tab.bind("<Button-1>", self.remove_focus)  # Привязка клика для снятия фокуса
        self.notebook.add(self.advanced_tab, text="Advanced settings")

        self.conversion_tab = ttk.Frame(self.notebook)
        self.conversion_tab.bind("<Button-1>", self.remove_focus)  # Привязка клика для снятия фокуса
        self.notebook.add(self.conversion_tab, text="LoRA Conversion")

        # Initialize tab contents
        self.create_training_settings()
        self.create_advanced_settings()
        self.create_conversion_settings()

        # Create context menu for copying console text
        self.context_menu = Menu(self.master, tearoff=0)
        self.context_menu.add_command(label="Copy", command=self.copy_selected_text)

    def remove_focus(self, event):
        """Снимает фокус с активного виджета при клике по фону"""
        self.master.focus_set()

    def setup_styles(self):
        """Set up styles for dark theme"""
        style = ttk.Style()
        style.theme_use("clam")

        style.configure(".", background=BG_COLOR, foreground=FG_COLOR)
        style.configure("TFrame", background=BG_COLOR)
        style.configure("TLabel", background=BG_COLOR, foreground=FG_COLOR)

        style.configure(
            "TButton",
            background=BG_COLOR,
            foreground=FG_COLOR,
            bordercolor=BORDER_COLOR,
            borderwidth=1,
            focusthickness=3,
            focuscolor=BG_COLOR,
            padding=[5, 1]
        )
        style.map(
            "TButton",
            background=[("active", BUTTON_ACTIVE), ("pressed", BUTTON_ACTIVE)],
            foreground=[("active", FG_COLOR), ("pressed", FG_COLOR)]
        )

        style.configure("TCheckbutton", background=BG_COLOR, foreground=FG_COLOR)
        style.map("TCheckbutton", background=[("active", BG_COLOR)], foreground=[("active", FG_COLOR)])

        style.configure("TNotebook", background=BG_COLOR, borderwidth=0)
        style.configure("TNotebook.Tab", background=BG_COLOR, foreground=FG_COLOR, padding=[5, 2])
        style.map("TNotebook.Tab", background=[("selected", ACCENT_COLOR)], foreground=[("selected", FG_COLOR)])

        style.configure(
            "TEntry",
            fieldbackground=ENTRY_BG,
            foreground=FG_COLOR,
            bordercolor=BORDER_COLOR
        )
        style.map("TEntry",
            fieldbackground=[("focus", ACTIVE_ENTRY_BG)],
            foreground=[("focus", ACTIVE_ENTRY_FG)]
        )

        style.configure(
            "TCombobox",
            fieldbackground=ENTRY_BG,
            background=BG_COLOR,
            foreground=FG_COLOR,
            bordercolor=BORDER_COLOR
        )
        style.map("TCombobox",
            fieldbackground=[("focus", ACTIVE_ENTRY_BG), ("readonly", ENTRY_BG), ("!disabled", ENTRY_BG)],
            foreground=[("focus", ACTIVE_ENTRY_FG), ("readonly", FG_COLOR), ("!disabled", FG_COLOR)],
            selectbackground=[("readonly", ENTRY_BG), ("!disabled", ENTRY_BG)],
            selectforeground=[("readonly", FG_COLOR), ("!disabled", FG_COLOR)]
        )

        style.configure(
            "Vertical.TScrollbar",
            background=ENTRY_BG,
            troughcolor=BG_COLOR,
            bordercolor=BORDER_COLOR,
            arrowcolor=FG_COLOR,
            darkcolor=BG_COLOR,
            lightcolor=BG_COLOR
        )
        style.map(
            "Vertical.TScrollbar",
            background=[("active", BUTTON_ACTIVE), ("pressed", BUTTON_ACTIVE)]
        )

    def create_training_settings(self):
        row = 0

        ttk.Label(self.training_tab, text="Training Settings", font=("Arial", 12, "bold")).grid(
            row=row, column=0, columnspan=3, pady=(10, 10)
        )
        row += 1

        button_frame_top = ttk.Frame(self.training_tab)
        button_frame_top.grid(row=row, column=0, columnspan=3, pady=5)
        ttk.Button(button_frame_top, text="Load Settings", command=self.load_settings).pack(side=tk.LEFT, padx=10)
        ttk.Button(button_frame_top, text="Save Settings", command=self.save_settings).pack(side=tk.LEFT, padx=10)
        row += 1

        settings_config = [
            ("Dataset Config", "DATASET_CONFIG", "file"),
            ("VAE Model", "VAE_MODEL", "file"),
            ("Clip Model", "CLIP_MODEL", "file"),
            ("T5 Model", "T5_MODEL", "file"),
            ("Dit Model", "DIT_MODEL", "file"),
            ("LoRA Output Dir", "LORA_OUTPUT_DIR", "directory"),
            ("LoRA Name", "LORA_NAME", "text"),
            ("Model Type", "MODEL_TYPE", "dropdown"),
            ("Flow Shift", "FLOW_SHIFT", "float"),
            ("Learning Rate", "LEARNING_RATE", "float"),
            ("LoRA LR Ratio", "LORA_LR_RATIO", "int"),
            ("Network Dim", "NETWORK_DIM", "int"),
            ("Network Alpha", "NETWORK_ALPHA", "float"),
            ("Max Train Epochs", "MAX_TRAIN_EPOCHS", "int"),
            ("Save Every N Epochs", "SAVE_EVERY_N_EPOCHS", "int"),
            ("Seed", "SEED", "int"),
            ("Blocks Swap", "BLOCKS_SWAP", "int"),
            ("Resume Training", "RESUME_TRAINING", "directory"),
            ("Optimizer Type", "OPTIMIZER_TYPE", "dropdown"),
            ("Optimizer Args", "OPTIMIZER_ARGS", "text"),
        ]

        self.entries = {}

        for label_text, key, input_type in settings_config:
            ttk.Label(self.training_tab, text=f"{label_text}:").grid(
                row=row, column=0, sticky=tk.W, padx=5, pady=2
            )

            if input_type == "dropdown":
                if key == "MODEL_TYPE":
                    var = tk.StringVar(value=self.settings[key])
                    self.entries[key] = ttk.Combobox(
                        self.training_tab, textvariable=var, values=self.model_types, state="readonly"
                    )
                    self.entries[key].current(self.model_types.index(self.settings[key]))
                elif key == "OPTIMIZER_TYPE":
                    var = tk.StringVar(value=self.settings[key])
                    self.entries[key] = ttk.Combobox(
                        self.training_tab, textvariable=var, values=self.optimizer_types, state="readonly"
                    )
                    self.entries[key].current(self.optimizer_types.index(self.settings[key]))
            else:
                self.entries[key] = ttk.Entry(self.training_tab, width=40)
                self.entries[key].insert(0, self.settings[key])

            self.entries[key].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)

            if input_type in ["file", "directory"]:
                ttk.Button(
                    self.training_tab,
                    text="Browse",
                    command=lambda k=key, t=input_type: self.browse_file(k, t)
                ).grid(row=row, column=2, sticky=tk.W, padx=5)

            row += 1

        # Weight Optimization Checkboxes
        ttk.Label(self.training_tab, text="Weight Optimization:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.fp8_var = tk.BooleanVar(value=self.settings["FP8"])
        self.scaled_var = tk.BooleanVar(value=self.settings["SCALED"])
        self.fp8_check = ttk.Checkbutton(self.training_tab, text="FP8 Base", variable=self.fp8_var, command=self.toggle_scaled)
        self.fp8_check.grid(row=row, column=1, sticky=tk.W, padx=5, pady=2)
        self.scaled_check = ttk.Checkbutton(self.training_tab, text="FP8 Scaled", variable=self.scaled_var, state=tk.DISABLED if not self.fp8_var.get() else tk.NORMAL)
        self.scaled_check.grid(row=row, column=1, sticky=tk.W, padx=100, pady=2)
        row += 1

        self.enable_cache_var = tk.BooleanVar(value=True)
        ttk.Checkbutton(
            self.training_tab, text="Enable Cache Preparation", variable=self.enable_cache_var
        ).grid(row=row, column=0, columnspan=3, pady=5)
        row += 1

        button_frame = ttk.Frame(self.training_tab)
        button_frame.grid(row=row, column=0, columnspan=3, pady=10)

        ttk.Button(button_frame, text="Start Training", command=self.start_training).pack(side=tk.LEFT, padx=10)
        ttk.Button(button_frame, text="Stop Training", command=self.stop_training).pack(side=tk.LEFT, padx=10)
        row += 1

        self.console_frame = ttk.Frame(self.training_tab)
        self.console_frame.grid(row=row, column=0, columnspan=3, padx=5, pady=5, sticky="nsew")

        self.console_output = tk.Text(
            self.console_frame,
            height=10,
            width=80,
            bg=ENTRY_BG,
            fg=FG_COLOR,
            wrap="word",
            state="disabled",
            selectbackground="white",
            selectforeground="black"
        )
        self.console_output.grid(row=0, column=0, sticky="nsew")

        self.console_scrollbar = ttk.Scrollbar(
            self.console_frame,
            orient="vertical",
            command=self.console_output.yview,
            style="Vertical.TScrollbar"
        )
        self.console_scrollbar.grid(row=0, column=1, sticky="ns")

        self.console_output.configure(yscrollcommand=self.console_scrollbar.set)

        self.console_output.bind("<MouseWheel>", self.on_mousewheel)
        self.console_output.bind("<Button-4>", self.on_mousewheel)  # For Linux
        self.console_output.bind("<Button-5>", self.on_mousewheel)  # For Linux
        self.console_output.bind("<Button-3>", self.show_context_menu)

        self.training_tab.grid_rowconfigure(row, weight=1)
        self.training_tab.grid_columnconfigure(1, weight=1)
        self.console_frame.grid_rowconfigure(0, weight=1)
        self.console_frame.grid_columnconfigure(0, weight=1)

    def toggle_scaled(self):
        """Enable or disable the Scaled checkbox based on FP8 checkbox state"""
        if self.fp8_var.get():
            self.scaled_check.config(state=tk.NORMAL)
        else:
            self.scaled_check.config(state=tk.DISABLED)
            self.scaled_var.set(False)

    def create_advanced_settings(self):
        row = 0

        ttk.Label(self.advanced_tab, text="Advanced Settings", font=("Arial", 12, "bold")).grid(
            row=row, column=0, columnspan=3, pady=(10, 10)
        )
        row += 1

        # Attention Mechanism
        ttk.Label(self.advanced_tab, text="Attention Mechanism:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.attention_var = tk.StringVar(value=self.settings["ATTENTION_MECHANISM"])
        attention_options = ["none", "sdpa", "flash_attn", "sage_attn", "xformers", "flash3", "split_attn"]
        self.entries["ATTENTION_MECHANISM"] = ttk.Combobox(self.advanced_tab, textvariable=self.attention_var, values=attention_options, state="readonly")
        self.entries["ATTENTION_MECHANISM"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        # Logging
        ttk.Label(self.advanced_tab, text="Logging Directory:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.entries["LOGGING_DIR"] = ttk.Entry(self.advanced_tab, width=40)
        self.entries["LOGGING_DIR"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        ttk.Button(self.advanced_tab, text="Browse", command=lambda: self.browse_directory("LOGGING_DIR")).grid(row=row, column=2, sticky=tk.W, padx=5)
        row += 1

        ttk.Label(self.advanced_tab, text="Log With:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.log_with_var = tk.StringVar(value=self.settings["LOG_WITH"])
        log_with_options = ["none", "tensorboard", "wandb", "all"]
        self.entries["LOG_WITH"] = ttk.Combobox(self.advanced_tab, textvariable=self.log_with_var, values=log_with_options, state="readonly")
        self.entries["LOG_WITH"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        ttk.Label(self.advanced_tab, text="Log Prefix:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.entries["LOG_PREFIX"] = ttk.Entry(self.advanced_tab, width=40)
        self.entries["LOG_PREFIX"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        # Memory Management
        self.img_in_txt_in_offloading_var = tk.BooleanVar(value=self.settings["IMG_IN_TXT_IN_OFFLOADING"])
        ttk.Checkbutton(self.advanced_tab, text="Offload img_in and txt_in to CPU", variable=self.img_in_txt_in_offloading_var).grid(row=row, column=0, columnspan=3, pady=5)
        self.entries["IMG_IN_TXT_IN_OFFLOADING"] = self.img_in_txt_in_offloading_var
        row += 1

        # Learning Rate Scheduler
        ttk.Label(self.advanced_tab, text="LR Scheduler:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.lr_scheduler_var = tk.StringVar(value=self.settings["LR_SCHEDULER"])
        lr_scheduler_options = ["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup", "adafactor"]
        self.entries["LR_SCHEDULER"] = ttk.Combobox(self.advanced_tab, textvariable=self.lr_scheduler_var, values=lr_scheduler_options, state="readonly")
        self.entries["LR_SCHEDULER"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        ttk.Label(self.advanced_tab, text="LR Warmup Steps:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.entries["LR_WARMUP_STEPS"] = ttk.Entry(self.advanced_tab, width=40)
        self.entries["LR_WARMUP_STEPS"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        ttk.Label(self.advanced_tab, text="LR Decay Steps:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.entries["LR_DECAY_STEPS"] = ttk.Entry(self.advanced_tab, width=40)
        self.entries["LR_DECAY_STEPS"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        # Timestep Sampling
        ttk.Label(self.advanced_tab, text="Timestep Sampling:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.timestep_sampling_var = tk.StringVar(value=self.settings["TIMESTEP_SAMPLING"])
        timestep_sampling_options = ["sigma", "uniform", "sigmoid", "shift"]
        self.entries["TIMESTEP_SAMPLING"] = ttk.Combobox(self.advanced_tab, textvariable=self.timestep_sampling_var, values=timestep_sampling_options, state="readonly")
        self.entries["TIMESTEP_SAMPLING"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        ttk.Label(self.advanced_tab, text="Discrete Flow Shift:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.entries["DISCRETE_FLOW_SHIFT"] = ttk.Entry(self.advanced_tab, width=40)
        self.entries["DISCRETE_FLOW_SHIFT"].insert(0, self.settings["DISCRETE_FLOW_SHIFT"])
        self.entries["DISCRETE_FLOW_SHIFT"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        # Weighting Scheme
        ttk.Label(self.advanced_tab, text="Weighting Scheme:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.weighting_scheme_var = tk.StringVar(value=self.settings["WEIGHTING_SCHEME"])
        weighting_scheme_options = ["logit_normal", "mode", "cosmap", "sigma_sqrt", "none"]
        self.entries["WEIGHTING_SCHEME"] = ttk.Combobox(self.advanced_tab, textvariable=self.weighting_scheme_var, values=weighting_scheme_options, state="readonly")
        self.entries["WEIGHTING_SCHEME"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        # Metadata
        ttk.Label(self.advanced_tab, text="Metadata Title:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.entries["METADATA_TITLE"] = ttk.Entry(self.advanced_tab, width=40)
        self.entries["METADATA_TITLE"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        ttk.Label(self.advanced_tab, text="Metadata Author:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.entries["METADATA_AUTHOR"] = ttk.Entry(self.advanced_tab, width=40)
        self.entries["METADATA_AUTHOR"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        ttk.Label(self.advanced_tab, text="Metadata Description:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.entries["METADATA_DESCRIPTION"] = ttk.Entry(self.advanced_tab, width=40)
        self.entries["METADATA_DESCRIPTION"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        ttk.Label(self.advanced_tab, text="Metadata License:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.entries["METADATA_LICENSE"] = ttk.Entry(self.advanced_tab, width=40)
        self.entries["METADATA_LICENSE"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        ttk.Label(self.advanced_tab, text="Metadata Tags:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.entries["METADATA_TAGS"] = ttk.Entry(self.advanced_tab, width=40)
        self.entries["METADATA_TAGS"].grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        row += 1

        # Настройка столбца для автоматического расширения
        self.advanced_tab.grid_columnconfigure(1, weight=1)

    def create_conversion_settings(self):
        """Create the LoRA Conversion tab with input fields and buttons"""
        row = 0

        ttk.Label(self.conversion_tab, text="LoRA Conversion Settings", font=("Arial", 12, "bold")).grid(
            row=row, column=0, columnspan=3, pady=(10, 10)
        )
        row += 1

        # Input LoRA File
        ttk.Label(self.conversion_tab, text="Input LoRA File:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.input_lora_entry = ttk.Entry(self.conversion_tab, width=40)
        self.input_lora_entry.grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        self.input_lora_entry.insert(0, self.settings["INPUT_LORA"])
        ttk.Button(self.conversion_tab, text="Browse", command=self.browse_input_lora).grid(row=row, column=2, sticky=tk.W, padx=5)
        row += 1

        # Output Directory
        ttk.Label(self.conversion_tab, text="Output Directory:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.output_dir_entry = ttk.Entry(self.conversion_tab, width=40)
        self.output_dir_entry.grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        self.output_dir_entry.insert(0, self.settings["OUTPUT_DIR"])
        ttk.Button(self.conversion_tab, text="Browse", command=self.browse_output_dir).grid(row=row, column=2, sticky=tk.W, padx=5)
        row += 1

        # Converted LoRA Name
        ttk.Label(self.conversion_tab, text="Converted LoRA Name:").grid(row=row, column=0, sticky=tk.W, padx=5, pady=2)
        self.converted_lora_name_entry = ttk.Entry(self.conversion_tab, width=40)
        self.converted_lora_name_entry.grid(row=row, column=1, sticky=tk.EW, padx=5, pady=2)
        self.converted_lora_name_entry.insert(0, self.settings["CONVERTED_LORA_NAME"])
        row += 1

        # Convert Button
        ttk.Button(self.conversion_tab, text="Convert", command=self.convert_lora).grid(row=row, column=0, columnspan=3, pady=10)

        # Configure grid to expand horizontally
        self.conversion_tab.grid_columnconfigure(1, weight=1)

        # Add entries to self.entries for saving/loading
        self.entries["INPUT_LORA"] = self.input_lora_entry
        self.entries["OUTPUT_DIR"] = self.output_dir_entry
        self.entries["CONVERTED_LORA_NAME"] = self.converted_lora_name_entry

    def show_context_menu(self, event):
        """Show context menu on right-click"""
        try:
            self.context_menu.tk_popup(event.x_root, event.y_root)
        finally:
            self.context_menu.grab_release()

    def copy_selected_text(self):
        """Copy selected text to clipboard"""
        if self.console_output.selection_get():
            self.master.clipboard_clear()
            self.master.clipboard_append(self.console_output.selection_get())

    def browse_directory(self, setting_name):
        path = filedialog.askdirectory()
        if path:
            self.entries[setting_name].delete(0, tk.END)
            self.entries[setting_name].insert(0, path)

    def on_mousewheel(self, event):
        """Handle scroll event"""
        if self.console_output.yview()[1] < 1.0:
            self.user_scrolled = True
        else:
            self.user_scrolled = False

    def update_console(self, line):
        """Update console with scroll handling"""
        self.console_output.configure(state="normal")
        self.console_output.insert(tk.END, line)
        if not self.user_scrolled:
            self.console_output.yview(tk.END)
        self.console_output.configure(state="disabled")

    def browse_file(self, setting_name, input_type):
        if input_type == "directory":
            path = filedialog.askdirectory()
        else:
            path = filedialog.askopenfilename()
        if path:
            self.settings[setting_name] = path
            self.entries[setting_name].delete(0, tk.END)
            self.entries[setting_name].insert(0, self.settings[setting_name])

    def browse_input_lora(self):
        """Browse for input LoRA file"""
        file_path = filedialog.askopenfilename(filetypes=[("LoRA files", "*.safetensors")])
        if file_path:
            self.input_lora_entry.delete(0, tk.END)
            self.input_lora_entry.insert(0, file_path)

    def browse_output_dir(self):
        """Browse for output directory"""
        dir_path = filedialog.askdirectory()
        if dir_path:
            self.output_dir_entry.delete(0, tk.END)
            self.output_dir_entry.insert(0, dir_path)

    def convert_lora(self):
        """Convert the LoRA model using specified settings"""
        input_path = self.input_lora_entry.get()
        output_dir = self.output_dir_entry.get()
        converted_name = self.converted_lora_name_entry.get()

        if not input_path or not output_dir or not converted_name:
            messagebox.showerror("Error", "Please fill in all fields.")
            return

        output_path = os.path.join(output_dir, converted_name + ".safetensors")

        command = [
            sys.executable, "convert_lora.py",
            "--input", input_path,
            "--output", output_path,
            "--target", "other"
        ]

        self.run_subprocess(command, "Conversion")

    def run_subprocess(self, cmd, name, callback=None):
        """Run a subprocess and handle its output with UTF-8 encoding"""
        env = os.environ.copy()
        env["PYTHONIOENCODING"] = "utf-8"  # Устанавливаем UTF-8 для среды выполнения

        if os.name == 'nt':
            creationflags = subprocess.CREATE_NEW_PROCESS_GROUP
            preexec_fn = None
        else:
            creationflags = 0
            preexec_fn = os.setsid

        # Запускаем подпроцесс с явным указанием кодировки UTF-8
        process = subprocess.Popen(
            cmd,
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
            text=True,  # Включаем текстовый режим для автоматической декодировки
            bufsize=1,  # Построчная буферизация
            universal_newlines=True,  # Поддержка универсальных переносов строк
            encoding='utf-8',  # Явно указываем кодировку UTF-8 для вывода
            env=env,
            creationflags=creationflags,
            preexec_fn=preexec_fn
        )
        self.current_process = process
        if os.name == 'nt':
            self.process_group_id = process.pid

        def read_output(pipe, output_type):
            """Читает вывод подпроцесса построчно"""
            while True:
                line = pipe.readline()
                if not line:
                    break
                self.master.after(0, self.update_console, f"{name} {output_type}: {line}")
            pipe.close()

        # Запускаем потоки для чтения stdout и stderr
        threading.Thread(target=read_output, args=(process.stdout, "STDOUT"), daemon=True).start()
        threading.Thread(target=read_output, args=(process.stderr, "STDERR"), daemon=True).start()

        def check_process():
            """Проверяет завершение подпроцесса"""
            process.wait()
            self.master.after(0, self.update_console, f"{name} process completed.\n")
            self.current_process = None
            if callback:
                callback()

        threading.Thread(target=check_process, daemon=True).start()

    def start_training(self):
        """Запускает обучение с последовательным выполнением процессов кэширования"""
        # Check for unsupported optimizer
        optimizer_type = self.entries["OPTIMIZER_TYPE"].get()
        if optimizer_type == "came":
            messagebox.showwarning(
                "Предупреждение",
                "Оптимизатор 'came' не поддерживается в текущей версии. Пожалуйста, выберите другой оптимизатор, например 'adamw' или 'adamw8bit'."
            )
            return

        # Update settings from entries
        self.settings.update({
            "MODEL_TYPE": self.entries["MODEL_TYPE"].get(),
            "FLOW_SHIFT": float(self.entries["FLOW_SHIFT"].get()),
            "LEARNING_RATE": float(self.entries["LEARNING_RATE"].get()),
            "LORA_LR_RATIO": int(self.entries["LORA_LR_RATIO"].get()),
            "NETWORK_DIM": int(self.entries["NETWORK_DIM"].get()),
            "NETWORK_ALPHA": float(self.entries["NETWORK_ALPHA"].get()),
            "MAX_TRAIN_EPOCHS": int(self.entries["MAX_TRAIN_EPOCHS"].get()),
            "SAVE_EVERY_N_EPOCHS": int(self.entries["SAVE_EVERY_N_EPOCHS"].get()),
            "SEED": int(self.entries["SEED"].get()),
            "BLOCKS_SWAP": int(self.entries["BLOCKS_SWAP"].get()),
            "DATASET_CONFIG": self.entries["DATASET_CONFIG"].get(),
            "VAE_MODEL": self.entries["VAE_MODEL"].get(),
            "CLIP_MODEL": self.entries["CLIP_MODEL"].get(),
            "T5_MODEL": self.entries["T5_MODEL"].get(),
            "DIT_MODEL": self.entries["DIT_MODEL"].get(),
            "LORA_OUTPUT_DIR": self.entries["LORA_OUTPUT_DIR"].get(),
            "LORA_NAME": self.entries["LORA_NAME"].get(),
            "RESUME_TRAINING": self.entries["RESUME_TRAINING"].get(),
            "OPTIMIZER_TYPE": optimizer_type,
            "OPTIMIZER_ARGS": self.entries["OPTIMIZER_ARGS"].get(),
            "ATTENTION_MECHANISM": self.entries["ATTENTION_MECHANISM"].get(),
            "LOGGING_DIR": self.entries["LOGGING_DIR"].get(),
            "LOG_WITH": self.entries["LOG_WITH"].get(),
            "LOG_PREFIX": self.entries["LOG_PREFIX"].get(),
            "IMG_IN_TXT_IN_OFFLOADING": self.entries["IMG_IN_TXT_IN_OFFLOADING"].get(),
            "LR_SCHEDULER": self.entries["LR_SCHEDULER"].get(),
            "LR_WARMUP_STEPS": self.entries["LR_WARMUP_STEPS"].get(),
            "LR_DECAY_STEPS": self.entries["LR_DECAY_STEPS"].get(),
            "TIMESTEP_SAMPLING": self.entries["TIMESTEP_SAMPLING"].get(),
            "DISCRETE_FLOW_SHIFT": self.entries["DISCRETE_FLOW_SHIFT"].get(),
            "WEIGHTING_SCHEME": self.entries["WEIGHTING_SCHEME"].get(),
            "METADATA_TITLE": self.entries["METADATA_TITLE"].get(),
            "METADATA_AUTHOR": self.entries["METADATA_AUTHOR"].get(),
            "METADATA_DESCRIPTION": self.entries["METADATA_DESCRIPTION"].get(),
            "METADATA_LICENSE": self.entries["METADATA_LICENSE"].get(),
            "METADATA_TAGS": self.entries["METADATA_TAGS"].get(),
            "FP8": self.fp8_var.get(),
            "SCALED": self.scaled_var.get()
        })

        # Build training command
        command = [
            "accelerate", "launch",
            "--num_cpu_threads_per_process", "2",
            "--mixed_precision", "bf16",
            "wan_train_network.py",
            "--task", self.settings["MODEL_TYPE"],
            "--dit", self.settings["DIT_MODEL"],
            "--dataset_config", self.settings["DATASET_CONFIG"],
            "--sdpa",
            "--mixed_precision", "bf16",
        ]

        # Добавляем параметры для Weight Optimization
        if self.settings["FP8"]:
            command.append("--fp8_base")
            if self.settings["SCALED"]:
                command.append("--fp8_scaled")

        command.extend([
            "--blocks_to_swap", str(self.settings["BLOCKS_SWAP"]),
            "--optimizer_type", self.settings["OPTIMIZER_TYPE"],
            "--learning_rate", str(self.settings["LEARNING_RATE"]),
            "--gradient_checkpointing",
            "--max_data_loader_n_workers", "2",
            "--persistent_data_loader_workers",
            "--network_module", "networks.lora_wan",
            "--network_dim", str(self.settings["NETWORK_DIM"]),
            "--network_alpha", str(self.settings["NETWORK_ALPHA"]),
            "--network_args", f"loraplus_lr_ratio={self.settings['LORA_LR_RATIO']}",
            "--timestep_sampling", self.settings["TIMESTEP_SAMPLING"],
            "--discrete_flow_shift", str(self.settings["DISCRETE_FLOW_SHIFT"]),
            "--max_train_epochs", str(self.settings["MAX_TRAIN_EPOCHS"]),
            "--save_every_n_epochs", str(self.settings["SAVE_EVERY_N_EPOCHS"]),
            "--save_state",
            "--seed", str(self.settings["SEED"]),
            "--output_dir", self.settings["LORA_OUTPUT_DIR"],
            "--output_name", self.settings["LORA_NAME"],
        ])

        if self.settings["OPTIMIZER_ARGS"]:
            command.extend(["--optimizer_args", self.settings["OPTIMIZER_ARGS"]])

        attention = self.settings["ATTENTION_MECHANISM"]
        if attention != "none":
            command.append(f"--{attention}")

        logging_dir = self.settings["LOGGING_DIR"]
        if logging_dir:
            command.extend(["--logging_dir", logging_dir])

        log_with = self.settings["LOG_WITH"]
        if log_with != "none":
            command.extend(["--log_with", log_with])

        log_prefix = self.settings["LOG_PREFIX"]
        if log_prefix:
            command.extend(["--log_prefix", log_prefix])

        if self.settings["IMG_IN_TXT_IN_OFFLOADING"]:
            command.append("--img_in_txt_in_offloading")

        lr_scheduler = self.settings["LR_SCHEDULER"]
        if lr_scheduler:
            command.extend(["--lr_scheduler", lr_scheduler])

        lr_warmup_steps = self.settings["LR_WARMUP_STEPS"]
        if lr_warmup_steps:
            command.extend(["--lr_warmup_steps", lr_warmup_steps])

        lr_decay_steps = self.settings["LR_DECAY_STEPS"]
        if lr_decay_steps:
            command.extend(["--lr_decay_steps", lr_decay_steps])

        weighting_scheme = self.settings["WEIGHTING_SCHEME"]
        if weighting_scheme != "none":
            command.extend(["--weighting_scheme", weighting_scheme])

        metadata_title = self.settings["METADATA_TITLE"]
        if metadata_title:
            command.extend(["--metadata_title", metadata_title])

        metadata_author = self.settings["METADATA_AUTHOR"]
        if metadata_author:
            command.extend(["--metadata_author", metadata_author])

        metadata_description = self.settings["METADATA_DESCRIPTION"]
        if metadata_description:
            command.extend(["--metadata_description", metadata_description])

        metadata_license = self.settings["METADATA_LICENSE"]
        if metadata_license:
            command.extend(["--metadata_license", metadata_license])

        metadata_tags = self.settings["METADATA_TAGS"]
        if metadata_tags:
            command.extend(["--metadata_tags", metadata_tags])

        if self.settings["RESUME_TRAINING"].strip():
            command.append(f"--resume={self.settings['RESUME_TRAINING']}")

        cache_preparation_command = [
            sys.executable, "wan_cache_latents.py",
            "--dataset_config", self.settings["DATASET_CONFIG"],
            "--vae", self.settings["VAE_MODEL"],
            "--clip", self.settings["CLIP_MODEL"]
        ]

        text_encoder_caching_command = [
            sys.executable, "wan_cache_text_encoder_outputs.py",
            "--dataset_config", self.settings["DATASET_CONFIG"],
            "--t5", self.settings["T5_MODEL"],
            "--batch_size", "16",
            "--fp8_t5"
        ]

        self.console_output.configure(state="normal")
        self.console_output.delete(1.0, tk.END)
        self.console_output.configure(state="disabled")

        if self.enable_cache_var.get():
            self.update_console("Starting cache preparation...\n")

            def on_text_encoder_caching_complete():
                self.update_console("Text encoder caching completed.\nStarting training...\n")
                self.run_subprocess(command, "Training")

            def on_cache_preparation_complete():
                self.update_console("Cache preparation completed.\nStarting text encoder caching...\n")
                self.run_subprocess(text_encoder_caching_command, "Text Encoder Caching", on_text_encoder_caching_complete)

            self.run_subprocess(cache_preparation_command, "Cache Preparation", on_cache_preparation_complete)
        else:
            self.update_console("Starting training without caching...\n")
            self.run_subprocess(command, "Training")

    def stop_training(self):
        """Stop the current running process"""
        if self.current_process and self.current_process.poll() is None:
            try:
                if os.name == 'nt':
                    self.current_process.send_signal(signal.CTRL_BREAK_EVENT)
                else:
                    os.killpg(os.getpgid(self.current_process.pid), signal.SIGTERM)
            except Exception as e:
                self.update_console("Error stopping process: " + str(e) + "\n")
            try:
                self.current_process.wait(timeout=5)
            except subprocess.TimeoutExpired:
                try:
                    self.current_process.kill()
                    self.current_process.wait()
                except Exception as e:
                    self.update_console("Error killing process: " + str(e) + "\n")
            self.current_process = None
            if self.training_thread:
                self.training_thread.join(timeout=1)
                self.training_thread = None
            self.update_console("Training stopped\n")
        else:
            self.update_console("No active process to stop\n")

    def save_settings(self):
        """Save all settings, including conversion settings, to a JSON file"""
        current_settings = {}
        for key, entry in self.entries.items():
            if isinstance(entry, ttk.Combobox):
                current_settings[key] = entry.get()
            elif isinstance(entry, tk.BooleanVar):
                current_settings[key] = entry.get()
            else:
                current_settings[key] = entry.get()
        current_settings["FP8"] = self.fp8_var.get()
        current_settings["SCALED"] = self.scaled_var.get()
        current_settings["ENABLE_CACHE"] = self.enable_cache_var.get()
        file_path = filedialog.asksaveasfilename(defaultextension=".json", filetypes=[("JSON files", "*.json")])
        if file_path:
            with open(file_path, "w") as f:
                json.dump(current_settings, f, indent=4)

    def load_settings(self):
        """Load settings from a JSON file, including conversion settings"""
        file_path = filedialog.askopenfilename(filetypes=[("JSON files", "*.json")])
        if file_path:
            with open(file_path, "r") as f:
                loaded_settings = json.load(f)
            for key, value in loaded_settings.items():
                if key in self.entries:
                    if isinstance(self.entries[key], ttk.Combobox):
                        self.entries[key].set(value)
                    elif isinstance(self.entries[key], tk.BooleanVar):
                        self.entries[key].set(value)
                    else:
                        self.entries[key].delete(0, tk.END)
                        self.entries[key].insert(0, value)
            if "FP8" in loaded_settings:
                self.fp8_var.set(loaded_settings["FP8"])
            if "SCALED" in loaded_settings:
                self.scaled_var.set(loaded_settings["SCALED"])
            if "ENABLE_CACHE" in loaded_settings:
                self.enable_cache_var.set(loaded_settings["ENABLE_CACHE"])
            self.toggle_scaled()  # Update Scaled checkbox state based on FP8

root = tk.Tk()
gui = LoRATrainerGUI(root)
root.mainloop()