Delete app-backup1.py

app-backup1.py

@@ -1,1214 +0,0 @@
-import os
-import gradio as gr
-import json
-import logging
-import torch
-from PIL import Image
-import spaces
-from diffusers import DiffusionPipeline, AutoencoderTiny, AutoencoderKL, AutoPipelineForImage2Image
-from live_preview_helpers import calculate_shift, retrieve_timesteps, flux_pipe_call_that_returns_an_iterable_of_images
-from diffusers.utils import load_image
-from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, snapshot_download
-import copy
-import random
-import time
-import requests
-import pandas as pd
-from transformers import pipeline
-from gradio_imageslider import ImageSlider
-import numpy as np
-import warnings
-
-# 상단에 허깅페이스 USERNAME (해당 계정) 반드시 개별 지정할것
-USERNAME = "openfree"
-
-huggingface_token = os.getenv("HF_TOKEN")
-
-
-translator = pipeline("translation", model="Helsinki-NLP/opus-mt-ko-en", device="cpu")
-
-
-        
-#Load prompts for randomization
-df = pd.read_csv('prompts.csv', header=None)
-prompt_values = df.values.flatten()
-
-# Load LoRAs from JSON file
-with open('loras.json', 'r') as f:
-    loras = json.load(f)
-
-# Initialize the base model
-dtype = torch.bfloat16
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-# 공통 FLUX 모델 로드
-base_model = "black-forest-labs/FLUX.1-dev"
-pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=dtype).to(device)
-
-# LoRA를 위한 설정
-taef1 = AutoencoderTiny.from_pretrained("madebyollin/taef1", torch_dtype=dtype).to(device)
-good_vae = AutoencoderKL.from_pretrained(base_model, subfolder="vae", torch_dtype=dtype).to(device)
-
-# Image-to-Image 파이프라인 설정
-pipe_i2i = AutoPipelineForImage2Image.from_pretrained(
-    base_model,
-    vae=good_vae,
-    transformer=pipe.transformer,
-    text_encoder=pipe.text_encoder,
-    tokenizer=pipe.tokenizer,
-    text_encoder_2=pipe.text_encoder_2,
-    tokenizer_2=pipe.tokenizer_2,
-    torch_dtype=dtype
-).to(device)
-
-MAX_SEED = 2**32 - 1
-MAX_PIXEL_BUDGET = 1024 * 1024
-
-pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_pipe_call_that_returns_an_iterable_of_images.__get__(pipe)
-
-class calculateDuration:
-    def __init__(self, activity_name=""):
-        self.activity_name = activity_name
-
-    def __enter__(self):
-        self.start_time = time.time()
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        self.end_time = time.time()
-        self.elapsed_time = self.end_time - self.start_time
-        if self.activity_name:
-            print(f"Elapsed time for {self.activity_name}: {self.elapsed_time:.6f} seconds")
-        else:
-            print(f"Elapsed time: {self.elapsed_time:.6f} seconds")
-
-def download_file(url, directory=None):
-    if directory is None:
-        directory = os.getcwd()  # Use current working directory if not specified
-    
-    # Get the filename from the URL
-    filename = url.split('/')[-1]
-    
-    # Full path for the downloaded file
-    filepath = os.path.join(directory, filename)
-    
-    # Download the file
-    response = requests.get(url)
-    response.raise_for_status()  # Raise an exception for bad status codes
-    
-    # Write the content to the file
-    with open(filepath, 'wb') as file:
-        file.write(response.content)
-    
-    return filepath
-
-def update_selection(evt: gr.SelectData, selected_indices, loras_state, width, height):
-    selected_index = evt.index
-    selected_indices = selected_indices or []
-    if selected_index in selected_indices:
-        selected_indices.remove(selected_index)
-    else:
-        if len(selected_indices) < 3:
-            selected_indices.append(selected_index)
-        else:
-            gr.Warning("You can select up to 3 LoRAs, remove one to select a new one.")
-            return gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), width, height, gr.update(), gr.update(), gr.update()
-
-    selected_info_1 = "Select LoRA 1"
-    selected_info_2 = "Select LoRA 2"
-    selected_info_3 = "Select LoRA 3"
-    
-    lora_scale_1 = 1.15
-    lora_scale_2 = 1.15
-    lora_scale_3 = 1.15    
-    lora_image_1 = None
-    lora_image_2 = None
-    lora_image_3 = None
-    
-    if len(selected_indices) >= 1:
-        lora1 = loras_state[selected_indices[0]]
-        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
-        lora_image_1 = lora1['image']
-    if len(selected_indices) >= 2:
-        lora2 = loras_state[selected_indices[1]]
-        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
-        lora_image_2 = lora2['image']
-    if len(selected_indices) >= 3:
-        lora3 = loras_state[selected_indices[2]]
-        selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}](https://huggingface.co/{lora3['repo']}) ✨"
-        lora_image_3 = lora3['image']
-        
-    if selected_indices:
-        last_selected_lora = loras_state[selected_indices[-1]]
-        new_placeholder = f"Type a prompt for {last_selected_lora['title']}"
-    else:
-        new_placeholder = "Type a prompt after selecting a LoRA"
-
-    return gr.update(placeholder=new_placeholder), selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, width, height, lora_image_1, lora_image_2, lora_image_3
-
-def remove_lora(selected_indices, loras_state, index_to_remove):
-    if len(selected_indices) > index_to_remove:
-        selected_indices.pop(index_to_remove)
-    
-    selected_info_1 = "Select LoRA 1"
-    selected_info_2 = "Select LoRA 2"
-    selected_info_3 = "Select LoRA 3"
-    lora_scale_1 = 1.15
-    lora_scale_2 = 1.15
-    lora_scale_3 = 1.15
-    lora_image_1 = None
-    lora_image_2 = None
-    lora_image_3 = None
-    
-    for i, idx in enumerate(selected_indices):
-        lora = loras_state[idx]
-        if i == 0:
-            selected_info_1 = f"### LoRA 1 Selected: [{lora['title']}]({lora['repo']}) ✨"
-            lora_image_1 = lora['image']
-        elif i == 1:
-            selected_info_2 = f"### LoRA 2 Selected: [{lora['title']}]({lora['repo']}) ✨"
-            lora_image_2 = lora['image']
-        elif i == 2:
-            selected_info_3 = f"### LoRA 3 Selected: [{lora['title']}]({lora['repo']}) ✨"
-            lora_image_3 = lora['image']
-    
-    return selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3
-
-def remove_lora_1(selected_indices, loras_state):
-    return remove_lora(selected_indices, loras_state, 0)
-
-def remove_lora_2(selected_indices, loras_state):
-    return remove_lora(selected_indices, loras_state, 1)
-
-def remove_lora_3(selected_indices, loras_state):
-    return remove_lora(selected_indices, loras_state, 2)
-
-def randomize_loras(selected_indices, loras_state):
-    try:
-        if len(loras_state) < 3:
-            raise gr.Error("Not enough LoRAs to randomize.")
-        selected_indices = random.sample(range(len(loras_state)), 3)
-        lora1 = loras_state[selected_indices[0]]
-        lora2 = loras_state[selected_indices[1]]
-        lora3 = loras_state[selected_indices[2]]
-        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}](https://huggingface.co/{lora1['repo']}) ✨"
-        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}](https://huggingface.co/{lora2['repo']}) ✨"
-        selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}](https://huggingface.co/{lora3['repo']}) ✨"
-        lora_scale_1 = 1.15
-        lora_scale_2 = 1.15
-        lora_scale_3 = 1.15
-        lora_image_1 = lora1.get('image', 'path/to/default/image.png')
-        lora_image_2 = lora2.get('image', 'path/to/default/image.png')
-        lora_image_3 = lora3.get('image', 'path/to/default/image.png')
-        random_prompt = random.choice(prompt_values)
-        return selected_info_1, selected_info_2, selected_info_3, selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, lora_image_1, lora_image_2, lora_image_3, random_prompt
-    except Exception as e:
-        print(f"Error in randomize_loras: {str(e)}")
-        return "Error", "Error", "Error", [], 1.15, 1.15, 1.15, 'path/to/default/image.png', 'path/to/default/image.png', 'path/to/default/image.png', ""
-
-def add_custom_lora(custom_lora, selected_indices, current_loras):
-    if custom_lora:
-        try:
-            title, repo, path, trigger_word, image = check_custom_model(custom_lora)
-            print(f"Loaded custom LoRA: {repo}")
-            existing_item_index = next((index for (index, item) in enumerate(current_loras) if item['repo'] == repo), None)
-            if existing_item_index is None:
-                if repo.endswith(".safetensors") and repo.startswith("http"):
-                    repo = download_file(repo)
-                new_item = {
-                    "image": image if image else "/home/user/app/custom.png",
-                    "title": title,
-                    "repo": repo,
-                    "weights": path,
-                    "trigger_word": trigger_word
-                }
-                print(f"New LoRA: {new_item}")
-                existing_item_index = len(current_loras)
-                current_loras.append(new_item)
-            
-            # Update gallery
-            gallery_items = [(item["image"], item["title"]) for item in current_loras]
-            # Update selected_indices if there's room
-            if len(selected_indices) < 3:
-                selected_indices.append(existing_item_index)
-            else:
-                gr.Warning("You can select up to 3 LoRAs, remove one to select a new one.")
-
-            # Update selected_info and images
-            selected_info_1 = "Select a LoRA 1"
-            selected_info_2 = "Select a LoRA 2"
-            selected_info_3 = "Select a LoRA 3"
-            lora_scale_1 = 1.15
-            lora_scale_2 = 1.15
-            lora_scale_3 = 1.15
-            lora_image_1 = None
-            lora_image_2 = None
-            lora_image_3 = None
-            if len(selected_indices) >= 1:
-                lora1 = current_loras[selected_indices[0]]
-                selected_info_1 = f"### LoRA 1 Selected: {lora1['title']} ✨"
-                lora_image_1 = lora1['image'] if lora1['image'] else None
-            if len(selected_indices) >= 2:
-                lora2 = current_loras[selected_indices[1]]
-                selected_info_2 = f"### LoRA 2 Selected: {lora2['title']} ✨"
-                lora_image_2 = lora2['image'] if lora2['image'] else None
-            if len(selected_indices) >= 3:
-                lora3 = current_loras[selected_indices[2]]
-                selected_info_3 = f"### LoRA 3 Selected: {lora3['title']} ✨"
-                lora_image_3 = lora3['image'] if lora3['image'] else None
-            print("Finished adding custom LoRA")
-            return (
-                current_loras,
-                gr.update(value=gallery_items),
-                selected_info_1, 
-                selected_info_2,
-                selected_info_3,
-                selected_indices,
-                lora_scale_1,
-                lora_scale_2,
-                lora_scale_3,
-                lora_image_1,
-                lora_image_2,
-                lora_image_3
-            )
-        except Exception as e:
-            print(e)
-            gr.Warning(str(e))
-            return current_loras, gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
-    else:
-        return current_loras, gr.update(), gr.update(), gr.update(), gr.update(), selected_indices, gr.update(), gr.update(), gr.update(), gr.update(), gr.update(), gr.update()
-
-def remove_custom_lora(selected_indices, current_loras):
-    if current_loras:
-        custom_lora_repo = current_loras[-1]['repo']
-        # Remove from loras list
-        current_loras = current_loras[:-1]
-        # Remove from selected_indices if selected
-        custom_lora_index = len(current_loras)
-        if custom_lora_index in selected_indices:
-            selected_indices.remove(custom_lora_index)
-    # Update gallery
-    gallery_items = [(item["image"], item["title"]) for item in current_loras]
-    # Update selected_info and images
-    selected_info_1 = "Select a LoRA 1"
-    selected_info_2 = "Select a LoRA 2"
-    selected_info_3 = "Select a LoRA 3"
-    lora_scale_1 = 1.15
-    lora_scale_2 = 1.15
-    lora_scale_3 = 1.15
-    lora_image_1 = None
-    lora_image_2 = None
-    lora_image_3 = None
-    if len(selected_indices) >= 1:
-        lora1 = current_loras[selected_indices[0]]
-        selected_info_1 = f"### LoRA 1 Selected: [{lora1['title']}]({lora1['repo']}) ✨"
-        lora_image_1 = lora1['image']
-    if len(selected_indices) >= 2:
-        lora2 = current_loras[selected_indices[1]]
-        selected_info_2 = f"### LoRA 2 Selected: [{lora2['title']}]({lora2['repo']}) ✨"
-        lora_image_2 = lora2['image']
-    if len(selected_indices) >= 3:
-        lora3 = current_loras[selected_indices[2]]
-        selected_info_3 = f"### LoRA 3 Selected: [{lora3['title']}]({lora3['repo']}) ✨"
-        lora_image_3 = lora3['image']
-    return (
-        current_loras,
-        gr.update(value=gallery_items),
-        selected_info_1,
-        selected_info_2,
-        selected_info_3,
-        selected_indices,
-        lora_scale_1,
-        lora_scale_2,
-        lora_scale_3,
-        lora_image_1,
-        lora_image_2,
-        lora_image_3
-    )
-
-
-def generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress):
-    print("Generating image...")
-    pipe.to("cuda")
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    with calculateDuration("Generating image"):
-        # Generate image
-        for img in pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-            prompt=prompt_mash,
-            num_inference_steps=steps,
-            guidance_scale=cfg_scale,
-            width=width,
-            height=height,
-            generator=generator,
-            joint_attention_kwargs={"scale": 1.0},
-            output_type="pil",
-            good_vae=good_vae,
-        ):
-            yield img
-
-
-def generate_image_to_image(prompt_mash, image_input_path, image_strength, steps, cfg_scale, width, height, seed):
-    pipe_i2i.to("cuda")
-    generator = torch.Generator(device="cuda").manual_seed(seed)
-    image_input = load_image(image_input_path)
-    final_image = pipe_i2i(
-        prompt=prompt_mash,
-        image=image_input,
-        strength=image_strength,
-        num_inference_steps=steps,
-        guidance_scale=cfg_scale,
-        width=width,
-        height=height,
-        generator=generator,
-        joint_attention_kwargs={"scale": 1.0},
-        output_type="pil",
-    ).images[0]
-    return final_image
-
-def run_lora(prompt, image_input, image_strength, cfg_scale, steps, selected_indices, 
-             lora_scale_1, lora_scale_2, lora_scale_3, randomize_seed, seed, 
-             width, height, loras_state, progress=gr.Progress(track_tqdm=True)):
-    try:
-        # 한글 감지 및 번역
-        if any('\u3131' <= char <= '\u318E' or '\uAC00' <= char <= '\uD7A3' for char in prompt):
-            translated = translator(prompt, max_length=512)[0]['translation_text']
-            print(f"Original prompt: {prompt}")
-            print(f"Translated prompt: {translated}")
-            prompt = translated
-
-        if not selected_indices:
-            raise gr.Error("You must select at least one LoRA before proceeding.")
-
-        selected_loras = [loras_state[idx] for idx in selected_indices]
-
-        # Build the prompt with trigger words
-        prepends = []
-        appends = []
-        for lora in selected_loras:
-            trigger_word = lora.get('trigger_word', '')
-            if trigger_word:
-                if lora.get("trigger_position") == "prepend":
-                    prepends.append(trigger_word)
-                else:
-                    appends.append(trigger_word)
-        prompt_mash = " ".join(prepends + [prompt] + appends)
-        print("Prompt Mash: ", prompt_mash)
-
-        # Unload previous LoRA weights
-        with calculateDuration("Unloading LoRA"):
-            pipe.unload_lora_weights()
-            pipe_i2i.unload_lora_weights()
-            
-        print(f"Active adapters before loading: {pipe.get_active_adapters()}")
-        
-        # Load LoRA weights with respective scales
-        lora_names = []
-        lora_weights = []
-
-        with calculateDuration("Loading LoRA weights"):
-            for idx, lora in enumerate(selected_loras):
-                try:
-                    lora_name = f"lora_{idx}"
-                    lora_path = lora['repo']
-            
-                    # Private 모델인 경우 특별 처리
-                    if lora.get('private', False):
-                        lora_path = load_private_model(lora_path, huggingface_token)
-                        print(f"Using private model path: {lora_path}")
-            
-                    if image_input is not None:
-                        pipe_i2i.load_lora_weights(
-                            lora_path,
-                            adapter_name=lora_name,
-                            token=huggingface_token
-                        )
-                    else:
-                        pipe.load_lora_weights(
-                            lora_path,
-                            adapter_name=lora_name,
-                            token=huggingface_token
-                        )
-            
-                    lora_names.append(lora_name)
-                    lora_weights.append(lora_scale_1 if idx == 0 else lora_scale_2 if idx == 1 else lora_scale_3)
-                    print(f"Successfully loaded LoRA {lora_name} from {lora_path}")
-            
-                except Exception as e:
-                    print(f"Failed to load LoRA {lora_name}: {str(e)}")
-                    continue
-
-
-
-            print("Loaded LoRAs:", lora_names)
-            print("Adapter weights:", lora_weights)
-            
-            if lora_names:
-                if image_input is not None:
-                    pipe_i2i.set_adapters(lora_names, adapter_weights=lora_weights)
-                else:
-                    pipe.set_adapters(lora_names, adapter_weights=lora_weights)
-            else:
-                print("No LoRAs were successfully loaded.")
-                return None, seed, gr.update(visible=False)
-
-        print(f"Active adapters after loading: {pipe.get_active_adapters()}")
-
-        # Randomize seed if needed
-        with calculateDuration("Randomizing seed"):
-            if randomize_seed:
-                seed = random.randint(0, MAX_SEED)
-
-        # Generate image
-        if image_input is not None:
-            final_image = generate_image_to_image(prompt_mash, image_input, image_strength, steps, cfg_scale, width, height, seed)
-        else:
-            image_generator = generate_image(prompt_mash, steps, seed, cfg_scale, width, height, progress)
-            final_image = None
-            step_counter = 0
-            for image in image_generator:
-                step_counter += 1
-                final_image = image
-                progress_bar = f'<div class="progress-container"><div class="progress-bar" style="--current: {step_counter}; --total: {steps};"></div></div>'
-                yield image, seed, gr.update(value=progress_bar, visible=True)
-
-        if final_image is None:
-            raise Exception("Failed to generate image")
-        
-        return final_image, seed, gr.update(visible=False)
-
-    except Exception as e:
-        print(f"Error in run_lora: {str(e)}")
-        return None, seed, gr.update(visible=False)
-
-run_lora.zerogpu = True
-
-def get_huggingface_safetensors(link):
-    split_link = link.split("/")
-    if len(split_link) == 2:
-        model_card = ModelCard.load(link)
-        base_model = model_card.data.get("base_model")
-        print(f"Base model: {base_model}")
-        if base_model not in ["black-forest-labs/FLUX.1-dev", "black-forest-labs/FLUX.1-schnell"]:
-            raise Exception("Not a FLUX LoRA!")
-        image_path = model_card.data.get("widget", [{}])[0].get("output", {}).get("url", None)
-        trigger_word = model_card.data.get("instance_prompt", "")
-        image_url = f"https://huggingface.co/{link}/resolve/main/{image_path}" if image_path else None
-        fs = HfFileSystem()
-        safetensors_name = None
-        try:
-            list_of_files = fs.ls(link, detail=False)
-            for file in list_of_files:
-                if file.endswith(".safetensors"):
-                    safetensors_name = file.split("/")[-1]
-                if not image_url and file.lower().endswith((".jpg", ".jpeg", ".png", ".webp")):
-                    image_elements = file.split("/")
-                    image_url = f"https://huggingface.co/{link}/resolve/main/{image_elements[-1]}"
-        except Exception as e:
-            print(e)
-            raise gr.Error("Invalid Hugging Face repository with a *.safetensors LoRA")
-        if not safetensors_name:
-            raise gr.Error("No *.safetensors file found in the repository")
-        return split_link[1], link, safetensors_name, trigger_word, image_url
-    else:
-        raise gr.Error("Invalid Hugging Face repository link")
-
-def check_custom_model(link):
-    if link.endswith(".safetensors"):
-        # Treat as direct link to the LoRA weights
-        title = os.path.basename(link)
-        repo = link
-        path = None  # No specific weight name
-        trigger_word = ""
-        image_url = None
-        return title, repo, path, trigger_word, image_url
-    elif link.startswith("https://"):
-        if "huggingface.co" in link:
-            link_split = link.split("huggingface.co/")
-            return get_huggingface_safetensors(link_split[1])
-        else:
-            raise Exception("Unsupported URL")
-    else:
-        # Assume it's a Hugging Face model path
-        return get_huggingface_safetensors(link)
-
-def update_history(new_image, history):
-    """Updates the history gallery with the new image."""
-    if history is None:
-        history = []
-    if new_image is not None:
-        history.insert(0, new_image)
-    return history
-
-
-
-def refresh_models(huggingface_token):
-    try:
-        headers = {
-            "Authorization": f"Bearer {huggingface_token}",
-            "Accept": "application/json"
-        }
-        
-        username = USERNAME
-        api_url = f"https://huggingface.co/api/models?author={username}"
-        response = requests.get(api_url, headers=headers)
-        if response.status_code != 200:
-            raise Exception(f"Failed to fetch models from HuggingFace. Status code: {response.status_code}")
-            
-        all_models = response.json()
-        print(f"Found {len(all_models)} models for user {username}")
-        
-        user_models = [
-            model for model in all_models 
-            if model.get('tags') and ('flux' in [tag.lower() for tag in model.get('tags', [])] or
-                                    'flux-lora' in [tag.lower() for tag in model.get('tags', [])])
-        ]
-        
-        print(f"Found {len(user_models)} FLUX models")
-        
-        new_models = []
-        for model in user_models:
-            try:
-                model_id = model['id']
-                model_card_url = f"https://huggingface.co/api/models/{model_id}"
-                model_info_response = requests.get(model_card_url, headers=headers)
-                model_info = model_info_response.json()
-                
-                # 이미지 URL에 토큰을 포함시키는 방식으로 변경
-                is_private = model.get('private', False)
-                base_image_name = "1732195028106__000001000_0.jpg"  # 기본 이미지 이름
-                
-                try:
-                    # 실제 이미지 파일 확인
-                    fs = HfFileSystem(token=huggingface_token)
-                    samples_path = f"{model_id}/samples"
-                    files = fs.ls(samples_path, detail=True)
-                    jpg_files = [
-                        f['name'] for f in files 
-                        if isinstance(f, dict) and 
-                        'name' in f and 
-                        f['name'].lower().endswith('.jpg') and 
-                        any(char.isdigit() for char in os.path.basename(f['name']))
-                    ]
-                    
-                    if jpg_files:
-                        base_image_name = os.path.basename(jpg_files[0])
-                except Exception as e:
-                    print(f"Error accessing samples folder for {model_id}: {str(e)}")
-                
-                # 이미지 URL 구성 (토큰 포함)
-                if is_private:
-                    # Private 모델의 경우 로컬 캐시 경로 사용
-                    cache_dir = f"models/{model_id.replace('/', '_')}/samples"
-                    os.makedirs(cache_dir, exist_ok=True)
-                    
-                    # 이미지 다운로드
-                    image_url = f"https://huggingface.co/{model_id}/resolve/main/samples/{base_image_name}"
-                    local_image_path = os.path.join(cache_dir, base_image_name)
-                    
-                    if not os.path.exists(local_image_path):
-                        response = requests.get(image_url, headers=headers)
-                        if response.status_code == 200:
-                            with open(local_image_path, 'wb') as f:
-                                f.write(response.content)
-                    
-                    image_url = local_image_path
-                else:
-                    image_url = f"https://huggingface.co/{model_id}/resolve/main/samples/{base_image_name}"
-                
-                model_info = {
-                    "image": image_url,
-                    "title": f"[Private] {model_id.split('/')[-1]}" if is_private else model_id.split('/')[-1],
-                    "repo": model_id,
-                    "weights": "pytorch_lora_weights.safetensors",
-                    "trigger_word": model_info.get('instance_prompt', ''),
-                    "private": is_private
-                }
-                new_models.append(model_info)
-                print(f"Added model: {model_id} with image: {image_url}")
-                
-            except Exception as e:
-                print(f"Error processing model {model['id']}: {str(e)}")
-                continue
-        
-        updated_loras = new_models + [lora for lora in loras if lora['repo'] not in [m['repo'] for m in new_models]]
-        
-        print(f"Total models after refresh: {len(updated_loras)}")
-        return updated_loras
-    except Exception as e:
-        print(f"Error refreshing models: {str(e)}")
-        return loras
-
-def load_private_model(model_id, huggingface_token):
-    """Private 모델을 로드하는 함수"""
-    try:
-        headers = {"Authorization": f"Bearer {huggingface_token}"}
-        
-        # 모델 다운로드
-        local_dir = snapshot_download(
-            repo_id=model_id,
-            token=huggingface_token,
-            local_dir=f"models/{model_id.replace('/', '_')}",
-            local_dir_use_symlinks=False
-        )
-        
-        # safetensors 파일 찾기
-        safetensors_file = None
-        for root, dirs, files in os.walk(local_dir):
-            for file in files:
-                if file.endswith('.safetensors'):
-                    safetensors_file = os.path.join(root, file)
-                    break
-            if safetensors_file:
-                break
-        
-        if not safetensors_file:
-            raise Exception(f"No .safetensors file found in {local_dir}")
-            
-        print(f"Found safetensors file: {safetensors_file}")
-        return safetensors_file  # 전체 경로를 반환
-        
-    except Exception as e:
-        print(f"Error loading private model {model_id}: {str(e)}")
-        raise e
-
-custom_theme = gr.themes.Base(
-    primary_hue="blue",
-    secondary_hue="purple",
-    neutral_hue="slate",
-).set(
-    button_primary_background_fill="*primary_500",
-    button_primary_background_fill_dark="*primary_600",
-    button_primary_background_fill_hover="*primary_400",
-    button_primary_border_color="*primary_500",
-    button_primary_border_color_dark="*primary_600",
-    button_primary_text_color="white",
-    button_primary_text_color_dark="white",
-    button_secondary_background_fill="*neutral_100",
-    button_secondary_background_fill_dark="*neutral_700",
-    button_secondary_background_fill_hover="*neutral_50",
-    button_secondary_text_color="*neutral_800",
-    button_secondary_text_color_dark="white",
-    background_fill_primary="*neutral_50",
-    background_fill_primary_dark="*neutral_900",
-    block_background_fill="white",
-    block_background_fill_dark="*neutral_800",
-    block_label_background_fill="*primary_500",
-    block_label_background_fill_dark="*primary_600",
-    block_label_text_color="white",
-    block_label_text_color_dark="white",
-    block_title_text_color="*neutral_800",
-    block_title_text_color_dark="white",
-    input_background_fill="white",
-    input_background_fill_dark="*neutral_800",
-    input_border_color="*neutral_200",
-    input_border_color_dark="*neutral_700",
-    input_placeholder_color="*neutral_400",
-    input_placeholder_color_dark="*neutral_400",
-    shadow_spread="8px",
-    shadow_inset="0px 2px 4px 0px rgba(0,0,0,0.05)"
-)
-
-css = '''
-/* 기본 버튼 및 컴포넌트 스타일 */
-#gen_btn {
-    height: 100%
-}
-
-#title {
-    text-align: center
-}
-
-#title h1 {
-    font-size: 3em; 
-    display: inline-flex; 
-    align-items: center
-}
-
-#title img {
-    width: 100px; 
-    margin-right: 0.25em
-}
-
-#lora_list {
-    background: var(--block-background-fill);
-    padding: 0 1em .3em; 
-    font-size: 90%
-}
-
-/* 커스텀 LoRA 카드 스타일 */
-.custom_lora_card {
-    margin-bottom: 1em
-}
-
-.card_internal {
-    display: flex;
-    height: 100px;
-    margin-top: .5em
-}
-
-.card_internal img {
-    margin-right: 1em
-}
-
-/* 유틸리티 클래스 */
-.styler {
-    --form-gap-width: 0px !important
-}
-
-/* 프로그레스 바 스타일 */
-#progress {
-    height: 30px;
-    width: 90% !important;
-    margin: 0 auto !important;
-}
-
-#progress .generating {
-    display: none
-}
-
-.progress-container {
-    width: 100%;
-    height: 30px;
-    background-color: #f0f0f0;
-    border-radius: 15px;
-    overflow: hidden;
-    margin-bottom: 20px
-}
-
-.progress-bar {
-    height: 100%;
-    background-color: #4f46e5;
-    width: calc(var(--current) / var(--total) * 100%);
-    transition: width 0.5s ease-in-out
-}
-
-/* 컴포넌트 특정 스타일 */
-#component-8, .button_total {
-    height: 100%; 
-    align-self: stretch;
-}
-
-#loaded_loras [data-testid="block-info"] {
-    font-size: 80%
-}
-
-#custom_lora_structure {
-    background: var(--block-background-fill)
-}
-
-#custom_lora_btn {
-    margin-top: auto;
-    margin-bottom: 11px
-}
-
-#random_btn {
-    font-size: 300%
-}
-
-#component-11 {
-    align-self: stretch;
-}
-
-/* 갤러리 메인 스타일 */
-#lora_gallery {
-    margin: 20px 0;
-    padding: 10px;
-    border: 1px solid #ddd;
-    border-radius: 12px;
-    background: linear-gradient(to bottom right, #ffffff, #f8f9fa);
-    width: 100% !important;
-    height: 800px !important;
-    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
-    display: block !important;
-}
-
-/* 갤러리 그리드 스타일 */
-#gallery {
-    display: grid !important;
-    grid-template-columns: repeat(10, 1fr) !important;
-    gap: 10px !important;
-    padding: 10px !important;
-    width: 100% !important;
-    height: 100% !important;
-    overflow-y: auto !important;
-    max-width: 100% !important;
-}
-
-/* 갤러리 아이템 스타일 */
-.gallery-item {
-    position: relative !important;
-    width: 100% !important;
-    aspect-ratio: 1 !important;
-    margin: 0 !important;
-    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
-    transition: transform 0.3s ease, box-shadow 0.3s ease;
-    border-radius: 12px;
-    overflow: hidden;
-}
-
-.gallery-item img {
-    width: 100% !important;
-    height: 100% !important;
-    object-fit: cover !important;
-    border-radius: 12px !important;
-}
-
-/* 갤러리 그리드 래퍼 */
-.wrap, .svelte-w6dy5e {
-    display: grid !important;
-    grid-template-columns: repeat(10, 1fr) !important;
-    gap: 10px !important;
-    width: 100% !important;
-    max-width: 100% !important;
-}
-
-/* 컨테이너 공통 스타일 */
-.container, .content, .block, .contain {
-    width: 100% !important;
-    max-width: 100% !important;
-    margin: 0 !important;
-    padding: 0 !important;
-}
-
-.row {
-    width: 100% !important;
-    margin: 0 !important;
-    padding: 0 !important;
-}
-
-/* 버튼 스타일 */
-.button_total {
-    box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
-    transition: all 0.3s ease;
-}
-
-.button_total:hover {
-    transform: translateY(-2px);
-    box-shadow: 0 10px 15px -3px rgba(0, 0, 0, 0.1), 0 4px 6px -2px rgba(0, 0, 0, 0.05);
-}
-
-/* 입력 필드 스타일 */
-input, textarea {
-    box-shadow: inset 0 2px 4px 0 rgba(0, 0, 0, 0.06);
-    transition: all 0.3s ease;
-}
-
-input:focus, textarea:focus {
-    box-shadow: 0 0 0 3px rgba(66, 153, 225, 0.5);
-}
-
-/* 컴포넌트 border-radius */
-.gradio-container .input, 
-.gradio-container .button,
-.gradio-container .block {
-    border-radius: 12px;
-}
-
-/* 스크롤바 스타일 */
-#gallery::-webkit-scrollbar {
-    width: 8px;
-}
-
-#gallery::-webkit-scrollbar-track {
-    background: #f1f1f1;
-    border-radius: 4px;
-}
-
-#gallery::-webkit-scrollbar-thumb {
-    background: #888;
-    border-radius: 4px;
-}
-
-#gallery::-webkit-scrollbar-thumb:hover {
-    background: #555;
-}
-
-/* Flex 컨테이너 */
-.flex {
-    width: 100% !important;
-    max-width: 100% !important;
-    display: flex !important;
-}
-
-/* Svelte 특정 클래스 */
-.svelte-1p9xokt {
-    width: 100% !important;
-    max-width: 100% !important;
-}
-
-/* Footer 숨김 */
-#footer {
-    visibility: hidden;
-}
-
-/* 결과 이미지 및 컨테이너 스타일 */
-#result_column, #result_column > div {
-    display: flex !important;
-    flex-direction: column !important;
-    align-items: flex-start !important;  /* center에서 flex-start로 변경 */
-    width: 100% !important;
-    margin: 0 !important;  /* auto에서 0으로 변경 */
-}
-
-.generated-image, .generated-image > div {
-    display: flex !important;
-    justify-content: flex-start !important;  /* center에서 flex-start로 변경 */
-    align-items: flex-start !important;  /* center에서 flex-start로 변경 */
-    width: 90% !important;
-    max-width: 768px !important;
-    margin: 0 !important;  /* auto에서 0으로 변경 */
-    margin-left: 20px !important;  /* 왼쪽 여백 추가 */
-}
-
-.generated-image img {
-    margin: 0 !important;  /* auto에서 0으로 변경 */
-    display: block !important;
-    max-width: 100% !important;
-}
-
-/* 히스토리 갤러리�� 좌측 정렬로 변경 */
-.history-gallery {
-    display: flex !important;
-    justify-content: flex-start !important;  /* center에서 flex-start로 변경 */
-    width: 90% !important;
-    max-width: 90% !important;
-    margin: 0 !important;  /* auto에서 0으로 변경 */
-    margin-left: 20px !important;  /* 왼쪽 여백 추가 */
-
-/* 새로고침 버튼 스타일 */
-#refresh-button {
-    margin: 10px;
-    padding: 8px 16px;
-    background-color: #4a5568;
-    color: white;
-    border-radius: 8px;
-    transition: all 0.3s ease;
-}
-
-#refresh-button:hover {
-    background-color: #2d3748;
-    transform: scale(1.05);
-}
-
-#refresh-button:active {
-    transform: scale(0.95);
-}    
-}
-'''
-
-with gr.Blocks(theme=custom_theme, css=css, delete_cache=(60, 3600)) as app:
-    loras_state = gr.State(loras)
-    selected_indices = gr.State([])
-
-    gr.Markdown(
-        """
-        # MixGen3: 멀티 Lora(이미지 학습) 통합 생성 모델
-        ### 사용 안내:
-        갤러리에서 원하는 모델을 선택(최대 3개까지) < 프롬프트에 한글 또는 영문으로 원하는 내용을 입력 < Generate 버튼 실행  
-        """
-    )
-    # 새로고침 버튼 추가
-    with gr.Row():
-        refresh_button = gr.Button("🔄 모델 새로고침(나만의 맞춤 학습된 Private 모델 불러오기)", variant="secondary")
-
-    with gr.Row(elem_id="lora_gallery", equal_height=True):
-        gallery = gr.Gallery(
-            value=[(item["image"], item["title"]) for item in loras],
-            label="LoRA Explorer Gallery",
-            columns=11,
-            elem_id="gallery",
-            height=800,
-            object_fit="cover",
-            show_label=True,
-            allow_preview=False,
-            show_share_button=False,
-            container=True,
-            preview=False
-        )
-
-
-    with gr.Tab(label="Generate"):
-        # Prompt and Generate Button
-        with gr.Row():
-            with gr.Column(scale=3):
-                prompt = gr.Textbox(label="Prompt", lines=1, placeholder="Type a prompt after selecting a LoRA")
-            with gr.Column(scale=1):
-                generate_button = gr.Button("Generate", variant="primary", elem_classes=["button_total"])
-
-        # LoRA Selection Area
-        with gr.Row(elem_id="loaded_loras"):
-            # Randomize Button
-            with gr.Column(scale=1, min_width=25):
-                randomize_button = gr.Button("🎲", variant="secondary", scale=1, elem_id="random_btn")
-            
-            # LoRA 1
-            with gr.Column(scale=8):
-                with gr.Row():
-                    with gr.Column(scale=0, min_width=50):
-                        lora_image_1 = gr.Image(label="LoRA 1 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
-                    with gr.Column(scale=3, min_width=100):
-                        selected_info_1 = gr.Markdown("Select a LoRA 1")
-                    with gr.Column(scale=5, min_width=50):
-                        lora_scale_1 = gr.Slider(label="LoRA 1 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
-                with gr.Row():
-                    remove_button_1 = gr.Button("Remove", size="sm")
-
-            # LoRA 2
-            with gr.Column(scale=8):
-                with gr.Row():
-                    with gr.Column(scale=0, min_width=50):
-                        lora_image_2 = gr.Image(label="LoRA 2 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
-                    with gr.Column(scale=3, min_width=100):
-                        selected_info_2 = gr.Markdown("Select a LoRA 2")
-                    with gr.Column(scale=5, min_width=50):
-                        lora_scale_2 = gr.Slider(label="LoRA 2 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
-                with gr.Row():
-                    remove_button_2 = gr.Button("Remove", size="sm")
-
-            # LoRA 3
-            with gr.Column(scale=8):
-                with gr.Row():
-                    with gr.Column(scale=0, min_width=50):
-                        lora_image_3 = gr.Image(label="LoRA 3 Image", interactive=False, min_width=50, width=50, show_label=False, show_share_button=False, show_download_button=False, show_fullscreen_button=False, height=50)
-                    with gr.Column(scale=3, min_width=100):
-                        selected_info_3 = gr.Markdown("Select a LoRA 3")
-                    with gr.Column(scale=5, min_width=50):
-                        lora_scale_3 = gr.Slider(label="LoRA 3 Scale", minimum=0, maximum=3, step=0.01, value=1.15)
-                with gr.Row():
-                    remove_button_3 = gr.Button("Remove", size="sm")
-
-        # Result and Progress Area
-        with gr.Column(elem_id="result_column"):
-            progress_bar = gr.Markdown(elem_id="progress", visible=False)
-            with gr.Column(elem_id="result_box"):  # Box를 Column으로 변경
-                result = gr.Image(
-                    label="Generated Image", 
-                    interactive=False,
-                    elem_classes=["generated-image"],
-                    container=True,
-                    elem_id="result_image",
-                    width="100%"
-                )
-            with gr.Accordion("History", open=False):
-                history_gallery = gr.Gallery(
-                    label="History", 
-                    columns=6, 
-                    object_fit="contain", 
-                    interactive=False,
-                    elem_classes=["history-gallery"]
-                )
-
-
-        # Advanced Settings
-        with gr.Row():
-            with gr.Accordion("Advanced Settings", open=False):
-                with gr.Row():
-                    input_image = gr.Image(label="Input image", type="filepath")
-                    image_strength = gr.Slider(label="Denoise Strength", info="Lower means more image influence", minimum=0.1, maximum=1.0, step=0.01, value=0.75)
-                with gr.Column():
-                    with gr.Row():
-                        cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=3.5)
-                        steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=28)
-                    with gr.Row():
-                        width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=1024)
-                        height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1024)
-                    with gr.Row():
-                        randomize_seed = gr.Checkbox(True, label="Randomize seed")
-                        seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0, randomize=True)
-
-        # Custom LoRA Section
-        with gr.Column():
-            with gr.Group():
-                with gr.Row(elem_id="custom_lora_structure"):
-                    custom_lora = gr.Textbox(label="Custom LoRA", info="LoRA Hugging Face path or *.safetensors public URL", placeholder="ginipick/flux-lora-eric-cat", scale=3, min_width=150)
-                    add_custom_lora_button = gr.Button("Add Custom LoRA", elem_id="custom_lora_btn", scale=2, min_width=150)
-                remove_custom_lora_button = gr.Button("Remove Custom LoRA", visible=False)
-                gr.Markdown("[Check the list of FLUX LoRAs](https://huggingface.co/models?other=base_model:adapter:black-forest-labs/FLUX.1-dev)", elem_id="lora_list")
-
-        # Event Handlers
-        gallery.select(
-            update_selection,
-            inputs=[selected_indices, loras_state, width, height],
-            outputs=[prompt, selected_info_1, selected_info_2, selected_info_3, selected_indices, 
-                lora_scale_1, lora_scale_2, lora_scale_3, width, height, 
-                lora_image_1, lora_image_2, lora_image_3]
-        )
-
-        remove_button_1.click(
-            remove_lora_1,
-            inputs=[selected_indices, loras_state],
-            outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, 
-                lora_scale_1, lora_scale_2, lora_scale_3, 
-                lora_image_1, lora_image_2, lora_image_3]
-        )
-
-        remove_button_2.click(
-            remove_lora_2,
-            inputs=[selected_indices, loras_state],
-            outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, 
-                lora_scale_1, lora_scale_2, lora_scale_3, 
-                lora_image_1, lora_image_2, lora_image_3]
-        )
-
-        remove_button_3.click(
-            remove_lora_3,
-            inputs=[selected_indices, loras_state],
-            outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, 
-                lora_scale_1, lora_scale_2, lora_scale_3, 
-                lora_image_1, lora_image_2, lora_image_3]
-        )
-
-        randomize_button.click(
-            randomize_loras,
-            inputs=[selected_indices, loras_state],
-            outputs=[selected_info_1, selected_info_2, selected_info_3, selected_indices, 
-                lora_scale_1, lora_scale_2, lora_scale_3, 
-                lora_image_1, lora_image_2, lora_image_3, prompt]
-        )
-
-        add_custom_lora_button.click(
-            add_custom_lora,
-            inputs=[custom_lora, selected_indices, loras_state],
-            outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_info_3, 
-                selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, 
-                lora_image_1, lora_image_2, lora_image_3]
-        )
-
-        remove_custom_lora_button.click(
-            remove_custom_lora,
-            inputs=[selected_indices, loras_state],
-            outputs=[loras_state, gallery, selected_info_1, selected_info_2, selected_info_3, 
-                selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, 
-                lora_image_1, lora_image_2, lora_image_3]
-        )
-
-        gr.on(
-            triggers=[generate_button.click, prompt.submit],
-            fn=run_lora,
-            inputs=[prompt, input_image, image_strength, cfg_scale, steps, 
-                selected_indices, lora_scale_1, lora_scale_2, lora_scale_3, 
-                randomize_seed, seed, width, height, loras_state],
-            outputs=[result, seed, progress_bar]
-        ).then(
-            fn=lambda x, history: update_history(x, history) if x is not None else history,
-            inputs=[result, history_gallery],
-            outputs=history_gallery
-        )
-
-# 새로고침 버튼 이벤트 핸들러
-    def refresh_gallery():
-        updated_loras = refresh_models(huggingface_token)
-        return (
-            gr.update(value=[(item["image"], item["title"]) for item in updated_loras]),
-            updated_loras
-        )
-
-    refresh_button.click(
-        refresh_gallery,
-        outputs=[gallery, loras_state]
-    )
-
-if __name__ == "__main__":
-    app.queue(max_size=20)
-    app.launch(debug=True)