minenotpublic

.gitattributes

@@ -1,35 +1,35 @@
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-*.arrow filter=lfs diff=lfs merge=lfs -text
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-*.bz2 filter=lfs diff=lfs merge=lfs -text
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-*tfevents* filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
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+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.pb filter=lfs diff=lfs merge=lfs -text
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+*.pkl filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,14 @@
----
-title: Minenotpublic
-emoji: 🏃
-colorFrom: red
-colorTo: red
-sdk: gradio
-sdk_version: 6.14.0
-python_version: '3.12'
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: ADULT.NSFW.AI.Anime&Comic
+emoji: 🚀
+colorFrom: purple
+colorTo: red
+sdk: gradio
+sdk_version: 6.2.0
+app_file: app.py
+pinned: false
+short_description: Adult AI images,anima & comic,non-realistic
+tag: not-for-all-audiences
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,982 @@
+import sys
+
+if hasattr(sys.stdout, "reconfigure"):
+    sys.stdout.reconfigure(encoding="utf-8")
+if hasattr(sys.stderr, "reconfigure"):
+    sys.stderr.reconfigure(encoding="utf-8")
+
+# ===== 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 socket
+import uuid
+import importlib.util
+from datetime import datetime
+import random
+import torch
+import gradio as gr
+from diffusers import StableDiffusionPipeline, StableDiffusionXLPipeline, EulerDiscreteScheduler
+from PIL import Image
+import traceback
+import numpy as np
+
+if hasattr(sys.stdout, "reconfigure"):
+    sys.stdout.reconfigure(encoding="utf-8")
+if hasattr(sys.stderr, "reconfigure"):
+    sys.stderr.reconfigure(encoding="utf-8")
+
+# ===== 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 =====
+# Kageillustrious风格核心关键词 - 使用Danbooru标签风格
+STYLE_KEYWORDS = {
+    "None": {
+        "prefix": "",
+        "suffix": ""
+    },
+    "Standard Quality": {
+        "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"
+    },
+    "High Detail": {
+        "prefix": "masterpiece, best quality, amazing quality, very aesthetic, high resolution, ultra-detailed, absurdres, newest, colorful, rim light, backlit, highest detailed",
+        "suffix": ""
+    },
+    "Realistic": {
+        "prefix": "masterpiece, best quality, amazing quality, very aesthetic, absurdres, (photorealistic:1.3), (realistic:1.4), detailed skin texture, cinematic lighting",
+        "suffix": "sharp focus, detailed anatomy, realistic proportions, detailed face, natural pose, expressive eyes, 8k resolution"
+    },
+    "Anime": {
+        "prefix": "masterpiece, best quality, amazing quality, very aesthetic, absurdres, anime style, vibrant colors, detailed anime",
+        "suffix": "cel shading, clean linework, vibrant anime colors, detailed anime eyes, smooth anime skin"
+    },
+    "Artistic": {
+        "prefix": "masterpiece, best quality, amazing quality, very aesthetic, absurdres, artistic, illustration, detailed artwork",
+        "suffix": "vibrant colors, expressive, detailed composition, artistic rendering"
+    }
+}
+
+# 通用质量增强词
+QUALITY_TAGS = "very awa, masterpiece, best quality, high resolution, highly detailed, professional"
+
+# 本地模型目录 - 只使用本机或挂载盘中的 safetensors
+LOCAL_MODEL_DIRECTORY = os.environ.get(
+    "LOCAL_SD_MODEL_DIR",
+    r"G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion"
+)
+SUPPORTED_MODEL_EXTENSIONS = [".safetensors", ".ckpt"]
+
+RANDOM_PROMPTS = [
+    "1girl, fantasy city at night, neon lights, detailed eyes, cinematic lighting",
+    "1boy, solo, forest clearing, soft lighting, highly detailed, realistic skin",
+    "couple, cozy bedroom, warm atmosphere, expressive eyes, perfect anatomy",
+    "anime style, elegant outfit, flowing hair, dynamic pose, dramatic lighting",
+    "portrait, close-up face, sharp focus, beautiful makeup, shiny hair"
+]
+
+def get_random_prompt():
+    return random.choice(RANDOM_PROMPTS)
+
+current_model_name = None
+current_model_path = None
+
+
+def get_server_port(default_port: int = 7860) -> int:
+    try:
+        requested_port = int(os.environ.get("GRADIO_SERVER_PORT", default_port))
+    except ValueError:
+        requested_port = default_port
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as sock:
+        sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+        try:
+            sock.bind(("0.0.0.0", requested_port))
+            return requested_port
+        except OSError:
+            sock.bind(("0.0.0.0", 0))
+            return sock.getsockname()[1]
+
+# 本地模型扫描
+
+def get_local_models():
+    if not os.path.isdir(LOCAL_MODEL_DIRECTORY):
+        return []
+    valid_files = []
+    for filename in os.listdir(LOCAL_MODEL_DIRECTORY):
+        extension = os.path.splitext(filename)[1].lower()
+        if extension not in SUPPORTED_MODEL_EXTENSIONS:
+            continue
+        file_path = os.path.join(LOCAL_MODEL_DIRECTORY, filename)
+        try:
+            if os.path.getsize(file_path) < 20 * 1024 * 1024:
+                continue
+        except OSError:
+            continue
+        valid_files.append(filename)
+    return sorted(valid_files)
+
+LOCAL_MODEL_CHOICES = get_local_models()
+
+# LoRA 配置 - 保留原有的LoRA(可能需要测试兼容性)
+LORA_CONFIGS = [
+    {
+        "repo_id": "artificialguybr/LogoRedmond-LogoLoraForSDXL-V2",
+        "weight_name": "LogoRedAF.safetensors",
+        "adapter_name": "logo_lora",
+        "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(model_filename: str):
+    """优化的模型初始化 - 从本地 safetensors 文件加载模型"""
+    global pipeline, compel_processor, device, model_loaded, current_model_name, current_model_path
+
+    if not model_filename:
+        print("❌ No model selected")
+        return False
+
+    model_path = os.path.join(LOCAL_MODEL_DIRECTORY, model_filename)
+    if not os.path.isfile(model_path):
+        print(f"❌ Model file not found: {model_path}")
+        return False
+
+    if model_loaded and pipeline is not None and model_filename == current_model_name:
+        print(f"✅ Model already loaded: {current_model_name}")
+        return True
+
+    if pipeline is not None and model_filename != current_model_name:
+        cleanup_pipeline()
+        pipeline = None
+        model_loaded = False
+
+    try:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"🖥️ Using device: {device}")
+        print(f"📦 Loading local model from: {model_path}")
+
+        tried_low_mem = False
+        use_accelerate = importlib.util.find_spec('accelerate') is not None
+        low_mem_kwargs = {
+            'torch_dtype': torch.float16 if torch.cuda.is_available() else torch.float32,
+            'safety_checker': None,
+            'requires_safety_checker': False
+        }
+        if use_accelerate:
+            low_mem_kwargs.update({
+                'device_map': 'auto',
+                'offload_folder': 'offload',
+                'low_cpu_mem_usage': True
+            })
+
+        try:
+            pipeline = StableDiffusionXLPipeline.from_single_file(
+                model_path,
+                torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                use_safetensors=True,
+                safety_checker=None,
+                requires_safety_checker=False
+            )
+            print("✅ Loaded model as SDXL pipeline")
+        except Exception as xlp_error:
+            print(f"⚠️ SDXL load failed: {xlp_error}")
+            # Try a low-memory loading strategy if available
+            try:
+                print("ℹ️ Attempting low-memory load (may use device mapping / lower precision)...")
+                tried_low_mem = True
+                pipeline = StableDiffusionXLPipeline.from_single_file(
+                    model_path,
+                    use_safetensors=True,
+                    **low_mem_kwargs
+                )
+                print("✅ Loaded SDXL pipeline with low-memory options")
+            except Exception as lowmem_err:
+                print(f"⚠️ Low-memory SDXL load failed: {lowmem_err}")
+                try:
+                    print("ℹ️ Falling back to standard Stable Diffusion loader")
+                    pipeline = StableDiffusionPipeline.from_single_file(
+                        model_path,
+                        torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+                        safety_checker=None,
+                        requires_safety_checker=False
+                    )
+                    print("✅ Loaded model as standard Stable Diffusion pipeline")
+                except Exception as sd_err:
+                    print(f"⚠️ Standard SD load failed: {sd_err}")
+                    # Try low-memory for standard pipeline
+                    try:
+                        if not tried_low_mem:
+                            pipeline = StableDiffusionPipeline.from_single_file(
+                                model_path,
+                                **low_mem_kwargs
+                            )
+                            print("✅ Loaded standard pipeline with low-memory options")
+                    except Exception as final_err:
+                        print(f"❌ Model loading error: {final_err}")
+                        # Detect common low-memory / paging-file errors and provide guidance
+                        err_msg = str(final_err).lower()
+                        if "paging file" in err_msg or "memoryerror" in err_msg or "out of memory" in err_msg:
+                            print("❗ Model is too large to load on the current machine (CPU memory / paging file insufficient).")
+                            print("Suggestions: 1) Use a GPU with more VRAM; 2) Increase Windows virtual memory (page file); 3) Use a smaller model (.safetensors/.ckpt); 4) Run with `low_cpu_mem_usage=True` or enable device mapping via accelerate.)")
+                        return False
+
+        if hasattr(pipeline, 'scheduler'):
+            pipeline.scheduler = EulerDiscreteScheduler.from_config(
+                pipeline.scheduler.config,
+                timestep_spacing="trailing"
+            )
+
+        if pipeline is None:
+            print("❌ No pipeline object created, aborting model initialization")
+            return False
+
+        pipeline = pipeline.to(device)
+
+        # 加载 LoRA
+        print("🎨 Loading LoRA models...")
+        adapter_names = []
+        adapter_scales = []
+
+        for lora_config in LORA_CONFIGS:
+            try:
+                pipeline.load_lora_weights(
+                    lora_config["repo_id"],
+                    weight_name=lora_config["weight_name"],
+                    adapter_name=lora_config["adapter_name"]
+                )
+                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}")
+
+        if adapter_names:
+            try:
+                pipeline.set_adapters(adapter_names, adapter_weights=adapter_scales)
+                print(f"✅ LoRA adapters activated with scales: {adapter_scales}")
+            except Exception as e:
+                print(f"⚠️ Failed to set adapter scales: {e}")
+
+        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 Exception:
+                    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}")
+
+        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
+
+        current_model_name = model_filename
+        current_model_path = model_path
+        model_loaded = True
+        print(f"✅ Local model initialization complete: {model_filename}")
+        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 - 适配Kageillustrious的Danbooru标签风格"""
+    if not prompt or prompt.strip() == "":
+        return ""
+    
+    # 获取风格关键词
+    style_config = STYLE_KEYWORDS.get(style, STYLE_KEYWORDS["None"])
+    
+    # 组合顺序:风格前缀 → 用户提示词 → 风格后缀 → 质量标签
+    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 - 适配Illustrious系列"""
+    # Illustrious系列推荐的负面提示词
+    base_negative = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"
+    
+    # 风格特定的负面词
+    style_negatives = {
+        "Standard Quality": ", (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, (depth of field:1.2), (bokeh:1.2)",
+        "Realistic": ", (cartoon:1.3), (anime:1.3), (3d render:1.2), (illustration:1.2)",
+        "Anime": ", (realistic:1.3), (photorealistic:1.3), (photo:1.2)",
+        "Artistic": ", (photo:1.2), (photorealistic:1.2)"
+    }
+    
+    negative = base_negative
+    if style in style_negatives:
+        negative += style_negatives[style]
+    
+    # 添加用户自定义负面词
+    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:
+        # Compel会自动处理超过77 tokens的提示词
+        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")
+    
+    # 获取 LoRA 信息
+    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}
+Seed: {seed}
+Steps: {steps}
+CFG Scale: {cfg_scale}
+Dimensions: {width}x{height}
+Style: {style}
+"""
+
+def cleanup_pipeline():
+    """清理 pipeline 状态，防止污染"""
+    global pipeline
+    
+    if pipeline is None:
+        return
+    
+    try:
+        # 清理 CUDA 缓存
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+        
+        # 清理 pipeline 的内部缓存
+        if hasattr(pipeline, 'unet'):
+            # 清空 UNet 的注意力缓存
+            if hasattr(pipeline.unet, 'set_attn_processor'):
+                try:
+                    from diffusers.models.attention_processor import AttnProcessor
+                    pipeline.unet.set_attn_processor(AttnProcessor())
+                except:
+                    pass
+        
+        # 清理 VAE 缓存
+        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 = 20, cfg_scale: float = 6.0,
+                   seed: int = -1, width: int = 896, height: int = 1152,
+                   model_name: str = None, num_images: int = 1,
+                   progress=gr.Progress()):
+    """图像生成主函数 - 使用选择的本地模型进行生成"""
+    
+    # 验证输入
+    if not prompt or prompt.strip() == "":
+        return None, "", "❌ Please enter a prompt"
+    if not model_name:
+        return None, "", "❌ Please select a local model"
+    
+    progress(0.05, desc="Initializing...")
+    
+    # 初始化模型
+    if not initialize_model(model_name):
+        return None, "", "❌ Failed to load selected model"
+    
+    # 清理之前的状态
+    cleanup_pipeline()
+    
+    progress(0.1, desc="Processing prompt...")
+    
+    try:
+        # prepare seeds for each image
+        if seed == -1:
+            seeds = [random.randint(0, np.iinfo(np.int32).max) for _ in range(max(1, num_images))]
+        else:
+            seeds = [int(seed) + i for i in range(max(1, num_images))]
+
+        # 增强提示词
+        enhanced_prompt = enhance_prompt(prompt, style)
+
+        # 构建负面提示词
+        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 images...")
+        
+        # 检查提示词长度并决定是否使用Compel
+        prompt_length = len(enhanced_prompt.split())
+        use_compel = prompt_length > 50 and compel_processor is not None
+        
+        images = []
+
+        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:
+                # 使用embeddings生成
+                for idx in range(len(seeds)):
+                    generator = torch.Generator(device).manual_seed(seeds[idx])
+                    out = 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]
+                    images.append(out)
+            else:
+                # Compel失败,回退到普通模式
+                print("⚠️ Falling back to standard generation")
+                for idx in range(len(seeds)):
+                    generator = torch.Generator(device).manual_seed(seeds[idx])
+                    out = 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]
+                    images.append(out)
+        else:
+            # 标准生成
+            print(f"📝 Standard generation ({prompt_length} words)")
+            for idx in range(len(seeds)):
+                generator = torch.Generator(device).manual_seed(seeds[idx])
+                out = 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]
+                images.append(out)
+        
+        progress(0.95, desc="Finalizing...")
+        
+        # 确保结果是PIL Image
+        for i, res in enumerate(images):
+            if not isinstance(res, Image.Image):
+                if isinstance(res, np.ndarray):
+                    if res.dtype != np.uint8:
+                        res = (res * 255).astype(np.uint8)
+                    res = Image.fromarray(res)
+                images[i] = res
+        
+        # 创建元数据
+        metadata = create_metadata_content(
+            prompt, enhanced_prompt, seeds[0] if seeds else -1, steps, cfg_scale,
+            width, height, style
+        )
+
+        generation_info = f"Model: {model_name} | Style: {style} | Seeds: {', '.join(str(s) for s in seeds)} | Size: {width}×{height} | Steps: {steps} | CFG: {cfg_scale}"
+        
+        # 生成后立即清理
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+        
+        progress(1.0, desc="Complete!")
+        print("✅ Generation successful\n")
+        
+        return images, generation_info, metadata
+        
+    except Exception as e:
+        error_msg = str(e)
+        print(f"❌ Generation error: {error_msg}")
+        print(traceback.format_exc())
+        
+        # 错误后也要清理
+        try:
+            cleanup_pipeline()
+        except:
+            pass
+        
+        return None, "", f"❌ Generation failed: {error_msg}"
+
+# ===== CSS样式 =====
+css = """
+.gradio-container {overflow-y: auto !important; height: 100vh !important;}
+#gallery {min-height: 800px !important; overflow-y: visible !important;}
+.scroll-hide {overflow-y: auto !important;}
+
+.gradio-container {overflow-y: auto !important; height: 100vh !important;}
+#gallery {min-height: 800px !important; overflow-y: visible !important;}
+.scroll-hide {overflow-y: auto !important;}
+
+.gradio-container {overflow-y: auto !important;}
+#gallery {min-height: 800px !important;}
+.scroll-hide {overflow-y: auto !important;}
+
+.gradio-container {overflow-y: auto !important;}
+#gallery {min-height: 800px !important;}
+.scroll-hide {overflow-y: auto !important;}
+
+.gradio-container {
+    max-width: 100% !important;
+    margin: 0 !important;
+    padding: 0 !important;
+    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%) !important;
+    min-height: 100vh !important;
+    font-family: 'Segoe UI', Arial, sans-serif !important;
+}
+
+.main-content {
+    background: rgba(255, 255, 255, 0.95) !important;
+    border-radius: 20px !important;
+    padding: 20px !important;
+    margin: 15px !important;
+    box-shadow: 0 10px 25px rgba(0, 0, 0, 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, #667eea, #764ba2) !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, #667eea, #764ba2) !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;
+}
+
+.model-info {
+    background: linear-gradient(135deg, rgba(102, 126, 234, 0.1), rgba(118, 75, 162, 0.1)) !important;
+    color: #764ba2 !important;
+    padding: 10px !important;
+    border-radius: 8px !important;
+    margin-bottom: 15px !important;
+    text-align: center !important;
+    font-weight: 600 !important;
+    font-size: 13px !important;
+    border: 2px solid rgba(118, 75, 162, 0.3) !important;
+}
+
+.prompt-box textarea, .prompt-box input {
+    border-radius: 10px !important;
+    border: 2px solid #667eea !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: #764ba2 !important;
+    box-shadow: 0 0 15px rgba(118, 75, 162, 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(102, 126, 234, 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: #667eea !important;
+}
+
+.controls-section input[type="number"],
+.controls-section input[type="range"] {
+    background: rgba(255, 255, 255, 0.9) !important;
+    border: 1px solid #667eea !important;
+    border-radius: 6px !important;
+    padding: 8px !important;
+    color: #2d2d2d !important;
+}
+
+.generate-btn {
+    background: linear-gradient(45deg, #667eea, #764ba2) !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(102, 126, 234, 0.5) !important;
+}
+
+.image-output {
+    border-radius: 15px !important;
+    overflow: hidden !important;
+    max-width: 100% !important;
+    max-height: 70vh !important;
+    border: 3px solid #764ba2 !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, 0.9), 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(102, 126, 234, 0.2) !important;
+    backdrop-filter: blur(5px) !important;
+}
+
+.metadata-box {
+    background: linear-gradient(135deg, rgba(248, 249, 250, 0.9), 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(102, 126, 234, 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(title="ADULT AI Image Generator") as interface:
+        with gr.Column(elem_classes=["main-content"]):
+            gr.HTML('<div class="title">🎨 ADULT 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 (Use Danbooru tags style)",
+                        placeholder="1boy, solo, messy hair, blue eyes, detailed face, handsome...",
+                        value=get_random_prompt(),
+                        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"]):
+                        model_input = gr.Dropdown(
+                            label="Choose Local Model",
+                            choices=LOCAL_MODEL_CHOICES,
+                            value=LOCAL_MODEL_CHOICES[0] if LOCAL_MODEL_CHOICES else None,
+                            interactive=True,
+                            allow_custom_value=False
+                        )
+                        if not LOCAL_MODEL_CHOICES:
+                            gr.HTML(
+                                f'<div class="warning-box">⚠️ No local models found in <code>{LOCAL_MODEL_DIRECTORY}</code>. Add .safetensors files or set LOCAL_SD_MODEL_DIR.</div>'
+                            )
+                    
+                    with gr.Group(elem_classes=["controls-section"]):
+                        style_input = gr.Radio(
+                            label="Style Preset",
+                            choices=list(STYLE_KEYWORDS.keys()),
+                            value="Standard Quality"
+                        )
+                    
+                    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=896,
+                            step=64,
+                            info="Recommended: 896"
+                        )
+                    
+                    with gr.Group(elem_classes=["controls-section"]):
+                        height_input = gr.Slider(
+                            label="Height",
+                            minimum=512,
+                            maximum=2048,
+                            value=1152,
+                            step=64,
+                            info="Recommended: 1152"
+                        )
+                    
+                    with gr.Group(elem_classes=["controls-section"]):
+                        steps_input = gr.Slider(
+                            label="Steps",
+                            minimum=10,
+                            maximum=50,
+                            value=20,
+                            step=1,
+                            info="Recommended: 20"
+                        )
+                        
+                        cfg_input = gr.Slider(
+                            label="CFG Scale",
+                            minimum=1.0,
+                            maximum=15.0,
+                            value=6.0,
+                            step=0.1,
+                            info="Recommended: 6.0"
+                        )
+
+                        num_images_input = gr.Slider(
+                            label="Number of Images",
+                            minimum=1,
+                            maximum=50,
+                            value=1,
+                            step=1,
+                            info="Generate multiple images (each with different seed)"
+                        )
+                    
+                    generate_button = gr.Button(
+                        "GENERATE",
+                        elem_classes=["generate-btn"],
+                        variant="primary"
+                    )
+            
+            image_output = gr.Gallery(
+                label="Generated Images",
+                elem_classes=["image-output"],
+                show_label=False,
+                container=True,
+                columns=2
+            )
+            
+            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, model_name, style, neg_prompt, steps, cfg, seed, num_images, width, height):
+                images, info, metadata = generate_image(
+                    prompt, style, neg_prompt, steps, cfg, seed, width, height, model_name, num_images
+                )
+                
+                if image is not None:
+                    return (
+                        images,
+                        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, model_input, style_input, negative_prompt_input,
+                    steps_input, cfg_input, seed_input, num_images_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, model_input, style_input, negative_prompt_input,
+                    steps_input, cfg_input, seed_input, num_images_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 ADULT AI Image Generator (YAOI Friendly) ")
+    print("="*50)
+    print(f"📂 Local model directory: {LOCAL_MODEL_DIRECTORY}")
+    print(f"📄 Available local models: {', '.join(LOCAL_MODEL_CHOICES) if LOCAL_MODEL_CHOICES else 'NONE FOUND'}")
+    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'}")
+    if LORA_CONFIGS:
+        print(f"🎨 LoRA: LogoRedmond-LogoLoraForSDXL-V2 (scale: {LORA_CONFIGS[0].get('scale', 0.8)})")
+    print("="*50 + "\n")
+    
+    # 不预加载模型,让ZeroGPU按需分配
+    # 这样可以避免GPU分配冲突
+    
+    app = create_interface()
+    app.queue(max_size=10, default_concurrency_limit=2)
+    
+    server_port = get_server_port(7860)
+    print(f"🚪 Launching Gradio on port: {server_port}")
+
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=server_port,
+        share=True,
+        css=css
+    )

app.py backup

@@ -0,0 +1,800 @@
+# ===== 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 =====
+# Kageillustrious风格核心关键词 - 使用Danbooru标签风格
+STYLE_KEYWORDS = {
+    "None": {
+        "prefix": "",
+        "suffix": ""
+    },
+    "Standard Quality": {
+        "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"
+    },
+    "High Detail": {
+        "prefix": "masterpiece, best quality, amazing quality, very aesthetic, high resolution, ultra-detailed, absurdres, newest, colorful, rim light, backlit, highest detailed",
+        "suffix": ""
+    },
+    "Realistic": {
+        "prefix": "masterpiece, best quality, amazing quality, very aesthetic, absurdres, (photorealistic:1.3), (realistic:1.4), detailed skin texture, cinematic lighting",
+        "suffix": "sharp focus, detailed anatomy, realistic proportions, detailed face, natural pose, expressive eyes, 8k resolution"
+    },
+    "Anime": {
+        "prefix": "masterpiece, best quality, amazing quality, very aesthetic, absurdres, anime style, vibrant colors, detailed anime",
+        "suffix": "cel shading, clean linework, vibrant anime colors, detailed anime eyes, smooth anime skin"
+    },
+    "Artistic": {
+        "prefix": "masterpiece, best quality, amazing quality, very aesthetic, absurdres, artistic, illustration, detailed artwork",
+        "suffix": "vibrant colors, expressive, detailed composition, artistic rendering"
+    }
+}
+
+# 通用质量增强词
+QUALITY_TAGS = "very awa, masterpiece, best quality, high resolution, highly detailed, professional"
+
+# 修改为Kageillustrious模型 - 使用from_single_file加载
+FIXED_MODEL_REPO = "PutiLeslie/kageillustrious_v60NLXLVersion"
+FIXED_MODEL_FILE = "kageillustrious_v60NLXLVersion.safetensors"
+
+# LoRA 配置 - 保留原有的LoRA(可能需要测试兼容性)
+LORA_CONFIGS = [
+    {
+        "repo_id": "artificialguybr/LogoRedmond-LogoLoraForSDXL-V2",
+        "weight_name": "LogoRedAF.safetensors",
+        "adapter_name": "logo_lora",
+        "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 - 使用from_single_file加载Kageillustrious"""
+    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 Kageillustrious model from: {FIXED_MODEL_REPO}")
+        
+        # 使用from_single_file加载单个safetensors文件
+        from huggingface_hub import hf_hub_download
+        
+        # 下载模型文件
+        model_path = hf_hub_download(
+            repo_id=FIXED_MODEL_REPO,
+            filename=FIXED_MODEL_FILE
+        )
+        
+        print(f"📥 Model downloaded to: {model_path}")
+        
+        # 使用from_single_file加载
+        pipeline = StableDiffusionXLPipeline.from_single_file(
+            model_path,
+            torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
+            use_safetensors=True,
+            safety_checker=None,
+            requires_safety_checker=False
+        )
+        
+        # 优化调度器 - 使用Euler适合Illustrious系列
+        pipeline.scheduler = EulerDiscreteScheduler.from_config(
+            pipeline.scheduler.config,
+            timestep_spacing="trailing"
+        )
+        
+        pipeline = pipeline.to(device)
+        
+        # 加载 LoRA
+        print("🎨 Loading LoRA models...")
+        adapter_names = []
+        adapter_scales = []
+        
+        for lora_config in LORA_CONFIGS:
+            try:
+                pipeline.load_lora_weights(
+                    lora_config["repo_id"],
+                    weight_name=lora_config["weight_name"],
+                    adapter_name=lora_config["adapter_name"]
+                )
+                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}")
+        
+        # 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}")
+            except Exception as e:
+                print(f"⚠️ Failed to set adapter scales: {e}")
+        
+        # GPU优化 - 适配ZeroGPU环境
+        if torch.cuda.is_available():
+            try:
+                # VAE优化
+                pipeline.enable_vae_slicing()
+                pipeline.enable_vae_tiling()
+                
+                # 尝试启用xformers
+                try:
+                    pipeline.enable_xformers_memory_efficient_attention()
+                    print("✅ xFormers enabled")
+                except:
+                    print("⚠️ xFormers not available, using default attention")
+                
+                # 不使用torch.compile，因为它在ZeroGPU环境中不稳定
+                print("ℹ️ Skipping torch.compile for ZeroGPU compatibility")
+                
+            except Exception as opt_error:
+                print(f"⚠️ Optimization warning: {opt_error}")
+        
+        # 初始化Compel用于长提示词
+        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
+        
+        model_loaded = True
+        print("✅ Kageillustrious 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 - 适配Kageillustrious的Danbooru标签风格"""
+    if not prompt or prompt.strip() == "":
+        return ""
+    
+    # 获取风格关键词
+    style_config = STYLE_KEYWORDS.get(style, STYLE_KEYWORDS["None"])
+    
+    # 组合顺序:风格前缀 → 用户提示词 → 风格后缀 → 质量标签
+    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 - 适配Illustrious系列"""
+    # Illustrious系列推荐的负面提示词
+    base_negative = "lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, signature, watermark, username, blurry"
+    
+    # 风格特定的负面词
+    style_negatives = {
+        "Standard Quality": ", (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, (depth of field:1.2), (bokeh:1.2)",
+        "Realistic": ", (cartoon:1.3), (anime:1.3), (3d render:1.2), (illustration:1.2)",
+        "Anime": ", (realistic:1.3), (photorealistic:1.3), (photo:1.2)",
+        "Artistic": ", (photo:1.2), (photorealistic:1.2)"
+    }
+    
+    negative = base_negative
+    if style in style_negatives:
+        negative += style_negatives[style]
+    
+    # 添加用户自定义负面词
+    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:
+        # Compel会自动处理超过77 tokens的提示词
+        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")
+    
+    # 获取 LoRA 信息
+    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}
+Seed: {seed}
+Steps: {steps}
+CFG Scale: {cfg_scale}
+Dimensions: {width}x{height}
+Style: {style}
+"""
+
+def cleanup_pipeline():
+    """清理 pipeline 状态，防止污染"""
+    global pipeline
+    
+    if pipeline is None:
+        return
+    
+    try:
+        # 清理 CUDA 缓存
+        if torch.cuda.is_available():
+            torch.cuda.empty_cache()
+            torch.cuda.ipc_collect()
+        
+        # 清理 pipeline 的内部缓存
+        if hasattr(pipeline, 'unet'):
+            # 清空 UNet 的注意力缓存
+            if hasattr(pipeline.unet, 'set_attn_processor'):
+                try:
+                    from diffusers.models.attention_processor import AttnProcessor
+                    pipeline.unet.set_attn_processor(AttnProcessor())
+                except:
+                    pass
+        
+        # 清理 VAE 缓存
+        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 = 20, cfg_scale: float = 6.0,
+                   seed: int = -1, width: int = 896, height: int = 1152,
+                   progress=gr.Progress()):
+    """图像生成主函数 - 使用Kageillustrious推荐参数"""
+    
+    # 验证输入
+    if not prompt or prompt.strip() == "":
+        return None, "", "❌ Please enter a prompt"
+    
+    progress(0.05, desc="Initializing...")
+    
+    # 初始化模型
+    if not initialize_model():
+        return None, "", "❌ Failed to load model"
+    
+    # 清理之前的状态
+    cleanup_pipeline()
+    
+    progress(0.1, desc="Processing prompt...")
+    
+    try:
+        # 处理seed
+        if seed == -1:
+            seed = random.randint(0, np.iinfo(np.int32).max)
+        
+        # 重要：为每次生成创建新的 generator，避免状态污染
+        generator = torch.Generator(device).manual_seed(seed)
+        
+        # 增强提示词
+        enhanced_prompt = enhance_prompt(prompt, style)
+        
+        # 构建负面提示词
+        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...")
+        
+        # 检查提示词长度并决定是否使用Compel
+        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:
+                # 使用embeddings生成
+                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:
+                # Compel失败,回退到普通模式
+                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:
+            # 标准生成
+            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...")
+        
+        # 确保结果是PIL Image
+        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)
+        
+        # 创建元数据
+        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}"
+        
+        # 生成后立即清理
+        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())
+        
+        # 错误后也要清理
+        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, #667eea 0%, #764ba2 100%) !important;
+    min-height: 100vh !important;
+    font-family: 'Segoe UI', Arial, sans-serif !important;
+}
+
+.main-content {
+    background: rgba(255, 255, 255, 0.95) !important;
+    border-radius: 20px !important;
+    padding: 20px !important;
+    margin: 15px !important;
+    box-shadow: 0 10px 25px rgba(0, 0, 0, 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, #667eea, #764ba2) !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, #667eea, #764ba2) !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;
+}
+
+.model-info {
+    background: linear-gradient(135deg, rgba(102, 126, 234, 0.1), rgba(118, 75, 162, 0.1)) !important;
+    color: #764ba2 !important;
+    padding: 10px !important;
+    border-radius: 8px !important;
+    margin-bottom: 15px !important;
+    text-align: center !important;
+    font-weight: 600 !important;
+    font-size: 13px !important;
+    border: 2px solid rgba(118, 75, 162, 0.3) !important;
+}
+
+.prompt-box textarea, .prompt-box input {
+    border-radius: 10px !important;
+    border: 2px solid #667eea !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: #764ba2 !important;
+    box-shadow: 0 0 15px rgba(118, 75, 162, 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(102, 126, 234, 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: #667eea !important;
+}
+
+.controls-section input[type="number"],
+.controls-section input[type="range"] {
+    background: rgba(255, 255, 255, 0.9) !important;
+    border: 1px solid #667eea !important;
+    border-radius: 6px !important;
+    padding: 8px !important;
+    color: #2d2d2d !important;
+}
+
+.generate-btn {
+    background: linear-gradient(45deg, #667eea, #764ba2) !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(102, 126, 234, 0.5) !important;
+}
+
+.image-output {
+    border-radius: 15px !important;
+    overflow: hidden !important;
+    max-width: 100% !important;
+    max-height: 70vh !important;
+    border: 3px solid #764ba2 !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, 0.9), 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(102, 126, 234, 0.2) !important;
+    backdrop-filter: blur(5px) !important;
+}
+
+.metadata-box {
+    background: linear-gradient(135deg, rgba(248, 249, 250, 0.9), 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(102, 126, 234, 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 AI Image Generator") as interface:
+        with gr.Column(elem_classes=["main-content"]):
+            gr.HTML('<div class="title">🎨 ADULT 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 (Use Danbooru tags style)",
+                        placeholder="1boy, solo, messy hair, blue eyes, detailed face, handsome...",
+                        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="Standard Quality"
+                        )
+                    
+                    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=896,
+                            step=64,
+                            info="Recommended: 896"
+                        )
+                    
+                    with gr.Group(elem_classes=["controls-section"]):
+                        height_input = gr.Slider(
+                            label="Height",
+                            minimum=512,
+                            maximum=2048,
+                            value=1152,
+                            step=64,
+                            info="Recommended: 1152"
+                        )
+                    
+                    with gr.Group(elem_classes=["controls-section"]):
+                        steps_input = gr.Slider(
+                            label="Steps",
+                            minimum=10,
+                            maximum=50,
+                            value=20,
+                            step=1,
+                            info="Recommended: 20"
+                        )
+                        
+                        cfg_input = gr.Slider(
+                            label="CFG Scale",
+                            minimum=1.0,
+                            maximum=15.0,
+                            value=6.0,
+                            step=0.1,
+                            info="Recommended: 6.0"
+                        )
+                    
+                    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 ADULT AI Image Generator (YAOI Friendly) ")
+    print("="*50)
+    print(f"📦 Model: {FIXED_MODEL_REPO}")
+    print(f"📄 Model File: {FIXED_MODEL_FILE}")
+    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'}")
+    if LORA_CONFIGS:
+        print(f"🎨 LoRA: LogoRedmond-LogoLoraForSDXL-V2 (scale: {LORA_CONFIGS[0].get('scale', 0.8)})")
+    print("="*50 + "\n")
+    
+    # 不预加载模���,让ZeroGPU按需分配
+    # 这样可以避免GPU分配冲突
+    
+    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
+    )

app_log.txt

@@ -0,0 +1,21 @@
+⚠️ Spaces not available - running in regular mode
+2026-05-16 18:12:52.886593: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+2026-05-16 18:13:11.504646: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+✅ Compel available for long prompt processing
+
+==================================================
+🚀 Starting ADULT AI Image Generator (YAOI Friendly) 
+==================================================
+📂 Local model directory: G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion
+📄 Available local models: Cartoonish.safetensors, add-micro-details-concept-illustrious-or-pony-or-noobai.safetensors, ass-ripple-also-known-.safetensors, kageillustrious_v60NLXLVersion.safetensors, nova-anime-xl.safetensors, nova-furry-xl.safetensors, nova-orange-xl.safetensors, novaAnimeXL.safetensors, ntrnetorare.safetensors, ramthrusts-nsfw-pink-alchemy-mix.safetensors, wai-illustrious-sdxl.safetensors
+🖥️ Device: CPU
+ΓÜí ZeroGPU: Disabled
+📝 Compel: Available
+🎨 LoRA: LogoRedmond-LogoLoraForSDXL-V2 (scale: 0.8)
+==================================================
+
+🚪 Launching Gradio on port: 62173
+* Running on local URL:  http://0.0.0.0:62173
+* Running on public URL: https://b4d65e015773a6fbb5.gradio.live
+
+This share link expires in 1 week. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)

app_run_log.txt

@@ -0,0 +1,3 @@
+2026-05-16 18:19:20.540581: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+2026-05-16 18:19:31.317168: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+⚠️ Spaces not available - running in regular mode

launch.log

@@ -0,0 +1,28 @@
+⚠️ Spaces not available - running in regular mode
+2026-05-16 17:45:18.175228: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+2026-05-16 17:46:31.210179: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+✅ Compel available for long prompt processing
+
+==================================================
+🚀 Starting ADULT AI Image Generator (YAOI Friendly) 
+==================================================
+� Local model directory: G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion
+📄 Available local models: Cartoonish.safetensors, add-micro-details-concept-illustrious-or-pony-or-noobai.safetensors, ass-ripple-also-known-.safetensors, kageillustrious_v60NLXLVersion.safetensors, nova-anime-xl.safetensors, nova-furry-xl.safetensors, nova-orange-xl.safetensors, novaAnimeXL.safetensors, ntrnetorare.safetensors, ramthrusts-nsfw-pink-alchemy-mix.safetensors, wai-illustrious-sdxl.safetensors
+🖥️ Device: CPU
+⚡ ZeroGPU: Disabled
+📝 Compel: Available
+🎨 LoRA: LogoRedmond-LogoLoraForSDXL-V2 (scale: 0.8)
+==================================================
+
+c:\Users\LAPTOP_PC\Desktop\New Folder\Raw_Alexander\app.py:675: UserWarning: The parameters have been moved from the Blocks constructor to the launch() method in Gradio 6.0: css. Please pass these parameters to launch() instead.
+  with gr.Blocks(css=css, title="ADULT AI Image Generator") as interface:
+ERROR:    [Errno 10048] error while attempting to bind on address ('0.0.0.0', 7860): only one usage of each socket address (protocol/network address/port) is normally permitted
+Traceback (most recent call last):
+  File "c:\Users\LAPTOP_PC\Desktop\New Folder\Raw_Alexander\app.py", line 864, in <module>
+    app.launch(server_name="0.0.0.0",
+  File "C:\Users\LAPTOP_PC\AppData\Local\Programs\Python\Python311\Lib\site-packages\gradio\blocks.py", line 2774, in launch
+    ) = http_server.start_server(
+        ^^^^^^^^^^^^^^^^^^^^^^^^^
+  File "C:\Users\LAPTOP_PC\AppData\Local\Programs\Python\Python311\Lib\site-packages\gradio\http_server.py", line 182, in start_server
+    raise OSError(
+OSError: Cannot find empty port in range: 7860-7860. You can specify a different port by setting the GRADIO_SERVER_PORT environment variable or passing the `server_port` parameter to `launch()`.

list_models.py

@@ -0,0 +1,10 @@
+import os
+p=r'G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion'
+files=[f for f in os.listdir(p) if os.path.splitext(f)[1].lower() in ['.safetensors','.ckpt']]
+for f in sorted(files):
+    fp=os.path.join(p,f)
+    try:
+        s=os.path.getsize(fp)
+    except Exception as e:
+        s=0
+    print(f, s//(1024*1024), 'MB')

requirements.txt

@@ -0,0 +1,27 @@
+# ===== Core ML Stack =====
+# torch 和 torchvision 由 ZeroGPU 自动管理，不要写死版本
+
+# Stable Diffusion + LoRA
+diffusers==0.31.0
+transformers==4.46.3
+accelerate==1.1.1
+safetensors==0.4.5
+peft==0.13.2
+compel==2.0.3
+
+# huggingface_hub 版本必须 <1.0 以兼容 transformers 4.46.3
+huggingface-hub>=0.23.2,<1.0
+
+# Gradio 和 spaces 由 ZeroGPU 自动管理，不要写死版本
+
+# Core Utilities
+Pillow==11.0.0
+numpy==1.26.4
+python-dateutil==2.9.0.post0
+
+# For logging / error handling
+requests==2.32.3
+tqdm==4.66.5
+
+# Optional visualization/debugging
+matplotlib==3.9.2

run_sample.py

@@ -0,0 +1,54 @@
+from app import LOCAL_MODEL_DIRECTORY, get_local_models, initialize_model, generate_image
+import os
+
+models = get_local_models()
+if not models:
+    print('No local models found')
+    raise SystemExit(1)
+
+models_with_size = []
+for model_name in models:
+    model_path = os.path.join(LOCAL_MODEL_DIRECTORY, model_name)
+    try:
+        size = os.path.getsize(model_path)
+    except OSError:
+        size = float('inf')
+    if size >= 50 * 1024 * 1024:
+        models_with_size.append((size, model_name))
+
+if not models_with_size:
+    print('No valid model files found with size >= 50MB')
+    raise SystemExit(1)
+
+model = min(models_with_size, key=lambda x: x[0])[1]
+print('Using model:', model)
+
+ok = initialize_model(model)
+if not ok:
+    print('Failed to initialize model')
+    raise SystemExit(1)
+
+prompt = "A cinematic, photorealistic landscape, dramatic lighting, 8k"
+
+images, info, meta = generate_image(
+    prompt=prompt,
+    style='Standard Quality',
+    negative_prompt='',
+    steps=20,
+    cfg_scale=6.0,
+    seed=-1,
+    width=896,
+    height=1152,
+    model_name=model,
+    num_images=2,
+    progress=lambda *a, **k: None
+)
+
+print(info)
+print(meta)
+
+os.makedirs('generated_images', exist_ok=True)
+for i, img in enumerate(images):
+    path = os.path.join('generated_images', f'sample_{i}.png')
+    img.save(path)
+    print('Saved', path)

run_sample_small.py

@@ -0,0 +1,33 @@
+from app import get_local_models, initialize_model, generate_image
+import os
+models = get_local_models()
+# pick smallest non-zero model
+p=r'G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion'
+models_with_size = []
+for m in models:
+    fp=os.path.join(p,m)
+    try:
+        s=os.path.getsize(fp)
+    except:
+        s=0
+    if s>0:
+        models_with_size.append((s,m))
+if not models_with_size:
+    print('No usable models found')
+    raise SystemExit(1)
+models_with_size.sort()
+model=models_with_size[0][1]
+print('Using model:', model)
+ok=initialize_model(model)
+if not ok:
+    print('Failed to initialize model')
+    raise SystemExit(1)
+prompt='A cinematic, photorealistic landscape, dramatic lighting, 8k'
+images, info, meta = generate_image(prompt=prompt, style='Standard Quality', negative_prompt='', steps=20, cfg_scale=6.0, seed=-1, width=896, height=1152, model_name=model, num_images=2, progress=lambda *a, **k: None)
+print(info)
+print(meta)
+os.makedirs('generated_images', exist_ok=True)
+for i, img in enumerate(images):
+    path=os.path.join('generated_images', f'small_sample_{i}.png')
+    img.save(path)
+    print('Saved', path)

run_small_log.txt

@@ -0,0 +1,20 @@
+2026-05-16 18:29:52.087943: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+2026-05-16 18:30:00.633376: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
+⚠️ Spaces not available - running in regular mode
+✅ Compel available for long prompt processing
+Using model: ass-ripple-also-known-.safetensors
+🖥️ Using device: cpu
+📦 Loading local model from: G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion\ass-ripple-also-known-.safetensors
+⚠️ SDXL load failed: Unable to load weights from checkpoint file for 'G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion\ass-ripple-also-known-.safetensors' at 'G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion\ass-ripple-also-known-.safetensors'. 
+ℹ️ Attempting low-memory load (may use device mapping / lower precision)...
+⚠️ Low-memory SDXL load failed: Unable to load weights from checkpoint file for 'G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion\ass-ripple-also-known-.safetensors' at 'G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion\ass-ripple-also-known-.safetensors'. 
+ℹ️ Falling back to standard Stable Diffusion loader
+⚠️ Standard SD load failed: Unable to load weights from checkpoint file for 'G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion\ass-ripple-also-known-.safetensors' at 'G:\My Drive\sd\stable-diffusion-webui\models\Stable-diffusion\ass-ripple-also-known-.safetensors'. 
+❌ Model loading error: 'NoneType' object has no attribute 'to'
+Traceback (most recent call last):
+  File "C:\Users\LAPTOP_PC\Desktop\New Folder\Raw_Alexander\app.py", line 226, in initialize_model
+    pipeline = pipeline.to(device)
+               ^^^^^^^^^^^
+AttributeError: 'NoneType' object has no attribute 'to'
+
+Failed to initialize model