Create app.py

app.py

@@ -0,0 +1,746 @@
+# ===== 必须首先导入spaces =====
+try:
+    import spaces
+    SPACES_AVAILABLE = True
+    print("✅ Spaces available - ZeroGPU mode")
+except ImportError:
+    SPACES_AVAILABLE = False
+    print("⚠️ Spaces not available - running in regular mode")
+
+# ===== 其他导入 =====
+import os
+import uuid
+from datetime import datetime
+import random
+import torch
+import gradio as gr
+from diffusers import StableDiffusionXLPipeline, EulerDiscreteScheduler
+from PIL import Image
+import traceback
+import numpy as np
+
+# ===== 长提示词处理 =====
+try:
+    from compel import Compel, ReturnedEmbeddingsType
+    COMPEL_AVAILABLE = True
+    print("✅ Compel available for long prompt processing")
+except ImportError:
+    COMPEL_AVAILABLE = False
+    print("⚠️ Compel not available - using standard prompt processing")
+
+# ===== 优化后的配置 =====
+STYLE_KEYWORDS = {
+    "None": {"prefix": "", "suffix": ""},
+    "Realistic": {
+        "prefix": "(RAW photo:1.3), (photorealistic:1.4), (hyperrealistic:1.3), 8k uhd, (ultra realistic skin texture:1.2), cinematic lighting, vibrant colors, masterpiece, realistic skin texture, detailed anatomy, professional photography",
+        "suffix": "sharp focus, (everything in focus:1.3), (no bokeh:1.2), realistic skin texture, subsurface scattering, detailed anatomy, (perfect anatomy:1.2), detailed face, detailed background, lifelike, professional photography, realistic proportions, (detailed face:1.1), natural pose, expressive eyes, 8k resolution"
+    },
+    "Anime": {
+        "prefix": "(anime style:1.3), (anime artwork:1.2), vibrant, key visual, studio anime, highly detailed anime",
+        "suffix": "cel shading, clean linework, vibrant anime colors, detailed anime eyes, smooth anime skin, perfect anime proportions, manga illustration"
+    },
+    "Comic": {
+        "prefix": "(comic book art:1.3), (graphic novel:1.2), bold inking, comic art style",
+        "suffix": "bold outlines, halftone dots, pop art colors, dynamic panel, graphic illustration, cel shading, comic book style"
+    },
+    "Watercolor": {
+        "prefix": "(watercolor painting:1.3), (watercolor art:1.2), soft edges, delicate washes, artistic",
+        "suffix": "soft gradients, pastel colors, paper texture, artistic brush strokes, traditional watercolor, hand-painted"
+    }
+}
+
+QUALITY_TAGS = "masterpiece, best quality, high resolution, detailed"
+
+# 🔧 修正：使用正确的模型名称
+FIXED_MODEL = "votepurchase/pornmasterPro_noobV3VAE"
+
+# 🔧 修正：使用正确的 LoRA 配置
+LORA_CONFIGS = [
+    {
+        "repo_id": "OedoSoldier/detail-tweaker-lora",
+        "weight_name": "add_detail.safetensors",
+        "adapter_name": "detail_tweaker",
+        "scale": 0.8
+    }
+]
+
+SAVE_DIR = "generated_images"
+os.makedirs(SAVE_DIR, exist_ok=True)
+
+# ===== 模型相关变量 =====
+pipeline = None
+compel_processor = None
+device = None
+model_loaded = False
+
+def initialize_model():
+    """优化的模型初始化 - 修复设备不一致问题"""
+    global pipeline, compel_processor, device, model_loaded
+    
+    if model_loaded and pipeline is not None:
+        print("✅ Model already loaded, skipping initialization")
+        return True
+    
+    try:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"🖥️ Using device: {device}")
+        
+        print(f"📦 Loading model: {FIXED_MODEL}")
+        
+        # 基础模型加载
+        pipeline = StableDiffusionXLPipeline.from_pretrained(
+            FIXED_MODEL,
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+            variant="fp16" if torch.cuda.is_available() else None,
+            use_safetensors=True,
+            safety_checker=None,
+            requires_safety_checker=False
+        )
+        
+        # 优化调度器
+        pipeline.scheduler = EulerDiscreteScheduler.from_config(
+            pipeline.scheduler.config,
+            timestep_spacing="trailing"
+        )
+        
+        # 先移到设备
+        pipeline = pipeline.to(device)
+        
+        # 加载 LoRA
+        print("🎨 Loading LoRA models...")
+        adapter_names = []
+        adapter_scales = []
+        
+        for lora_config in LORA_CONFIGS:
+            try:
+                print(f"   Loading: {lora_config['repo_id']}/{lora_config['weight_name']}")
+                pipeline.load_lora_weights(
+                    lora_config["repo_id"],
+                    weight_name=lora_config["weight_name"],
+                    adapter_name=lora_config["adapter_name"]
+                )
+                
+                # 🔧 关键修复: 确保 LoRA 权重在正确的设备上
+                if hasattr(pipeline, 'unet') and hasattr(pipeline.unet, 'to'):
+                    pipeline.unet.to(device)
+                
+                adapter_names.append(lora_config["adapter_name"])
+                adapter_scales.append(lora_config.get("scale", 0.8))
+                print(f"   ✅ LoRA loaded: {lora_config['adapter_name']} (scale: {lora_config.get('scale', 0.8)})")
+            except Exception as lora_error:
+                print(f"   ⚠️ Failed to load LoRA {lora_config['adapter_name']}: {lora_error}")
+                print(traceback.format_exc())
+        
+        # 设置 LoRA 强度
+        if adapter_names:
+            try:
+                pipeline.set_adapters(adapter_names, adapter_weights=adapter_scales)
+                print(f"✅ LoRA adapters activated with scales: {adapter_scales}")
+                
+                # 🔧 再次确保所有组件在同一设备
+                pipeline.to(device)
+            except Exception as e:
+                print(f"⚠️ Failed to set adapter scales: {e}")
+        
+        # GPU优化
+        if torch.cuda.is_available():
+            try:
+                pipeline.enable_vae_slicing()
+                pipeline.enable_vae_tiling()
+                
+                try:
+                    pipeline.enable_xformers_memory_efficient_attention()
+                    print("✅ xFormers enabled")
+                except:
+                    print("⚠️ xFormers not available, using default attention")
+                
+                print("ℹ️ Skipping torch.compile for ZeroGPU compatibility")
+                
+            except Exception as opt_error:
+                print(f"⚠️ Optimization warning: {opt_error}")
+        
+        # 初始化Compel
+        if COMPEL_AVAILABLE:
+            try:
+                compel_processor = Compel(
+                    tokenizer=[pipeline.tokenizer, pipeline.tokenizer_2],
+                    text_encoder=[pipeline.text_encoder, pipeline.text_encoder_2],
+                    returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
+                    requires_pooled=[False, True],
+                    truncate_long_prompts=False
+                )
+                print("✅ Compel processor initialized")
+            except Exception as compel_error:
+                print(f"⚠️ Compel initialization failed: {compel_error}")
+                compel_processor = None
+        
+        # 🔧 最终设备检查
+        print(f"🔍 Final device check:")
+        print(f"   - UNet device: {next(pipeline.unet.parameters()).device}")
+        print(f"   - VAE device: {next(pipeline.vae.parameters()).device}")
+        print(f"   - Text Encoder device: {next(pipeline.text_encoder.parameters()).device}")
+        
+        model_loaded = True
+        print("✅ Model initialization complete")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Model loading error: {e}")
+        print(traceback.format_exc())
+        model_loaded = False
+        return False
+
+def enhance_prompt(prompt: str, style: str) -> str:
+    """优化的提示词增强"""
+    if not prompt or prompt.strip() == "":
+        return ""
+    
+    style_config = STYLE_KEYWORDS.get(style, STYLE_KEYWORDS["None"])
+    parts = []
+    
+    if style_config["prefix"]:
+        parts.append(style_config["prefix"])
+    
+    parts.append(prompt.strip())
+    
+    if style_config["suffix"]:
+        parts.append(style_config["suffix"])
+    
+    parts.append(QUALITY_TAGS)
+    
+    enhanced = ", ".join(parts)
+    
+    print(f"\n🎨 Style: {style}")
+    print(f"📝 User prompt: {prompt[:100]}...")
+    print(f"✨ Enhanced: {enhanced[:200]}...\n")
+    
+    return enhanced
+
+def build_negative_prompt(style: str, custom_negative: str = "") -> str:
+    """根据风格构建负面提示词"""
+    base_negative = "(low quality:1.4), (worst quality:1.4), (bad anatomy:1.3), (bad hands:1.2), blurry, watermark, text, error, cropped, jpeg artifacts, ugly, duplicate, deformed"
+    
+    style_negatives = {
+        "Realistic": ", (cartoon:1.3), (anime:1.3), (3d render:1.2), (illustration:1.2), (painting:1.2), (drawing:1.2), (art:1.2), (sketch:1.2), artificial, unrealistic",
+        "Anime": ", (realistic:1.3), (photorealistic:1.3), (photo:1.2), (3d:1.2), (hyperrealistic:1.2)",
+        "Comic": ", (realistic:1.2), (photorealistic:1.2), (blurry lines:1.2), (soft edges:1.2)",
+        "Watercolor": ", (digital art:1.2), (sharp edges:1.2), (vector art:1.2), (3d:1.2)"
+    }
+    
+    negative = base_negative
+    if style in style_negatives:
+        negative += style_negatives[style]
+    
+    if custom_negative.strip():
+        negative += f", {custom_negative.strip()}"
+    
+    return negative
+
+def process_with_compel(prompt, negative_prompt):
+    """使用Compel处理长提示词"""
+    if not compel_processor:
+        return None, None
+    
+    try:
+        conditioning, pooled = compel_processor([prompt, negative_prompt])
+        print("✅ Long prompt processed with Compel")
+        return conditioning, pooled
+    except Exception as e:
+        print(f"⚠️ Compel processing failed: {e}")
+        return None, None
+
+def apply_spaces_decorator(func):
+    """应用spaces装饰器"""
+    if SPACES_AVAILABLE:
+        return spaces.GPU(duration=45)(func)
+    return func
+
+def create_metadata_content(prompt, enhanced_prompt, seed, steps, cfg_scale, width, height, style):
+    """创建元数据"""
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    lora_info = ", ".join([f"{lora['adapter_name']}({lora.get('scale', 1.0)})" for lora in LORA_CONFIGS])
+    
+    return f"""Generated Image Metadata
+======================
+Timestamp: {timestamp}
+Original Prompt: {prompt}
+Enhanced Prompt: {enhanced_prompt}
+Seed: {seed}
+Steps: {steps}
+CFG Scale: {cfg_scale}
+Dimensions: {width}x{height}
+Style: {style}
+LoRA: {lora_info}
+"""
+
+def cleanup_pipeline():
+    """清理 pipeline 状态"""
+    global pipeline
+    
+    if pipeline is None:
+        return
+    
+    try:
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+        
+        if hasattr(pipeline, 'unet'):
+            if hasattr(pipeline.unet, 'set_attn_processor'):
+                try:
+                    from diffusers.models.attention_processor import AttnProcessor
+                    pipeline.unet.set_attn_processor(AttnProcessor())
+                except:
+                    pass
+        
+        if hasattr(pipeline, 'vae'):
+            pipeline.vae.to('cpu')
+            pipeline.vae.to(device)
+        
+        print("🧹 Pipeline cleaned")
+        
+    except Exception as e:
+        print(f"⚠️ Cleanup warning: {e}")
+
+@apply_spaces_decorator
+def generate_image(prompt: str, style: str, negative_prompt: str = "",
+                   steps: int = 25, cfg_scale: float = 7.0,
+                   seed: int = -1, width: int = 1024, height: int = 1024,
+                   progress=gr.Progress()):
+    """图像生成主函数"""
+    
+    if not prompt or prompt.strip() == "":
+        return None, "", "❌ Please enter a prompt"
+    
+    progress(0.05, desc="Initializing...")
+    
+    if not initialize_model():
+        return None, "", "❌ Failed to load model"
+    
+    cleanup_pipeline()
+    
+    progress(0.1, desc="Processing prompt...")
+    
+    try:
+        if seed == -1:
+            seed = random.randint(0, np.iinfo(np.int32).max)
+        
+        generator = torch.Generator(device).manual_seed(seed)
+        
+        enhanced_prompt = enhance_prompt(prompt, style)
+        final_negative = build_negative_prompt(style, negative_prompt)
+        
+        print(f"🔧 Generation params: seed={seed}, steps={steps}, cfg={cfg_scale}, size={width}x{height}")
+        print(f"📝 Prompt preview: {enhanced_prompt[:100]}...")
+        
+        progress(0.2, desc="Generating image...")
+        
+        prompt_length = len(enhanced_prompt.split())
+        use_compel = prompt_length > 50 and compel_processor is not None
+        
+        if use_compel:
+            print(f"📏 Long prompt detected ({prompt_length} words), using Compel")
+            conditioning, pooled = process_with_compel(enhanced_prompt, final_negative)
+            
+            if conditioning is not None:
+                result = pipeline(
+                    prompt_embeds=conditioning[0:1],
+                    pooled_prompt_embeds=pooled[0:1],
+                    negative_prompt_embeds=conditioning[1:2],
+                    negative_pooled_prompt_embeds=pooled[1:2],
+                    num_inference_steps=steps,
+                    guidance_scale=cfg_scale,
+                    width=width,
+                    height=height,
+                    generator=generator,
+                    output_type="pil"
+                ).images[0]
+            else:
+                print("⚠️ Falling back to standard generation")
+                result = pipeline(
+                    prompt=enhanced_prompt,
+                    negative_prompt=final_negative,
+                    num_inference_steps=steps,
+                    guidance_scale=cfg_scale,
+                    width=width,
+                    height=height,
+                    generator=generator,
+                    output_type="pil"
+                ).images[0]
+        else:
+            print(f"📝 Standard generation ({prompt_length} words)")
+            result = pipeline(
+                prompt=enhanced_prompt,
+                negative_prompt=final_negative,
+                num_inference_steps=steps,
+                guidance_scale=cfg_scale,
+                width=width,
+                height=height,
+                generator=generator,
+                output_type="pil"
+            ).images[0]
+        
+        progress(0.95, desc="Finalizing...")
+        
+        if not isinstance(result, Image.Image):
+            if isinstance(result, np.ndarray):
+                if result.dtype != np.uint8:
+                    result = (result * 255).astype(np.uint8)
+                result = Image.fromarray(result)
+        
+        metadata = create_metadata_content(
+            prompt, enhanced_prompt, seed, steps, cfg_scale,
+            width, height, style
+        )
+        
+        generation_info = f"Style: {style} | Seed: {seed} | Size: {width}×{height} | Steps: {steps} | CFG: {cfg_scale}"
+        
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        
+        progress(1.0, desc="Complete!")
+        print("✅ Generation successful\n")
+        
+        return result, generation_info, metadata
+        
+    except Exception as e:
+        error_msg = str(e)
+        print(f"❌ Generation error: {error_msg}")
+        print(traceback.format_exc())
+        
+        try:
+            cleanup_pipeline()
+        except:
+            pass
+        
+        return None, "", f"❌ Generation failed: {error_msg}"
+
+# ===== CSS样式 =====
+css = """
+.gradio-container {
+    max-width: 100% !important;
+    margin: 0 !important;
+    padding: 0 !important;
+    background: linear-gradient(135deg, #e6a4f2 0%, #1197e4 100%) !important;
+    min-height: 100vh !important;
+    font-family: 'Segoe UI', Arial, sans-serif !important;
+}
+
+.main-content {
+    background: rgba(255, 255, 255, 0.9) !important;
+    border-radius: 20px !important;
+    padding: 20px !important;
+    margin: 15px !important;
+    box-shadow: 0 10px 25px rgba(255, 255, 255, 0.2) !important;
+    min-height: calc(100vh - 30px) !important;
+    color: #3e3e3e !important;
+    backdrop-filter: blur(10px) !important;
+}
+
+.title {
+    text-align: center !important;
+    background: linear-gradient(45deg, #bb6ded, #08676b) !important;
+    -webkit-background-clip: text !important;
+    -webkit-text-fill-color: transparent !important;
+    background-clip: text !important;
+    font-size: 2rem !important;
+    margin-bottom: 15px !important;
+    font-weight: bold !important;
+}
+
+.warning-box {
+    background: linear-gradient(45deg, #bb6ded, #08676b) !important;
+    color: white !important;
+    padding: 8px !important;
+    border-radius: 8px !important;
+    margin-bottom: 15px !important;
+    text-align: center !important;
+    font-weight: bold !important;
+    font-size: 14px !important;
+}
+
+.prompt-box textarea, .prompt-box input {
+    border-radius: 10px !important;
+    border: 2px solid #bb6ded !important;
+    padding: 15px !important;
+    font-size: 18px !important;
+    background: linear-gradient(135deg, rgba(245, 243, 255, 0.9), rgba(237, 233, 254, 0.9)) !important;
+    color: #2d2d2d !important;
+}
+
+.prompt-box textarea:focus, .prompt-box input:focus {
+    border-color: #08676b !important;
+    box-shadow: 0 0 15px rgba(77, 8, 161, 0.3) !important;
+    background: linear-gradient(135deg, rgba(255, 255, 255, 0.95), rgba(248, 249, 250, 0.95)) !important;
+}
+
+.controls-section {
+    background: linear-gradient(135deg, rgba(224, 218, 255, 0.8), rgba(196, 181, 253, 0.8)) !important;
+    border-radius: 12px !important;
+    padding: 15px !important;
+    margin-bottom: 8px !important;
+    border: 2px solid rgba(187, 109, 237, 0.3) !important;
+    backdrop-filter: blur(5px) !important;
+}
+
+.controls-section label {
+    font-weight: 600 !important;
+    color: #2d2d2d !important;
+    margin-bottom: 8px !important;
+}
+
+.controls-section input[type="radio"] {
+    accent-color: #bb6ded !important;
+}
+
+.controls-section input[type="number"],
+.controls-section input[type="range"] {
+    background: rgba(255, 255, 255, 0.9) !important;
+    border: 1px solid #bb6ded !important;
+    border-radius: 6px !important;
+    padding: 8px !important;
+    color: #2d2d2d !important;
+}
+
+.generate-btn {
+    background: linear-gradient(45deg, #bb6ded, #08676b) !important;
+    color: white !important;
+    border: none !important;
+    padding: 15px 25px !important;
+    border-radius: 25px !important;
+    font-size: 16px !important;
+    font-weight: bold !important;
+    width: 100% !important;
+    cursor: pointer !important;
+    transition: all 0.3s ease !important;
+    text-transform: uppercase !important;
+    letter-spacing: 1px !important;
+}
+
+.generate-btn:hover {
+    transform: translateY(-2px) !important;
+    box-shadow: 0 8px 25px rgba(187, 109, 237, 0.5) !important;
+}
+
+.image-output {
+    border-radius: 15px !important;
+    overflow: hidden !important;
+    max-width: 100% !important;
+    max-height: 70vh !important;
+    border: 3px solid #08676b !important;
+    box-shadow: 0 8px 20px rgba(0,0,0,0.15) !important;
+    background: linear-gradient(135deg, rgba(255, 255, 255, 0.9), rgba(248, 249, 250, 0.9)) !important;
+}
+
+.image-info {
+    background: linear-gradient(135deg, rgba(248, 249, 250, 0.2), rgba(233, 236, 239, 0.9)) !important;
+    border-radius: 8px !important;
+    padding: 12px !important;
+    margin-top: 10px !important;
+    font-size: 12px !important;
+    color: #495057 !important;
+    border: 2px solid rgba(187, 109, 237, 0.2) !important;
+    backdrop-filter: blur(5px) !important;
+}
+
+.metadata-box {
+    background: linear-gradient(135deg, rgba(248, 249, 250, 0.2), rgba(233, 236, 239, 0.9)) !important;
+    border-radius: 8px !important;
+    padding: 15px !important;
+    margin-top: 15px !important;
+    font-family: 'Courier New', monospace !important;
+    font-size: 12px !important;
+    color: #495057 !important;
+    border: 2px solid rgba(187, 109, 237, 0.2) !important;
+    backdrop-filter: blur(5px) !important;
+    white-space: pre-wrap !important;
+    overflow-y: auto !important;
+    max-height: 300px !important;
+}
+
+@media (max-width: 768px) {
+    .main-content {
+        margin: 10px !important;
+        padding: 15px !important;
+    }
+    .title {
+        font-size: 1.5rem !important;
+    }
+}
+"""
+
+# ===== 创建UI =====
+def create_interface():
+    with gr.Blocks(css=css, title="Adult NSFW AI Image Generator") as interface:
+        with gr.Column(elem_classes=["main-content"]):
+            gr.HTML('<div class="title">Adult NSFW AI Image Generator</div>')
+            gr.HTML('<div class="warning-box">⚠️ 18+ CONTENT WARNING ⚠️</div>')
+            
+            with gr.Row():
+                with gr.Column(scale=2):
+                    prompt_input = gr.Textbox(
+                        label="Detailed Prompt",
+                        placeholder="Enter your detailed prompt here...",
+                        lines=15,
+                        elem_classes=["prompt-box"]
+                    )
+                    
+                    negative_prompt_input = gr.Textbox(
+                        label="Negative Prompt (Optional)",
+                        placeholder="Additional things you don't want...",
+                        lines=4,
+                        elem_classes=["prompt-box"]
+                    )
+                
+                with gr.Column(scale=1):
+                    with gr.Group(elem_classes=["controls-section"]):
+                        style_input = gr.Radio(
+                            label="Style Preset",
+                            choices=list(STYLE_KEYWORDS.keys()),
+                            value="Realistic"
+                        )
+                    
+                    with gr.Group(elem_classes=["controls-section"]):
+                        seed_input = gr.Number(
+                            label="Seed (-1 for random)",
+                            value=-1,
+                            precision=0
+                        )
+                    
+                    with gr.Group(elem_classes=["controls-section"]):
+                        width_input = gr.Slider(
+                            label="Width",
+                            minimum=512,
+                            maximum=2048,
+                            value=1024,
+                            step=64
+                        )
+                    
+                    with gr.Group(elem_classes=["controls-section"]):
+                        height_input = gr.Slider(
+                            label="Height",
+                            minimum=512,
+                            maximum=2048,
+                            value=1024,
+                            step=64
+                        )
+                    
+                    with gr.Group(elem_classes=["controls-section"]):
+                        steps_input = gr.Slider(
+                            label="Steps",
+                            minimum=10,
+                            maximum=50,
+                            value=25,
+                            step=1
+                        )
+                        
+                        cfg_input = gr.Slider(
+                            label="CFG Scale",
+                            minimum=1.0,
+                            maximum=15.0,
+                            value=7.0,
+                            step=0.1
+                        )
+                    
+                    generate_button = gr.Button(
+                        "GENERATE",
+                        elem_classes=["generate-btn"],
+                        variant="primary"
+                    )
+            
+            image_output = gr.Image(
+                label="Generated Image",
+                elem_classes=["image-output"],
+                show_label=False,
+                container=True
+            )
+            
+            with gr.Row():
+                generation_info = gr.Textbox(
+                    label="Generation Info",
+                    interactive=False,
+                    elem_classes=["image-info"],
+                    show_label=True,
+                    visible=False
+                )
+            
+            with gr.Row():
+                metadata_display = gr.Textbox(
+                    label="Image Metadata",
+                    interactive=True,
+                    elem_classes=["metadata-box"],
+                    show_label=True,
+                    lines=15,
+                    visible=False
+                )
+            
+            def on_generate(prompt, style, neg_prompt, steps, cfg, seed, width, height):
+                image, info, metadata = generate_image(
+                    prompt, style, neg_prompt, steps, cfg, seed, width, height
+                )
+                
+                if image is not None:
+                    return (
+                        image,
+                        info,
+                        metadata,
+                        gr.update(visible=True, value=info),
+                        gr.update(visible=True, value=metadata)
+                    )
+                else:
+                    return (
+                        None,
+                        info,
+                        "",
+                        gr.update(visible=False),
+                        gr.update(visible=False)
+                    )
+            
+            generate_button.click(
+                fn=on_generate,
+                inputs=[
+                    prompt_input, style_input, negative_prompt_input,
+                    steps_input, cfg_input, seed_input, width_input, height_input
+                ],
+                outputs=[
+                    image_output, generation_info, metadata_display,
+                    generation_info, metadata_display
+                ],
+                show_progress=True
+            )
+            
+            prompt_input.submit(
+                fn=on_generate,
+                inputs=[
+                    prompt_input, style_input, negative_prompt_input,
+                    steps_input, cfg_input, seed_input, width_input, height_input
+                ],
+                outputs=[
+                    image_output, generation_info, metadata_display,
+                    generation_info, metadata_display
+                ],
+                show_progress=True
+            )
+    
+    return interface
+
+# ===== 启动应用 =====
+if __name__ == "__main__":
+    print("\n" + "="*50)
+    print("🚀 Starting NSFW Image Generator")
+    print("="*50)
+    print(f"📦 Model: {FIXED_MODEL}")
+    print(f"🖥️ Device: {'CUDA' if torch.cuda.is_available() else 'CPU'}")
+    print(f"⚡ ZeroGPU: {'Enabled' if SPACES_AVAILABLE else 'Disabled'}")
+    print(f"📝 Compel: {'Available' if COMPEL_AVAILABLE else 'Not Available'}")
+    print(f"🎨 LoRA: detail-tweaker-lora (scale: {LORA_CONFIGS[0].get('scale', 0.8)})")
+    print("="*50 + "\n")
+    
+    app = create_interface()
+    app.queue(max_size=10, default_concurrency_limit=2)
+    
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False
+    )