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Gerchegg commited on Oct 18, 2025

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-import gradio as gr
-import numpy as np
-import random
-import json
-import torch
-from PIL import Image
-import os
-import time
-import logging
-# Опциональный импорт spaces для работы в Runpod
-try:
-    import spaces
-    SPACES_AVAILABLE = True
-except ImportError:
-    SPACES_AVAILABLE = False
-    logger = logging.getLogger(__name__)
-    logger.warning("⚠️ spaces module not available - running without ZeroGPU support")
-from diffusers import (
-    DiffusionPipeline,
-    QwenImageImg2ImgPipeline
-)
-from huggingface_hub import hf_hub_download
-# Настройка логирования
-logging.basicConfig(
-    level=logging.INFO,
-    format='%(asctime)s | %(levelname)s | %(message)s',
-    datefmt='%Y-%m-%d %H:%M:%S'
-)
-logger = logging.getLogger(__name__)
-logger.info("=" * 60)
-logger.info("LOADING QWEN-SOLOBAND ADVANCED")
-logger.info("=" * 60)
-hf_token = os.environ.get("HF_TOKEN")
-device = "cuda" if torch.cuda.is_available() else "cpu"
-dtype = torch.bfloat16
-# Логируем GPU
-logger.info(f"CUDA available: {torch.cuda.is_available()}")
-if torch.cuda.is_available():
-    gpu_count = torch.cuda.device_count()
-    logger.info(f"Number of GPUs: {gpu_count}")
-    for i in range(gpu_count):
-        logger.info(f"  GPU {i}: {torch.cuda.get_device_name(i)}")
-        logger.info(f"    Memory: {torch.cuda.get_device_properties(i).total_memory / 1024**3:.1f} GB")
-# =================================================================
-# ЗАГРУЗКА МОДЕЛЕЙ
-# =================================================================
-# 1. Базовая модель для Text-to-Image
-logger.info("\n[1/3] Loading base Text2Image model...")
-model_id = "Gerchegg/Qwen-Soloband-Diffusers"
-try:
-    start_time = time.time()
-    # Определяем device_map
-    if gpu_count > 1:
-        device_map = "balanced"
-        logger.info(f"  Device map: balanced ({gpu_count} GPUs)")
-    else:
-        device_map = None
-        logger.info("  Device map: single GPU")
-    # Загружаем базовую модель
-    pipe_txt2img = DiffusionPipeline.from_pretrained(
-        model_id,
-        torch_dtype=dtype,
-        device_map=device_map,
-        token=hf_token
-    )
-    if device_map is None:
-        pipe_txt2img.to(device)
-    load_time = time.time() - start_time
-    logger.info(f"  ✓ Text2Image loaded in {load_time:.1f}s")
-except Exception as e:
-    logger.error(f"  ❌ Error loading Text2Image: {e}")
-    raise
-# 2. Image-to-Image модель (используем те же компоненты)
-logger.info("\n[2/3] Creating Image2Image pipeline...")
-try:
-    # Создаем QwenImageImg2ImgPipeline переиспользуя компоненты Text2Image pipeline
-    # Это правильный способ для Qwen-Image архитектуры
-    pipe_img2img = QwenImageImg2ImgPipeline(
-        vae=pipe_txt2img.vae,
-        text_encoder=pipe_txt2img.text_encoder,
-        tokenizer=pipe_txt2img.tokenizer,
-        transformer=pipe_txt2img.transformer,
-        scheduler=pipe_txt2img.scheduler
-    )
-    logger.info("  ✓ Image2Image pipeline created (reusing components)")
-except Exception as e:
-    logger.error(f"  ❌ Error creating Image2Image: {e}")
-    pipe_img2img = None
-# ControlNet не используется - убран для упрощения
-# Оптимизации памяти
-logger.info("\nApplying memory optimizations...")
-for pipe in [pipe_txt2img, pipe_img2img]:
-    if pipe and hasattr(pipe, 'vae'):
-        if hasattr(pipe.vae, 'enable_tiling'):
-            pipe.vae.enable_tiling()
-        if hasattr(pipe.vae, 'enable_slicing'):
-            pipe.vae.enable_slicing()
-logger.info("  ✓ VAE tiling and slicing enabled")
-logger.info("\n" + "=" * 60)
-logger.info("✓ ALL MODELS LOADED")
-logger.info("=" * 60)
-# =================================================================
-# HELPER FUNCTIONS
-# =================================================================
-def resize_image(input_image, max_size=1024):
-    """Изменяет размер изображения с сохранением пропорций (кратно 8)"""
-    w, h = input_image.size
-    aspect_ratio = w / h
-    if w > h:
-        new_w = max_size
-        new_h = int(new_w / aspect_ratio)
-    else:
-        new_h = max_size
-        new_w = int(new_h * aspect_ratio)
-    # Кратно 8
-    new_w = new_w - (new_w % 8)
-    new_h = new_h - (new_h % 8)
-    if new_w == 0: new_w = 8
-    if new_h == 0: new_h = 8
-    return input_image.resize((new_w, new_h), Image.Resampling.LANCZOS)
-# =================================================================
-# LORA FUNCTIONS
-# =================================================================
-# Папка дл�� локальных LoRA
-LOCAL_LORA_DIR = "/workspace/loras"
-# Базовые LoRA из HuggingFace Hub (загружаются по требованию)
-HUB_LORAS = {
-    "Realism": {
-        "repo": "flymy-ai/qwen-image-realism-lora",
-        "trigger": "Super Realism portrait of",
-        "weights": "pytorch_lora_weights.safetensors",
-        "source": "hub"
-    },
-    "Anime": {
-        "repo": "alfredplpl/qwen-image-modern-anime-lora",
-        "trigger": "Japanese modern anime style, ",
-        "weights": "pytorch_lora_weights.safetensors",
-        "source": "hub"
-    }
-    # Другие LoRA положите в /workspace/loras/ как .safetensors файлы
-}
-def scan_local_loras():
-    """
-    Сканирует папку /workspace/loras на наличие .safetensors файлов
-    Возвращает dict с найденными LoRA
-    """
-    local_loras = {}
-    if not os.path.exists(LOCAL_LORA_DIR):
-        logger.info(f"  Local LoRA directory not found: {LOCAL_LORA_DIR}")
-        return local_loras
-    logger.info(f"  Scanning local LoRA directory: {LOCAL_LORA_DIR}")
-    try:
-        for file in os.listdir(LOCAL_LORA_DIR):
-            if file.endswith('.safetensors'):
-                lora_name = os.path.splitext(file)[0]  # Имя без расширения
-                local_path = os.path.join(LOCAL_LORA_DIR, file)
-                # Добавляем в список
-                local_loras[lora_name] = {
-                    "path": local_path,
-                    "trigger": "",  # Без trigger word для локальных
-                    "weights": file,
-                    "source": "local"
-                }
-                logger.info(f"    ✓ Found local LoRA: {lora_name} ({file})")
-    except Exception as e:
-        logger.warning(f"  Error scanning local LoRA directory: {e}")
-    return local_loras
-# Сканируем локальные LoRA
-logger.info("\nScanning for LoRA models...")
-LOCAL_LORAS = scan_local_loras()
-# Объединяем Hub и локальные LoRA
-AVAILABLE_LORAS = {**HUB_LORAS, **LOCAL_LORAS}
-if LOCAL_LORAS:
-    logger.info(f"  ✓ Found {len(LOCAL_LORAS)} local LoRA(s)")
-logger.info(f"  Total available LoRAs: {len(AVAILABLE_LORAS)}")
-def load_lora_weights(pipeline, lora_name, lora_scale, hf_token):
-    """
-    Загружает LoRA веса в pipeline (ленивая загрузка)
-    Hub LoRA скачиваются только при использовании
-    Локальные LoRA загружаются из /workspace/loras/
-    """
-    if lora_name == "None" or lora_name not in AVAILABLE_LORAS:
-        return None
-    lora_info = AVAILABLE_LORAS[lora_name]
-    try:
-        if lora_info['source'] == 'hub':
-            # Ленивая загрузка с HuggingFace Hub (скачивается при первом использовании)
-            logger.info(f"  Loading LoRA from Hub: {lora_info['repo']}")
-            logger.info(f"    (Downloading if not cached...)")
-            pipeline.load_lora_weights(
-                lora_info['repo'],
-                weight_name=lora_info.get('weights', 'pytorch_lora_weights.safetensors'),
-                token=hf_token
-            )
-            logger.info(f"    ✓ Hub LoRA loaded (cached for future use)")
-        else:
-            # Загрузка локального файла из /workspace/loras/
-            logger.info(f"  Loading local LoRA: {lora_info['path']}")
-            pipeline.load_lora_weights(
-                lora_info['path'],
-                adapter_name=lora_name
-            )
-            logger.info(f"    ✓ Local LoRA loaded")
-        # Устанавливаем scale
-        if hasattr(pipeline, 'set_adapters'):
-            pipeline.set_adapters([lora_name], adapter_weights=[lora_scale])
-        return lora_info.get('trigger', '')
-    except Exception as e:
-        logger.error(f"  ❌ Error loading LoRA {lora_name}: {e}")
-        return None
-# =================================================================
-# GENERATION FUNCTIONS
-# =================================================================
-MAX_SEED = np.iinfo(np.int32).max
-# Декоратор для spaces если доступен
-def gpu_decorator(duration=180):
-    def decorator(func):
-        if SPACES_AVAILABLE:
-            return spaces.GPU(duration=duration)(func)
-        return func
-    return decorator
-@gpu_decorator(duration=180)
-def generate_text2img(
-    prompt,
-    negative_prompt=" ",
-    width=1664,
-    height=928,
-    seed=42,
-    randomize_seed=False,
-    guidance_scale=2.5,
-    num_inference_steps=40,
-    lora_name="None",
-    lora_scale=1.0,
-    progress=gr.Progress(track_tqdm=True)
-):
-    """Text-to-Image генерация"""
-    logger.info("\n" + "=" * 60)
-    logger.info("TEXT-TO-IMAGE GENERATION")
-    logger.info("=" * 60)
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    logger.info(f"  Prompt: {prompt[:100]}...")
-    logger.info(f"  Size: {width}x{height}")
-    logger.info(f"  Steps: {num_inference_steps}, CFG: {guidance_scale}")
-    logger.info(f"  Seed: {seed}")
-    logger.info(f"  LoRA: {lora_name} (scale: {lora_scale})")
-    try:
-        # Загружаем LoRA если выбрана
-        trigger_word = None
-        if lora_name != "None":
-            trigger_word = load_lora_weights(pipe_txt2img, lora_name, lora_scale, hf_token)
-            # Добавляем trigger word если есть
-            if trigger_word:
-                prompt = trigger_word + prompt
-                logger.info(f"  Added trigger: {trigger_word}")
-        generator = torch.Generator(device=device).manual_seed(seed)
-        image = pipe_txt2img(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            width=width,
-            height=height,
-            num_inference_steps=num_inference_steps,
-            true_cfg_scale=guidance_scale,
-            generator=generator
-        ).images[0]
-        # Выгружаем LoRA после генерации
-        if lora_name != "None":
-            pipe_txt2img.unload_lora_weights()
-        logger.info("  ✓ Generation completed")
-        return image, seed
-    except Exception as e:
-        logger.error(f"  ❌ Error: {e}")
-        raise
-@gpu_decorator(duration=180)
-def generate_img2img(
-    input_image,
-    prompt,
-    negative_prompt=" ",
-    strength=0.75,
-    seed=42,
-    randomize_seed=False,
-    guidance_scale=2.5,
-    num_inference_steps=40,
-    lora_name="None",
-    lora_scale=1.0,
-    progress=gr.Progress(track_tqdm=True)
-):
-    """Image-to-Image генерация"""
-    logger.info("\n" + "=" * 60)
-    logger.info("IMAGE-TO-IMAGE GENERATION")
-    logger.info("=" * 60)
-    if input_image is None:
-        raise gr.Error("Please upload an input image")
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-    # Изменяем размер изображения
-    resized = resize_image(input_image, max_size=1024)
-    logger.info(f"  Prompt: {prompt[:100]}...")
-    logger.info(f"  Input size: {input_image.size} → {resized.size}")
-    logger.info(f"  Strength: {strength}")
-    logger.info(f"  Steps: {num_inference_steps}, CFG: {guidance_scale}")
-    logger.info(f"  LoRA: {lora_name}")
-    try:
-        if pipe_img2img is None:
-            raise gr.Error("Image2Image pipeline not available")
-        # Загружаем LoRA если выбрана
-        trigger_word = None
-        if lora_name != "None":
-            trigger_word = load_lora_weights(pipe_img2img, lora_name, lora_scale, hf_token)
-            # Добавляем trigger word если есть
-            if trigger_word:
-                prompt = trigger_word + prompt
-        generator = torch.Generator(device=device).manual_seed(seed)
-        image = pipe_img2img(
-            prompt=prompt,
-            negative_prompt=negative_prompt,
-            image=resized,
-            strength=strength,
-            num_inference_steps=num_inference_steps,
-            true_cfg_scale=guidance_scale,
-            generator=generator
-        ).images[0]
-        # Выгружаем LoRA
-        if lora_name != "None":
-            pipe_img2img.unload_lora_weights()
-        logger.info("  ✓ Generation completed")
-        return image, seed
-    except Exception as e:
-        logger.error(f"  ❌ Error: {e}")
-        raise
-# ControlNet функция убрана - не используется
-# =================================================================
-# GRADIO INTERFACE
-# =================================================================
-MAX_SEED = np.iinfo(np.int32).max
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 1400px;
-}
-"""
-with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
-    lora_choices = ["None"] + list(AVAILABLE_LORAS.keys())
-    gr.Markdown(f"""
-    # 🎨 Qwen Soloband - Image2Image + LoRA
-    **Продвинутая модель генерации** с поддержкой Text-to-Image, Image-to-Image и LoRA стилей.
-    ### ✨ Возможности:
-    - 🖼️ **Text-to-Image** - Генерация из текста, разрешения до 2048×2048
-    - 🔄 **Image-to-Image** - Модификация изображений с контролем strength (0.0-1.0)
-    - 🎭 **LoRA Support** - {len(AVAILABLE_LORAS)} доступных стилей (Hub + локальные)
-    - 🔌 **Full API** - Все функции доступны через API
-    - ⚡ **Optimized** - VAE tiling/slicing, правильный QwenImageImg2ImgPipeline
-    **Модель**: [Gerchegg/Qwen-Soloband-Diffusers](https://huggingface.co/Gerchegg/Qwen-Soloband-Diffusers)
-    💡 **Local LoRAs**: Положите .safetensors файлы в `/workspace/loras/` - они появятся автоматически!
-    """)
-    with gr.Tabs() as tabs:
-        # TAB 1: Text-to-Image
-        with gr.Tab("📝 Text-to-Image"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    t2i_prompt = gr.Text(
-                        label="Prompt",
-                        placeholder="SB_AI, a beautiful landscape...",
-                        lines=3
-                    )
-                    t2i_run = gr.Button("Generate", variant="primary")
-                    with gr.Accordion("Advanced Settings", open=False):
-                        t2i_negative = gr.Text(label="Negative Prompt", value="blurry, low quality")
-                        with gr.Row():
-                            t2i_width = gr.Slider(label="Width", minimum=512, maximum=2048, step=64, value=1664)
-                            t2i_height = gr.Slider(label="Height", minimum=512, maximum=2048, step=64, value=928)
-                        with gr.Row():
-                            t2i_steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=40)
-                            t2i_cfg = gr.Slider(label="CFG", minimum=0.0, maximum=7.5, step=0.1, value=2.5)
-                        with gr.Row():
-                            t2i_seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-                            t2i_random_seed = gr.Checkbox(label="Random", value=True)
-                        t2i_lora = gr.Radio(
-                            label="LoRA Style",
-                            choices=lora_choices,
-                            value="None",
-                            info=f"Hub: {len(HUB_LORAS)}, Local: {len(LOCAL_LORAS)}"
-                        )
-                        t2i_lora_scale = gr.Slider(label="LoRA Strength", minimum=0.0, maximum=2.0, step=0.1, value=1.0)
-                with gr.Column(scale=1):
-                    t2i_output = gr.Image(label="Generated Image")
-                    t2i_seed_output = gr.Number(label="Used Seed")
-        # TAB 2: Image-to-Image
-        with gr.Tab("🔄 Image-to-Image"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    i2i_input = gr.Image(type="pil", label="Input Image")
-                    i2i_prompt = gr.Text(
-                        label="Prompt",
-                        placeholder="Transform this image into...",
-                        lines=3
-                    )
-                    i2i_strength = gr.Slider(
-                        label="Denoising Strength",
-                        info="0.0 = original image, 1.0 = complete redraw",
-                        minimum=0.0,
-                        maximum=1.0,
-                        step=0.05,
-                        value=0.75
-                    )
-                    i2i_run = gr.Button("Generate", variant="primary")
-                    with gr.Accordion("Advanced Settings", open=False):
-                        i2i_negative = gr.Text(label="Negative Prompt", value="blurry, low quality")
-                        with gr.Row():
-                            i2i_steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=40)
-                            i2i_cfg = gr.Slider(label="CFG", minimum=0.0, maximum=7.5, step=0.1, value=2.5)
-                        with gr.Row():
-                            i2i_seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
-                            i2i_random_seed = gr.Checkbox(label="Random", value=True)
-                        i2i_lora = gr.Radio(
-                            label="LoRA Style",
-                            choices=lora_choices,
-                            value="None",
-                            info=f"Hub: {len(HUB_LORAS)}, Local: {len(LOCAL_LORAS)}"
-                        )
-                        i2i_lora_scale = gr.Slider(label="LoRA Strength", minimum=0.0, maximum=2.0, step=0.1, value=1.0)
-                with gr.Column(scale=1):
-                    i2i_output = gr.Image(label="Generated Image")
-                    i2i_seed_output = gr.Number(label="Used Seed")
-    # Event handlers
-    t2i_run.click(
-        fn=generate_text2img,
-        inputs=[
-            t2i_prompt, t2i_negative, t2i_width, t2i_height,
-            t2i_seed, t2i_random_seed, t2i_cfg, t2i_steps,
-            t2i_lora, t2i_lora_scale
-        ],
-        outputs=[t2i_output, t2i_seed_output],
-        api_name="text2img"
-    )
-    i2i_run.click(
-        fn=generate_img2img,
-        inputs=[
-            i2i_input, i2i_prompt, i2i_negative, i2i_strength,
-            i2i_seed, i2i_random_seed, i2i_cfg, i2i_steps,
-            i2i_lora, i2i_lora_scale
-        ],
-        outputs=[i2i_output, i2i_seed_output],
-        api_name="img2img"
-    )
-if __name__ == "__main__":
-    demo.launch(
-        show_api=True,
-        share=False
-    )

+import gradio as gr
+import numpy as np
+import random
+import json
+import torch
+from PIL import Image
+import os
+import time
+import logging
+# Опциональный импорт spaces для работы в Runpod
+try:
+    import spaces
+    SPACES_AVAILABLE = True
+except ImportError:
+    SPACES_AVAILABLE = False
+    logger = logging.getLogger(__name__)
+    logger.warning("⚠️ spaces module not available - running without ZeroGPU support")
+from diffusers import (
+    DiffusionPipeline,
+    QwenImageImg2ImgPipeline
+)
+from diffusers.schedulers import (
+    DPMSolverMultistepScheduler,
+    EulerDiscreteScheduler,
+    EulerAncestralDiscreteScheduler,
+    UniPCMultistepScheduler
+)
+from huggingface_hub import hf_hub_download
+# Настройка логирования
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s | %(levelname)s | %(message)s',
+    datefmt='%Y-%m-%d %H:%M:%S'
+)
+logger = logging.getLogger(__name__)
+logger.info("=" * 60)
+logger.info("LOADING QWEN-SOLOBAND ADVANCED")
+logger.info("=" * 60)
+hf_token = os.environ.get("HF_TOKEN")
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.bfloat16
+# Логируем GPU
+logger.info(f"CUDA available: {torch.cuda.is_available()}")
+if torch.cuda.is_available():
+    gpu_count = torch.cuda.device_count()
+    logger.info(f"Number of GPUs: {gpu_count}")
+    for i in range(gpu_count):
+        logger.info(f"  GPU {i}: {torch.cuda.get_device_name(i)}")
+        logger.info(f"    Memory: {torch.cuda.get_device_properties(i).total_memory / 1024**3:.1f} GB")
+# =================================================================
+# SCHEDULER CONFIGURATIONS
+# =================================================================
+# Конфигурации для разных типов schedulers
+# Основаны на исследовании популярных вариантов в Qwen-Image и сообществе
+SCHEDULER_CONFIGS = {
+    # === ПОПУЛЯРНЫЕ (Рекомендуемые) ===
+    "DPM++ 2M Karras": {
+        "class": DPMSolverMultistepScheduler,
+        "config": {"algorithm_type": "dpmsolver++", "solver_order": 2, "use_karras_sigmas": True},
+        "alias": "dpmpp_2m_karras"
+    },
+    "DPM++ SDE Karras": {
+        "class": DPMSolverMultistepScheduler,
+        "config": {"algorithm_type": "sde-dpmsolver++", "solver_order": 2, "use_karras_sigmas": True},
+        "alias": "dpmpp_sde_karras"
+    },
+    "DPM++ 3M SDE Karras": {
+        "class": DPMSolverMultistepScheduler,
+        "config": {"algorithm_type": "sde-dpmsolver++", "solver_order": 3, "use_karras_sigmas": True},
+        "alias": "dpmpp_3m_sde_karras"
+    },
+    "Euler Ancestral": {
+        "class": EulerAncestralDiscreteScheduler,
+        "config": {},
+        "alias": "euler_a"
+    },
+    "Euler": {
+        "class": EulerDiscreteScheduler,
+        "config": {},
+        "alias": "euler"
+    },
+    # === ЭКСПЕРИМЕНТАЛЬНЫЕ (Альтернативные варианты сигм) ===
+    "DPM++ 2M": {
+        "class": DPMSolverMultistepScheduler,
+        "config": {"algorithm_type": "dpmsolver++", "solver_order": 2},
+        "alias": "dpmpp_2m"
+    },
+    "DPM++ 2M Beta": {
+        "class": DPMSolverMultistepScheduler,
+        "config": {"algorithm_type": "dpmsolver++", "solver_order": 2, "use_beta_sigmas": True},
+        "alias": "dpmpp_2m_beta"
+    },
+    "DPM++ 2M Exponential": {
+        "class": DPMSolverMultistepScheduler,
+        "config": {"algorithm_type": "dpmsolver++", "solver_order": 2, "use_exponential_sigmas": True},
+        "alias": "dpmpp_2m_exp"
+    },
+    "Euler Karras": {
+        "class": EulerDiscreteScheduler,
+        "config": {"use_karras_sigmas": True},
+        "alias": "euler_karras"
+    },
+    # === БЫСТРЫЕ (Для экспериментов) ===
+    "UniPC": {
+        "class": UniPCMultistepScheduler,
+        "config": {},
+        "alias": "unipc"
+    }
+}
+DEFAULT_SCHEDULER = "DPM++ 2M Karras"
+# Для UI - группируем по категориям для удобства
+SCHEDULER_GROUPS = {
+    "⭐ Рекомендуемые": [
+        "DPM++ 2M Karras",
+        "DPM++ SDE Karras",
+        "DPM++ 3M SDE Karras",
+        "Euler Ancestral",
+        "Euler"
+    ],
+    "🧪 Экспериментальные": [
+        "DPM++ 2M",
+        "DPM++ 2M Beta",
+        "DPM++ 2M Exponential",
+        "Euler Karras"
+    ],
+    "⚡ Быстрые": [
+        "UniPC"
+    ]
+}
+# Плоский список для API
+SCHEDULER_CHOICES = list(SCHEDULER_CONFIGS.keys())
+# Маппинг алиасов для API (можно использовать как alias, так и полное имя)
+SCHEDULER_ALIASES = {v["alias"]: k for k, v in SCHEDULER_CONFIGS.items()}
+logger.info(f"Available schedulers: {len(SCHEDULER_CONFIGS)}")
+logger.info(f"  Default: {DEFAULT_SCHEDULER}")
+# =================================================================
+# ЗАГРУЗКА МОДЕЛЕЙ
+# =================================================================
+# 1. Базовая модель для Text-to-Image
+logger.info("\n[1/3] Loading base Text2Image model...")
+model_id = os.environ.get("MODEL_REPO", "Gerchegg/Qwen-Soloband-Diffusers")
+logger.info(f"  Model ID: {model_id}")
+try:
+    start_time = time.time()
+    # Определяем device_map
+    if gpu_count > 1:
+        device_map = "balanced"
+        logger.info(f"  Device map: balanced ({gpu_count} GPUs)")
+    else:
+        device_map = None
+        logger.info("  Device map: single GPU")
+    # Загружаем базовую модель
+    pipe_txt2img = DiffusionPipeline.from_pretrained(
+        model_id,
+        torch_dtype=dtype,
+        device_map=device_map,
+        token=hf_token
+    )
+    if device_map is None:
+        pipe_txt2img.to(device)
+    load_time = time.time() - start_time
+    logger.info(f"  ✓ Text2Image loaded in {load_time:.1f}s")
+except Exception as e:
+    logger.error(f"  ❌ Error loading Text2Image: {e}")
+    raise
+# 2. Image-to-Image модель (используем те же компоненты)
+logger.info("\n[2/3] Creating Image2Image pipeline...")
+try:
+    # Создаем QwenImageImg2ImgPipeline переиспользуя компоненты Text2Image pipeline
+    # Это правильный способ для Qwen-Image архитектуры
+    pipe_img2img = QwenImageImg2ImgPipeline(
+        vae=pipe_txt2img.vae,
+        text_encoder=pipe_txt2img.text_encoder,
+        tokenizer=pipe_txt2img.tokenizer,
+        transformer=pipe_txt2img.transformer,
+        scheduler=pipe_txt2img.scheduler
+    )
+    logger.info("  ✓ Image2Image pipeline created (reusing components)")
+except Exception as e:
+    logger.error(f"  ❌ Error creating Image2Image: {e}")
+    pipe_img2img = None
+# ControlNet не используется - убран для упрощения
+# Оптимизации памяти
+logger.info("\nApplying memory optimizations...")
+for pipe in [pipe_txt2img, pipe_img2img]:
+    if pipe and hasattr(pipe, 'vae'):
+        if hasattr(pipe.vae, 'enable_tiling'):
+            pipe.vae.enable_tiling()
+        if hasattr(pipe.vae, 'enable_slicing'):
+            pipe.vae.enable_slicing()
+logger.info("  ✓ VAE tiling and slicing enabled")
+logger.info("\n" + "=" * 60)
+logger.info("✓ ALL MODELS LOADED")
+logger.info("=" * 60)
+# =================================================================
+# SCHEDULER FUNCTIONS
+# =================================================================
+def resolve_scheduler_name(scheduler_name):
+    """Разрешает алиас scheduler в полное имя"""
+    # Если это алиас, преобразуем в полное имя
+    if scheduler_name in SCHEDULER_ALIASES:
+        return SCHEDULER_ALIASES[scheduler_name]
+    # Если это уже полное имя, возвращаем как есть
+    if scheduler_name in SCHEDULER_CONFIGS:
+        return scheduler_name
+    # Если не найдено, возвращаем дефолтный
+    logger.warning(f"Unknown scheduler: {scheduler_name}, using default: {DEFAULT_SCHEDULER}")
+    return DEFAULT_SCHEDULER
+def switch_scheduler(pipeline, scheduler_name):
+    """
+    Переключает scheduler в pipeline
+    Args:
+        pipeline: DiffusionPipeline или QwenImageImg2ImgPipeline
+        scheduler_name: Имя scheduler или его алиас
+    Returns:
+        pipeline с новым scheduler
+    """
+    # Разрешаем алиас
+    scheduler_name = resolve_scheduler_name(scheduler_name)
+    if scheduler_name not in SCHEDULER_CONFIGS:
+        logger.warning(f"Unknown scheduler: {scheduler_name}, using default")
+        scheduler_name = DEFAULT_SCHEDULER
+    scheduler_info = SCHEDULER_CONFIGS[scheduler_name]
+    scheduler_class = scheduler_info["class"]
+    scheduler_config = scheduler_info["config"]
+    try:
+        # Создаем новый scheduler с конфигом текущего + дополнительными параметрами
+        new_scheduler = scheduler_class.from_config(
+            pipeline.scheduler.config,
+            **scheduler_config
+        )
+        pipeline.scheduler = new_scheduler
+        logger.info(f"  Switched to scheduler: {scheduler_name} ({scheduler_info['alias']})")
+    except Exception as e:
+        logger.error(f"  ❌ Error switching scheduler: {e}")
+        logger.info(f"  Keeping current scheduler")
+    return pipeline
+# =================================================================
+# HELPER FUNCTIONS
+# =================================================================
+def resize_image(input_image, max_size=1024):
+    """Изменяет размер изображения с сохранением пропорций (кратно 8)"""
+    w, h = input_image.size
+    aspect_ratio = w / h
+    if w > h:
+        new_w = max_size
+        new_h = int(new_w / aspect_ratio)
+    else:
+        new_h = max_size
+        new_w = int(new_h * aspect_ratio)
+    # Кратно 8
+    new_w = new_w - (new_w % 8)
+    new_h = new_h - (new_h % 8)
+    if new_w == 0: new_w = 8
+    if new_h == 0: new_h = 8
+    return input_image.resize((new_w, new_h), Image.Resampling.LANCZOS)
+# =================================================================
+# LORA FUNCTIONS
+# =================================================================
+# Папка для локальных LoRA
+LOCAL_LORA_DIR = "/workspace/loras"
+# Базовые LoRA из HuggingFace Hub (загружаются по требованию)
+HUB_LORAS = {
+    "Realism": {
+        "repo": "flymy-ai/qwen-image-realism-lora",
+        "trigger": "Super Realism portrait of",
+        "weights": "pytorch_lora_weights.safetensors",
+        "source": "hub"
+    },
+    "Anime": {
+        "repo": "alfredplpl/qwen-image-modern-anime-lora",
+        "trigger": "Japanese modern anime style, ",
+        "weights": "pytorch_lora_weights.safetensors",
+        "source": "hub"
+    }
+    # Другие LoRA положите в /workspace/loras/ как .safetensors файлы
+}
+def scan_local_loras():
+    """
+    Сканирует папку /workspace/loras на наличие .safetensors файлов
+    Возвращает dict с найденными LoRA
+    """
+    local_loras = {}
+    if not os.path.exists(LOCAL_LORA_DIR):
+        logger.info(f"  Local LoRA directory not found: {LOCAL_LORA_DIR}")
+        return local_loras
+    logger.info(f"  Scanning local LoRA directory: {LOCAL_LORA_DIR}")
+    try:
+        for file in os.listdir(LOCAL_LORA_DIR):
+            if file.endswith('.safetensors'):
+                lora_name = os.path.splitext(file)[0]  # Имя без расширения
+                local_path = os.path.join(LOCAL_LORA_DIR, file)
+                # Добавляем в список
+                local_loras[lora_name] = {
+                    "path": local_path,
+                    "trigger": "",  # Без trigger word для локальных
+                    "weights": file,
+                    "source": "local"
+                }
+                logger.info(f"    ✓ Found local LoRA: {lora_name} ({file})")
+    except Exception as e:
+        logger.warning(f"  Error scanning local LoRA directory: {e}")
+    return local_loras
+# Сканируем локальные LoRA
+logger.info("\nScanning for LoRA models...")
+LOCAL_LORAS = scan_local_loras()
+# Объединяем Hub и локальные LoRA
+AVAILABLE_LORAS = {**HUB_LORAS, **LOCAL_LORAS}
+if LOCAL_LORAS:
+    logger.info(f"  ✓ Found {len(LOCAL_LORAS)} local LoRA(s)")
+logger.info(f"  Total available LoRAs: {len(AVAILABLE_LORAS)}")
+def load_lora_weights(pipeline, lora_name, lora_scale, hf_token):
+    """
+    Загружает LoRA веса в pipeline (ленивая загрузка)
+    Hub LoRA скачиваются только при использовании
+    Локальные LoRA загружаются из /workspace/loras/
+    """
+    if lora_name == "None" or lora_name not in AVAILABLE_LORAS:
+        return None
+    lora_info = AVAILABLE_LORAS[lora_name]
+    try:
+        if lora_info['source'] == 'hub':
+            # Ленивая загрузка с HuggingFace Hub (скачивается при первом использовании)
+            logger.info(f"  Loading LoRA from Hub: {lora_info['repo']}")
+            logger.info(f"    (Downloading if not cached...)")
+            pipeline.load_lora_weights(
+                lora_info['repo'],
+                weight_name=lora_info.get('weights', 'pytorch_lora_weights.safetensors'),
+                token=hf_token
+            )
+            logger.info(f"    ✓ Hub LoRA loaded (cached for future use)")
+        else:
+            # Загрузка локального файла из /workspace/loras/
+            logger.info(f"  Loading local LoRA: {lora_info['path']}")
+            pipeline.load_lora_weights(
+                lora_info['path'],
+                adapter_name=lora_name
+            )
+            logger.info(f"    ✓ Local LoRA loaded")
+        # Устанавливаем scale
+        if hasattr(pipeline, 'set_adapters'):
+            pipeline.set_adapters([lora_name], adapter_weights=[lora_scale])
+        return lora_info.get('trigger', '')
+    except Exception as e:
+        logger.error(f"  ❌ Error loading LoRA {lora_name}: {e}")
+        return None
+# =================================================================
+# GENERATION FUNCTIONS
+# =================================================================
+MAX_SEED = np.iinfo(np.int32).max
+# Декоратор для spaces если доступен
+def gpu_decorator(duration=180):
+    def decorator(func):
+        if SPACES_AVAILABLE:
+            return spaces.GPU(duration=duration)(func)
+        return func
+    return decorator
+@gpu_decorator(duration=180)
+def generate_text2img(
+    prompt,
+    negative_prompt=" ",
+    width=1664,
+    height=928,
+    seed=42,
+    randomize_seed=False,
+    guidance_scale=2.5,
+    num_inference_steps=40,
+    lora_name="None",
+    lora_scale=1.0,
+    scheduler_name=DEFAULT_SCHEDULER,
+    progress=gr.Progress(track_tqdm=True)
+):
+    """Text-to-Image генерация"""
+    logger.info("\n" + "=" * 60)
+    logger.info("TEXT-TO-IMAGE GENERATION")
+    logger.info("=" * 60)
+    # Switch scheduler if needed
+    switch_scheduler(pipe_txt2img, scheduler_name)
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    logger.info(f"  Prompt: {prompt[:100]}...")
+    logger.info(f"  Size: {width}x{height}")
+    logger.info(f"  Steps: {num_inference_steps}, CFG: {guidance_scale}")
+    logger.info(f"  Seed: {seed}")
+    logger.info(f"  Scheduler: {scheduler_name}")
+    logger.info(f"  LoRA: {lora_name} (scale: {lora_scale})")
+    try:
+        # Загружаем LoRA если выбрана
+        trigger_word = None
+        if lora_name != "None":
+            trigger_word = load_lora_weights(pipe_txt2img, lora_name, lora_scale, hf_token)
+            # Добавляем trigger word если есть
+            if trigger_word:
+                prompt = trigger_word + prompt
+                logger.info(f"  Added trigger: {trigger_word}")
+        generator = torch.Generator(device=device).manual_seed(seed)
+        image = pipe_txt2img(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            width=width,
+            height=height,
+            num_inference_steps=num_inference_steps,
+            true_cfg_scale=guidance_scale,
+            generator=generator
+        ).images[0]
+        # Выгружаем LoRA после генерации
+        if lora_name != "None":
+            pipe_txt2img.unload_lora_weights()
+        logger.info("  ✓ Generation completed")
+        return image, seed
+    except Exception as e:
+        logger.error(f"  ❌ Error: {e}")
+        raise
+@gpu_decorator(duration=180)
+def generate_img2img(
+    input_image,
+    prompt,
+    negative_prompt=" ",
+    strength=0.75,
+    seed=42,
+    randomize_seed=False,
+    guidance_scale=2.5,
+    num_inference_steps=40,
+    lora_name="None",
+    lora_scale=1.0,
+    scheduler_name=DEFAULT_SCHEDULER,
+    progress=gr.Progress(track_tqdm=True)
+):
+    """Image-to-Image генерация"""
+    logger.info("\n" + "=" * 60)
+    logger.info("IMAGE-TO-IMAGE GENERATION")
+    logger.info("=" * 60)
+    # Switch scheduler if needed
+    switch_scheduler(pipe_img2img, scheduler_name)
+    if input_image is None:
+        raise gr.Error("Please upload an input image")
+    if randomize_seed:
+        seed = random.randint(0, MAX_SEED)
+    # Изменяем размер изображения
+    resized = resize_image(input_image, max_size=1024)
+    logger.info(f"  Prompt: {prompt[:100]}...")
+    logger.info(f"  Input size: {input_image.size} → {resized.size}")
+    logger.info(f"  Strength: {strength}")
+    logger.info(f"  Steps: {num_inference_steps}, CFG: {guidance_scale}")
+    logger.info(f"  Scheduler: {scheduler_name}")
+    logger.info(f"  LoRA: {lora_name}")
+    try:
+        if pipe_img2img is None:
+            raise gr.Error("Image2Image pipeline not available")
+        # Загружаем LoRA если выбрана
+        trigger_word = None
+        if lora_name != "None":
+            trigger_word = load_lora_weights(pipe_img2img, lora_name, lora_scale, hf_token)
+            # Добавляем trigger word если есть
+            if trigger_word:
+                prompt = trigger_word + prompt
+        generator = torch.Generator(device=device).manual_seed(seed)
+        image = pipe_img2img(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=resized,
+            strength=strength,
+            num_inference_steps=num_inference_steps,
+            true_cfg_scale=guidance_scale,
+            generator=generator
+        ).images[0]
+        # Выгружаем LoRA
+        if lora_name != "None":
+            pipe_img2img.unload_lora_weights()
+        logger.info("  ✓ Generation completed")
+        return image, seed
+    except Exception as e:
+        logger.error(f"  ❌ Error: {e}")
+        raise
+# ControlNet функция убрана - не используется
+# =================================================================
+# GRADIO INTERFACE
+# =================================================================
+MAX_SEED = np.iinfo(np.int32).max
+css = """
+#col-container {
+    margin: 0 auto;
+    max-width: 1400px;
+}
+"""
+with gr.Blocks(css=css, theme=gr.themes.Soft()) as demo:
+    lora_choices = ["None"] + list(AVAILABLE_LORAS.keys())
+    gr.Markdown(f"""
+    # 🎨 Qwen Soloband - Image2Image + LoRA
+    **Продвинутая модель генерации** с поддержкой Text-to-Image, Image-to-Image и LoRA стилей.
+    ### ✨ Возможности:
+    - 🖼️ **Text-to-Image** - Генерация из текста, разрешения до 2048×2048
+    - 🔄 **Image-to-Image** - Модификация изображений с контролем strength (0.0-1.0)
+    - 🎭 **LoRA Support** - {len(AVAILABLE_LORAS)} доступных стилей (Hub + локальные)
+    - 🔌 **Full API** - Все функции доступны через API
+    - ⚡ **Optimized** - VAE tiling/slicing, правильный QwenImageImg2ImgPipeline
+    **Модель**: [Gerchegg/Qwen-Soloband-Diffusers](https://huggingface.co/Gerchegg/Qwen-Soloband-Diffusers)
+    💡 **Local LoRAs**: Положите .safetensors файлы в `/workspace/loras/` - они появятся автоматически!
+    """)
+    with gr.Tabs() as tabs:
+        # TAB 1: Text-to-Image
+        with gr.Tab("📝 Text-to-Image"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    t2i_prompt = gr.Text(
+                        label="Prompt",
+                        placeholder="SB_AI, a beautiful landscape...",
+                        lines=3
+                    )
+                    t2i_run = gr.Button("Generate", variant="primary")
+                    with gr.Accordion("Advanced Settings", open=False):
+                        t2i_negative = gr.Text(label="Negative Prompt", value="blurry, low quality")
+                        with gr.Row():
+                            t2i_width = gr.Slider(label="Width", minimum=512, maximum=2048, step=64, value=1664)
+                            t2i_height = gr.Slider(label="Height", minimum=512, maximum=2048, step=64, value=928)
+                        with gr.Row():
+                            t2i_steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=40)
+                            t2i_cfg = gr.Slider(label="CFG", minimum=0.0, maximum=7.5, step=0.1, value=2.5)
+                        with gr.Row():
+                            t2i_seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                            t2i_random_seed = gr.Checkbox(label="Random", value=True)
+                        t2i_scheduler = gr.Dropdown(
+                            label="Sampler/Scheduler",
+                            choices=SCHEDULER_CHOICES,
+                            value=DEFAULT_SCHEDULER,
+                            info="Алгоритм семплирования (поддерживает алиасы в API)"
+                        )
+                        t2i_lora = gr.Radio(
+                            label="LoRA Style",
+                            choices=lora_choices,
+                            value="None",
+                            info=f"Hub: {len(HUB_LORAS)}, Local: {len(LOCAL_LORAS)}"
+                        )
+                        t2i_lora_scale = gr.Slider(label="LoRA Strength", minimum=0.0, maximum=2.0, step=0.1, value=1.0)
+                with gr.Column(scale=1):
+                    t2i_output = gr.Image(label="Generated Image")
+                    t2i_seed_output = gr.Number(label="Used Seed")
+        # TAB 2: Image-to-Image
+        with gr.Tab("🔄 Image-to-Image"):
+            with gr.Row():
+                with gr.Column(scale=1):
+                    i2i_input = gr.Image(type="pil", label="Input Image")
+                    i2i_prompt = gr.Text(
+                        label="Prompt",
+                        placeholder="Transform this image into...",
+                        lines=3
+                    )
+                    i2i_strength = gr.Slider(
+                        label="Denoising Strength",
+                        info="0.0 = original image, 1.0 = complete redraw",
+                        minimum=0.0,
+                        maximum=1.0,
+                        step=0.05,
+                        value=0.75
+                    )
+                    i2i_run = gr.Button("Generate", variant="primary")
+                    with gr.Accordion("Advanced Settings", open=False):
+                        i2i_negative = gr.Text(label="Negative Prompt", value="blurry, low quality")
+                        with gr.Row():
+                            i2i_steps = gr.Slider(label="Steps", minimum=1, maximum=50, step=1, value=40)
+                            i2i_cfg = gr.Slider(label="CFG", minimum=0.0, maximum=7.5, step=0.1, value=2.5)
+                        with gr.Row():
+                            i2i_seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=0)
+                            i2i_random_seed = gr.Checkbox(label="Random", value=True)
+                        i2i_scheduler = gr.Dropdown(
+                            label="Sampler/Scheduler",
+                            choices=SCHEDULER_CHOICES,
+                            value=DEFAULT_SCHEDULER,
+                            info="Алгоритм семплирования (поддерживает алиасы в API)"
+                        )
+                        i2i_lora = gr.Radio(
+                            label="LoRA Style",
+                            choices=lora_choices,
+                            value="None",
+                            info=f"Hub: {len(HUB_LORAS)}, Local: {len(LOCAL_LORAS)}"
+                        )
+                        i2i_lora_scale = gr.Slider(label="LoRA Strength", minimum=0.0, maximum=2.0, step=0.1, value=1.0)
+                with gr.Column(scale=1):
+                    i2i_output = gr.Image(label="Generated Image")
+                    i2i_seed_output = gr.Number(label="Used Seed")
+    # Event handlers
+    t2i_run.click(
+        fn=generate_text2img,
+        inputs=[
+            t2i_prompt, t2i_negative, t2i_width, t2i_height,
+            t2i_seed, t2i_random_seed, t2i_cfg, t2i_steps,
+            t2i_lora, t2i_lora_scale, t2i_scheduler
+        ],
+        outputs=[t2i_output, t2i_seed_output],
+        api_name="text2img"
+    )
+    i2i_run.click(
+        fn=generate_img2img,
+        inputs=[
+            i2i_input, i2i_prompt, i2i_negative, i2i_strength,
+            i2i_seed, i2i_random_seed, i2i_cfg, i2i_steps,
+            i2i_lora, i2i_lora_scale, i2i_scheduler
+        ],
+        outputs=[i2i_output, i2i_seed_output],
+        api_name="img2img"
+    )
+if __name__ == "__main__":
+    demo.launch(
+        show_api=True,
+        share=False
+    )