Upload app.py

app.py

@@ -51,523 +51,6 @@ IS_COLAB = utils.is_google_colab() or os.getenv("IS_COLAB") == "1"
 HF_TOKEN = os.getenv("HF_TOKEN")
 CACHE_EXAMPLES = torch.cuda.is_available() and os.getenv("CACHE_EXAMPLES") == "1"
 
-# PyTorch settings for better performance and determinism
-#torch.backends.cudnn.deterministic = True
-#torch.backends.cudnn.benchmark = False
-#torch.backends.cuda.matmul.allow_tf32 = True
-#device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
-#logger.info(f"Using device: {device}")
-
-# グローバル変数としてパイプラインを定義
-pipe = None
-vae = None
-
-# スタイルリストから名前のみを抽出
-style_names = [style["name"] for style in style_list]
-
-@spaces.GPU(timeout_seconds=120)
-def initialize_llm():
-    """アプリケーション起動時にLLMだけを初期化する関数"""
-
-    if TEXT_TO_PROMPT_ENABLED:
-        torch.backends.cudnn.deterministic = True
-        torch.backends.cudnn.benchmark = False
-        torch.backends.cuda.matmul.allow_tf32 = True
-
-        logger.info("Loading LLM for prompt generation first...")
-        prompt_generator.load_model()
-        return "LLM loaded successfully"
-    return "LLM loading skipped (disabled in config)"
-
-
-def cleanup_old_images(output_dir, max_age_hours=1):
-    """
-    指定されたディレクトリ内の古い画像ファイル（PNG）を削除します
-
-    Args:
-        output_dir: 画像ファイルが保存されているディレクトリのパス
-        max_age_hours: この時間（時間単位）より古いファイルを削除する
-    """
-    import os
-    import time
-    from datetime import datetime
-
-    logger.info(f"Cleaning up images older than {max_age_hours} hours in {output_dir}")
-    current_time = time.time()
-    max_age_seconds = max_age_hours * 60 * 60
-    deleted_count = 0
-
-    # ディレクトリが存在しない場合は作成
-    if not os.path.exists(output_dir):
-        os.makedirs(output_dir, exist_ok=True)
-        return 0
-
-    # ディレクトリ内のすべてのファイルをチェック
-    for filename in os.listdir(output_dir):
-        if filename.lower().endswith('.png'):
-            file_path = os.path.join(output_dir, filename)
-            file_age = current_time - os.path.getmtime(file_path)
-
-            # 指定された時間より古いファイルを削除
-            if file_age > max_age_seconds:
-                try:
-                    os.remove(file_path)
-                    deleted_count += 1
-                except Exception as e:
-                    logger.error(f"Failed to delete {file_path}: {str(e)}")
-
-    if deleted_count > 0:
-        logger.info(f"Deleted {deleted_count} old image files from {output_dir}")
-    return deleted_count
-
-
-# シリーズとキャラクターの紐付けを処理する
-def get_character_series_mapping():
-    try:
-        mapping = config.config.get('text_to_prompt', {}).get('character_series_mapping', {})
-        return mapping
-    except Exception as e:
-        logger.error(f"Failed to get character-series mapping: {str(e)}")
-        return {}
-
-# シリーズ名と表示名を分割
-def parse_series_list():
-    series_dict = {}
-    display_series_list = []
-    
-    for item in series_list:
-        if '|' in item:
-            code, display = item.split('|', 1)
-            series_dict[code] = display
-            display_series_list.append(f"{code} / {display}")
-        else:
-            series_dict[item] = item
-            display_series_list.append(item)
-            
-    return series_dict, display_series_list
-
-# キャラクター名と表示名を分割
-def parse_character_list():
-    character_dict = {}
-    display_character_list = []
-    
-    for item in character_list:
-        if '|' in item:
-            code, display = item.split('|', 1)
-            character_dict[code] = display
-            display_character_list.append(f"{code} / {display}")
-        else:
-            character_dict[item] = item
-            display_character_list.append(item)
-            
-    return character_dict, display_character_list
-
-# カテゴリー名と表示名を分割
-def parse_category_list():
-    category_dict = {}
-    display_category_list = []
-    
-    for item in category_list:
-        # カテゴリの表示が英語のみなので、そのまま表示
-        category_dict[item] = item
-        display_category_list.append(item)
-            
-    return category_dict, display_category_list
-
-# 逆引き辞書の作成
-def create_reverse_dict(original_dict):
-    return {v: k for k, v in original_dict.items()}
-
-# 表示名から内部コードを取得する関数
-def get_code_from_display(display_name, reverse_dict):
-    # 表示名が "code / display" 形式の場合
-    if " / " in display_name:
-        code = display_name.split(" / ")[0]
-        return code
-    # 元のコードの場合はそのまま返す
-    return reverse_dict.get(display_name, display_name)
-
-# 辞書とマッピングの作成
-series_dict, display_series_list = parse_series_list()
-character_dict, display_character_list = parse_character_list()
-category_dict, display_category_list = parse_category_list()
-reverse_series_dict = create_reverse_dict(series_dict)
-reverse_character_dict = create_reverse_dict(character_dict)
-character_series_mapping = get_character_series_mapping()
-
-# 特定のシリーズに属するキャラクターのリストを取得
-def get_characters_for_series(series_display_name):
-    try:
-        # 表示名からシリーズコードを取得
-        series_code = get_code_from_display(series_display_name, reverse_series_dict)
-        
-        if not series_code:
-            logger.warning(f"Unknown series: {series_display_name}")
-            return display_character_list
-            
-        character_codes = character_series_mapping.get(series_code, [])
-        if not character_codes:
-            logger.warning(f"No characters found for series: {series_code}")
-            return display_character_list
-            
-        # コードから表示名へ変換
-        characters = [f"{code} / {character_dict.get(code, code)}" for code in character_codes]
-        return characters
-    except Exception as e:
-        logger.error(f"Error getting characters for series: {str(e)}")
-        return display_character_list
-
-class GenerationError(Exception):
-    """Custom exception for generation errors"""
-    pass
-
-def validate_prompt(prompt: str) -> str:
-    """Validate and clean up the input prompt."""
-    if not isinstance(prompt, str):
-        raise GenerationError("Prompt must be a string")
-    try:
-        # Ensure proper UTF-8 encoding/decoding
-        prompt = prompt.encode('utf-8').decode('utf-8')
-        # Add space between ! and ,
-        prompt = prompt.replace("!,", "! ,")
-    except UnicodeError:
-        raise GenerationError("Invalid characters in prompt")
-
-    # Only check if the prompt is completely empty or only whitespace
-    if not prompt or prompt.isspace():
-        raise GenerationError("Prompt cannot be empty")
-    return prompt.strip()
-
-def validate_dimensions(width: int, height: int) -> None:
-    """Validate image dimensions."""
-    if not MIN_IMAGE_SIZE <= width <= MAX_IMAGE_SIZE:
-        raise GenerationError(f"Width must be between {MIN_IMAGE_SIZE} and {MAX_IMAGE_SIZE}")
-
-    if not MIN_IMAGE_SIZE <= height <= MAX_IMAGE_SIZE:
-        raise GenerationError(f"Height must be between {MIN_IMAGE_SIZE} and {MAX_IMAGE_SIZE}")
-
-def convert_text_to_prompt(
-    novel_text: str,
-    series_display_name: str = series_dict.get(DEFAULT_SERIES, DEFAULT_SERIES),
-    character_display_name: str = character_dict.get(DEFAULT_CHARACTER, DEFAULT_CHARACTER),
-    category: str = DEFAULT_CATEGORY,
-) -> Tuple[str, str]:
-    """テキストからプロンプトを生成する関数"""
-    if not TEXT_TO_PROMPT_ENABLED:
-        return "Text to Prompt機能は無効になっています", novel_text
-    
-    # 表示名からコードに変換
-    series_name = get_code_from_display(series_display_name, reverse_series_dict)
-    character_name = get_code_from_display(character_display_name, reverse_character_dict)
-
-    try:
-        logger.info(f"prompt_generator.generate_prompt")
-        thinking, prompt = prompt_generator.generate_prompt(
-            novel_text, series_name, character_name, category
-        )
-        return thinking, prompt
-    except Exception as e:
-        logger.error(f"Error in convert_text_to_prompt: {str(e)}")
-        return f"エラーが発生しました: {str(e)}", novel_text
-
-@spaces.GPU
-def load_image_model(timeout_seconds=120):
-    """画像生成モデルをロードする関数"""
-    global pipe, vae
-    
-    # メモリ管理
-    if utils.is_space_environment():
-        torch.cuda.empty_cache()
-        gc.collect()
-    
-    # LLMがロードされていれば解放
-    if is_space_environment != True and TEXT_TO_PROMPT_ENABLED:
-        prompt_generator.unload_model()
-
-    logger.info("Loading image generation model...")
-    styles = {style["name"]: (style["prompt"], style.get("negative_prompt", "")) for style in style_list}
-    
-    # VAEを明示的にロード - subfolder パラメータを使用
-    vae = AutoencoderKL.from_pretrained(
-        MODEL,               # モデル名（例: "cagliostrolab/animagine-xl-4.0"）
-        subfolder="vae",     # サブフォルダ名
-        torch_dtype=torch.float16
-    )
-    
-    # パイプラインにVAEを渡す
-    pipe = utils.load_pipeline(MODEL, device, HF_TOKEN, vae=vae)
-    
-    if USE_TORCH_COMPILE:
-        pipe.unet = torch.compile(pipe.unet, mode="reduce-overhead", fullgraph=True)
-        logger.info("Model compiled with torch.compile")
-    
-    return "Image generation model loaded successfully"
-
-@spaces.GPU(timeout_seconds=300)
-def generate(
-    prompt: str,
-    negative_prompt: str = DEFAULT_NEGATIVE_PROMPT,
-    seed: int = -1,
-    custom_width: int = 1024,
-    custom_height: int = 1024,
-    guidance_scale: float = 5.0,
-    num_inference_steps: int = 28,
-    sampler: str = "Euler a",
-    aspect_ratio_selector: str = DEFAULT_ASPECT_RATIO,
-    style_selector: str = "(None)",
-    use_upscaler: bool = False,
-    upscaler_strength: float = 0.55,
-    upscale_by: float = 1.5,
-    add_quality_tags: bool = True,
-    progress: gr.Progress = gr.Progress(track_tqdm=True),
-) -> Tuple[List[str], Dict]:
-    """Generate images based on the given parameters."""
-    global pipe
-    
-    if pipe is None:
-        load_image_model()
-        logger.info(f"Loading image model status: {status}")
-    
-    start_time = time.time()
-    upscaler_pipe = None
-    backup_scheduler = None
-    styles = {style["name"]: (style["prompt"], style.get("negative_prompt", "")) for style in style_list}
-
-    try:
-        # Memory management
-        if is_space_environment != True :
-            cleanup_old_images(OUTPUT_DIR)
-            torch.cuda.empty_cache()
-            gc.collect()
-
-        # Input validation
-        prompt = validate_prompt(prompt)
-        if negative_prompt:
-            negative_prompt = negative_prompt.encode('utf-8').decode('utf-8')
-
-        validate_dimensions(custom_width, custom_height)
-
-        # Set up generation
-        if seed == 0:  # 0が入力された場合、ランダムなシード値を生成
-            seed = random.randint(0, utils.MAX_SEED)
-        generator = utils.seed_everything(seed)
-
-
-        width, height = utils.aspect_ratio_handler(
-            aspect_ratio_selector,
-            custom_width,
-            custom_height,
-        )
-
-        # Process prompts
-        if add_quality_tags:
-            prompt = "{prompt}, masterpiece, high score, great score, absurdres".format(prompt=prompt)
-
-        prompt, negative_prompt = utils.preprocess_prompt(
-            styles, style_selector, prompt, negative_prompt
-        )
-
-        width, height = utils.preprocess_image_dimensions(width, height)
-
-        # Set up pipeline
-        backup_scheduler = pipe.scheduler
-        pipe.scheduler = utils.get_scheduler(pipe.scheduler.config, sampler)
-
-        if use_upscaler:
-            upscaler_pipe = StableDiffusionXLImg2ImgPipeline(**pipe.components)
-
-        # Prepare metadata
-        metadata = {
-            "prompt": prompt,
-            "negative_prompt": negative_prompt,
-            "resolution": f"{width} x {height}",
-            "guidance_scale": guidance_scale,
-            "num_inference_steps": num_inference_steps,
-            "style_preset": style_selector,
-            "seed": seed,
-            "sampler": sampler,
-            "Model": "Animagine XL 4.0 Opt",
-            "Model hash": "6327eca98b",
-        }
-
-        if use_upscaler:
-            new_width = int(width * upscale_by)
-            new_height = int(height * upscale_by)
-            metadata["use_upscaler"] = {
-                "upscale_method": "nearest-exact",
-                "upscaler_strength": upscaler_strength,
-                "upscale_by": upscale_by,
-                "new_resolution": f"{new_width} x {new_height}",
-            }
-        else:
-            metadata["use_upscaler"] = None
-
-        logger.info(f"Starting generation with parameters: {json.dumps(metadata, indent=4)}")
-
-        # Generate images
-        if use_upscaler:
-            latents = pipe(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                width=width,
-                height=height,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                generator=generator,
-                output_type="latent",
-            ).images
-            upscaled_latents = utils.upscale(latents, "nearest-exact", upscale_by)
-            images = upscaler_pipe(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                image=upscaled_latents,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                strength=upscaler_strength,
-                generator=generator,
-                output_type="pil",
-            ).images
-        else:
-            images = pipe(
-                prompt=prompt,
-                negative_prompt=negative_prompt,
-                width=width,
-                height=height,
-                guidance_scale=guidance_scale,
-                num_inference_steps=num_inference_steps,
-                generator=generator,
-                output_type="pil",
-            ).images
-
-        # Save images
-        if images:
-            total = len(images)
-            image_paths = []
-            for idx, image in enumerate(images, 1):
-                progress(idx/total, desc="Saving images...")
-                path = utils.save_image(image, metadata, OUTPUT_DIR, IS_COLAB)
-                image_paths.append(path)
-                logger.info(f"Image {idx}/{total} saved as {path}")
-
-        generation_time = time.time() - start_time
-        logger.info(f"Generation completed successfully in {generation_time:.2f} seconds")
-        metadata["generation_time"] = f"{generation_time:.2f}s"
-
-        return image_paths, metadata
-
-    except GenerationError as e:
-        logger.warning(f"Generation validation error: {str(e)}")
-        raise gr.Error(str(e))
-    except Exception as e:
-        logger.exception("Unexpected error during generation")
-        raise gr.Error(f"Generation failed: {str(e)}")
-    finally:
-        # Cleanup
-        torch.cuda.empty_cache()
-        gc.collect()
-
-        if upscaler_pipe is not None:
-            del upscaler_pipe
-
-        if backup_scheduler is not None and pipe is not None:
-            pipe.scheduler = backup_scheduler
-
-        utils.free_memory()
-
-# シリーズが変更されたときに対応するキャラクターを更新
-def update_character_list(series_display_name):
-    characters = get_characters_for_series(series_display_name)
-    if characters and len(characters) > 0:
-        default_character = characters[0]
-    else:
-        default_character = display_character_list[0]
-    
-    return gr.update(choices=characters, value=default_character)
-
-# テキストからプロンプトを生成する関数を追加
-@spaces.GPU(timeout_seconds=120)
-def process_text_to_prompt(
-    novel_text: str,
-    series_display_name: str = series_dict.get(DEFAULT_SERIES, DEFAULT_SERIES),
-    character_display_name: str = character_dict.get(DEFAULT_CHARACTER, DEFAULT_CHARACTER),
-    category: str = DEFAULT_CATEGORY,
-) -> Tuple[str, str, Dict]:
-    """テキストからプロンプトを生成して、UIに表示する関数"""
-    try:
-        # 必要に応じてLLMをロード
-        if TEXT_TO_PROMPT_ENABLED and not hasattr(prompt_generator, "_model") or prompt_generator._model is None:
-            prompt_generator.load_model()
-        
-        thinking, prompt_text = convert_text_to_prompt(novel_text, series_display_name, character_display_name, category)
-
-        # プロンプト生成に関するメタデータ
-        metadata = {
-            "novel_text": novel_text[:100] + "..." if len(novel_text) > 100 else novel_text,
-            "series": series_display_name,
-            "character": character_display_name,
-            "category": category,
-            "generated_at": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-        }
-
-        return thinking, prompt_text, metadata
-
-    except Exception as e:
-        logger.exception("Error in process_text_to_prompt")
-        error_message = f"プロンプト生成中にエラーが発生しました: {str(e)}"
-        return error_message, "", {"error": str(e)}
-
-# 生成されたプロンプトを画像生成パラメータにコピーする関数
-def copy_prompt_to_generation(prompt_text):
-    return prompt_text, gr.update(visible=False)
-
-# スタイルが変更された時にプロンプトを更新する関数
-def update_prompt_with_style(prompt_text, current_style, new_style):
-    if prompt_text.strip() == "":
-        return prompt_text
-    
-    # スタイル情報を取得
-    styles = {style["name"]: (style["prompt"], style.get("negative_prompt", "")) for style in style_list}
-    
-    # 現在のスタイルのプロンプト部分を取得
-    current_style_prompt = ""
-    if current_style != "(None)":
-        current_style_template = styles.get(current_style, ("", ""))[0]
-        # {prompt} の部分を除外してスタイル部分だけを抽出
-        if "{prompt}" in current_style_template:
-            current_style_prompt = current_style_template.replace("{prompt}", "").strip()
-            if current_style_prompt.startswith(","):
-                current_style_prompt = current_style_prompt[1:].strip()
-    
-    # 新しいスタイルのプロンプト部分を取得
-    new_style_prompt = ""
-    if new_style != "(None)":
-        new_style_template = styles.get(new_style, ("", ""))[0]
-        # {prompt} の部分を除外してスタイル部分だけを抽出
-        if "{prompt}" in new_style_template:
-            new_style_prompt = new_style_template.replace("{prompt}", "").strip()
-            if new_style_prompt.startswith(","):
-                new_style_prompt = new_style_prompt[1:].strip()
-    
-    # 現在のプロンプトからスタイル部分を削除
-    base_prompt = prompt_text
-    if current_style_prompt:
-        style_part = f", {current_style_prompt}"
-        if style_part in base_prompt:
-            base_prompt = base_prompt.replace(style_part, "")
-    
-    # 新しいスタイルを追加
-    if new_style_prompt:
-        if base_prompt.strip():
-            base_prompt = f"{base_prompt.strip()}, {new_style_prompt}"
-        else:
-            base_prompt = new_style_prompt
-    
-    return base_prompt
-
-
-initialize_llm()
-
-
 # Create CSS with improved buttons and styling
 custom_css = """
 .header {
@@ -598,106 +81,29 @@ custom_css = """
     opacity: 0.9;
 }
 
-.section {
-    background: white;
+.notification {
+    background-color: #fff8e1;
+    border-left: 5px solid #ffc107;
+    padding: 20px;
+    margin: 20px 0;
+    font-size: 1.2rem;
     border-radius: 10px;
-    padding: 1.5rem;
-    margin-bottom: 1.5rem;
-    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.05);
-    border: 1px solid #e1e4e8;
+    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1);
 }
 
-.section-title {
-    font-size: 1.3rem;
-    margin-top: 0;
-    margin-bottom: 1.2rem;
-    color: #4a69bd;
-    border-bottom: 2px solid #e1e4e8;
-    padding-bottom: 0.5rem;
-}
-
-/* Improved button styling */
-.primary-button {
-    background-color: #4a69bd !important;
-    color: white !important;
-    font-weight: 600 !important;
-    padding: 0.7rem 1.2rem !important;
-    border-radius: 8px !important;
-    border: none !important;
-    cursor: pointer !important;
-    transition: all 0.2s ease !important;
-    box-shadow: 0 2px 5px rgba(0, 0, 0, 0.1) !important;
-    text-transform: uppercase !important;
-    letter-spacing: 0.5px !important;
-}
-
-.primary-button:hover {
-    background-color: #3a539b !important;
-    box-shadow: 0 4px 8px rgba(0, 0, 0, 0.15) !important;
-    transform: translateY(-1px) !important;
-}
-
-/* 思考プロセスとプロンプト出力のスタイルを改善 */
-.thinking-output-label {
-    font-weight: 600 !important;
-    color: #4285f4 !important;
-    background-color: transparent !important;
-    margin-bottom: 4px !important;
-}
-
-.thinking-output {
-    background-color: #f0f7ff !important;
-    border-left: 4px solid #4285f4 !important;
-    padding: 12px !important;
-    border-radius: 6px !important;
-    font-size: 0.95rem !important;
-    color: #333 !important;
-}
-
-.generated-prompt-label {
-    font-weight: 600 !important;
-    color: #34a853 !important;
-    background-color: transparent !important;
-    margin-bottom: 4px !important;
-    margin-top: 12px !important;
-}
-
-.generated-prompt {
-    background-color: #f0fff4 !important;
-    border-left: 4px solid #34a853 !important;
-    padding: 12px !important;
-    border-radius: 6px !important;
-    font-weight: 500 !important;
-    font-size: 0.95rem !important;
-    color: #333 !important;
-}
-
-.text-input-area {
-    border: 1px solid #d0d7de;
-    border-radius: 8px;
-}
-
-/* Add animation for loading states */
-@keyframes pulse {
-    0% { opacity: 1; }
-    50% { opacity: 0.7; }
-    100% { opacity: 1; }
+.notification-title {
+    color: #e65100;
+    font-size: 1.5rem;
+    margin-bottom: 10px;
+    font-weight: 600;
 }
 
-.loading {
-    animation: pulse 1.5s infinite;
+.en-message {
+    margin-bottom: 15px;
 }
 
-/* Gallery improvements */
-.gallery-item {
-    border-radius: 8px;
-    overflow: hidden;
-    box-shadow: 0 3px 10px rgba(0, 0, 0, 0.1);
-    transition: transform 0.2s ease;
-}
-
-.gallery-item:hover {
-    transform: scale(1.02);
+.jp-message {
+    font-weight: 500;
 }
 """
 
@@ -706,181 +112,20 @@ with gr.Blocks(css=custom_css) as demo:
     gr.HTML("<div class='header'><h1 class='title'>FanFic Illustrator <span class='subtitle-inline'>with Animagine XL 4.0 Opt</span></h1><p class='subtitle'>Illustrate your fan stories with beautiful AI-generated art<br>二次創作ファン小説にAIで魅力的な挿絵を</p></div>")
     
     with gr.Column():
-        # Text Input Section
-        with gr.Group(elem_classes=["section"]):
-            gr.HTML("<h3 class='section-title'>1. Your Narrative / あなたの創作した物語</h3>")
-            novel_text = gr.Textbox(
-                label="",
-                placeholder="Enter your fan story or narrative here... / ここにファンストーリーや物語を入力してください...",
-                lines=10,
-                elem_classes=["text-input-area"],
-            )
-            
-            with gr.Row():
-                with gr.Column(scale=1):
-                    series_selector = gr.Dropdown(
-                        choices=display_series_list,
-                        value=display_series_list[0] if display_series_list else "",
-                        label="Series / シリーズ",
-                    )
-                with gr.Column(scale=1):
-                    character_selector = gr.Dropdown(
-                        choices=get_characters_for_series(display_series_list[0] if display_series_list else ""),
-                        value=display_character_list[0] if display_character_list else "",
-                        label="Character / キャラクター",
-                    )
-            
-            with gr.Row():
-                category_selector = gr.Dropdown(
-                    choices=display_category_list,
-                    value=display_category_list[0] if display_category_list else "",
-                    label="Illustration Type / イラストタイプ",
-                )
-            
-            convert_btn = gr.Button("Generate Prompt / プロンプト生成", elem_classes=["primary-button"])
-        
-        # Thinking Process & Generated Prompt Section
-        with gr.Group(elem_classes=["section"]):
-            gr.HTML("<h3 class='section-title'>2. AI Interpretation / AIの解釈結果</h3>")
-            
-            gr.HTML("<div class='thinking-output-label'>AI Thought Process / AIの思考過程</div>")
-            thinking_output = gr.Textbox(
-                label="",
-                lines=6,
-                elem_classes=["thinking-output"],
-                visible=True
-            )
-            
-            gr.HTML("<div class='generated-prompt-label'>Generated Prompt / 生成されたプロンプト</div>")
-            prompt_output = gr.Textbox(
-                label="",
-                lines=3,
-                elem_classes=["generated-prompt"],
-            )
-            
-            use_prompt_btn = gr.Button("Create Illustration with This Prompt / このプロンプトでイラスト作成", elem_classes=["primary-button"])
-        
-        # Image Generation Section
-        with gr.Group(elem_classes=["section"]):
-            gr.HTML("<h3 class='section-title'>3. Illustration Generation / イラスト生成</h3>")
-            
-            # 生成イラストを一番上に配置
-            output_gallery = gr.Gallery(label="Generated Illustrations / 生成されたイラスト", show_label=True)
-            
-            # プロンプト入力欄
-            prompt = gr.Textbox(
-                label="Prompt / プロンプト",
-                placeholder="Enter your prompt here... / ここにプロンプトを入力してください...",
-                lines=3,
-            )
-            
-            # 詳細設定のアコーディオン - デフォルトでは閉じている
-            with gr.Accordion("Advanced Options / 詳細設定", open=False):
-
-                # スタイルセレクター
-                current_style = gr.State("(None)")  # 現在選択されているスタイルを保存
-                style_selector = gr.Dropdown(
-                    choices=style_names,
-                    value="(None)",
-                    label="Style / スタイル",
-                    info="Select a style to apply to your prompt / プロンプトに適用するスタイルを選択",
-                )
-
-                # ネガティブプロンプト
-                negative_prompt = gr.Textbox(
-                    label="Negative Prompt / ネガティブプロンプト",
-                    placeholder="What you don't want to see in the image / 画像に含めたくない要素",
-                    value=DEFAULT_NEGATIVE_PROMPT,
-                    lines=3,
-                )
-                
-            # 「イラスト生成」を「イラスト再生成」に変更
-            generate_btn = gr.Button("Regenerate Illustration / イラスト再生成", elem_classes=["primary-button"])
-    
-    # Setup event listeners
-    # シリーズが変更されたときにキャラクターリストを更新するイベント
-    series_selector.change(
-        fn=update_character_list,
-        inputs=[series_selector],
-        outputs=[character_selector],
-    )
-    
-    # スタイルが変更されたときにプロンプトを更新するイベント
-    style_selector.change(
-        fn=update_prompt_with_style,
-        inputs=[prompt, current_style, style_selector],
-        outputs=[prompt],
-    ).then(
-        fn=lambda x: x,
-        inputs=[style_selector],
-        outputs=[current_style],
-    )
-    
-    # プロンプト生成ボタンのイベント
-    convert_btn.click(
-        fn=process_text_to_prompt,
-        inputs=[
-            novel_text,
-            series_selector,
-            character_selector,
-            category_selector,
-        ],
-        outputs=[thinking_output, prompt_output, gr.JSON(visible=False)],
-    )
-    
-    # プロンプトを画像生成に使用するボタンのイベント
-    use_prompt_btn.click(
-        fn=copy_prompt_to_generation,
-        inputs=[prompt_output],
-        outputs=[prompt, gr.Textbox(visible=False)],
-    ).then(
-        fn=load_image_model,
-        inputs=[],
-        outputs=[gr.Textbox(visible=False)],
-    ).then(
-        fn=lambda p, np, style: generate(
-            prompt=p,
-            negative_prompt=np,
-            seed=0,  # デフォルトのseed値
-            custom_width=832,  # デフォルトの幅
-            custom_height=1216,  # デフォルトの高さ
-            guidance_scale=5.0,  # デフォルトのguidance_scale
-            num_inference_steps=28,  # デフォルトのnum_inference_steps
-            sampler="Euler a",  # デフォルトのsampler
-            aspect_ratio_selector=DEFAULT_ASPECT_RATIO,  # デフォルトのアスペクト比
-            style_selector=style,  # スタイルセレクターの値を渡す
-            use_upscaler=False,  # デフォルトのupscaler設定
-            upscaler_strength=0.55,  # デフォルトのupscaler強度
-            upscale_by=1.5,  # デフォルトのupscale値
-            add_quality_tags=True,  # デフォルトの品質タグ設定
-        ),
-        inputs=[prompt, negative_prompt, style_selector],
-        outputs=[output_gallery, gr.JSON(visible=False)],
-    )
-    
-    # 画像生成ボタンのイベント
-    generate_btn.click(
-        fn=lambda p, np, style: generate(
-            prompt=p,
-            negative_prompt=np,
-            seed=0,
-            custom_width=832,
-            custom_height=1216,
-            guidance_scale=5.0,
-            num_inference_steps=28,
-            sampler="Euler a",
-            aspect_ratio_selector=DEFAULT_ASPECT_RATIO,
-            style_selector=style,
-            use_upscaler=False,
-            upscaler_strength=0.55,
-            upscale_by=1.5,
-            add_quality_tags=True,
-        ),
-        inputs=[prompt, negative_prompt, style_selector],
-        outputs=[output_gallery, gr.JSON(visible=False)],
-    )
-
+        # Service temporarily unavailable notification
+        gr.HTML("""
+        <div class="notification">
+            <div class="notification-title">Service Temporarily Suspended</div>
+            <div class="en-message">
+                This service has been temporarily suspended due to frequent ZERO GPU allocation failures.
+            </div>
+            <div class="jp-message">
+                ZERO GPUの割当に失敗する事が多すぎるので一時停止しました。
+            </div>
+        </div>
+        """)
 
 # Launch the app
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", share=IS_COLAB)
+