Delete Raw_Alexander

Raw_Alexander/.gitattributes

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Raw_Alexander/.gradio/certificate.pem

@@ -1,31 +0,0 @@
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Raw_Alexander/README.md

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----
-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

Raw_Alexander/app.py

@@ -1,982 +0,0 @@
-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
-    )

Raw_Alexander/app.py backup

@@ -1,800 +0,0 @@
-# ===== 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
-    )

Raw_Alexander/app_log.txt

@@ -1,21 +0,0 @@
-⚠️ 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)

Raw_Alexander/app_run_log.txt

@@ -1,3 +0,0 @@
-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

Raw_Alexander/launch.log

@@ -1,28 +0,0 @@
-⚠️ 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()`.

Raw_Alexander/list_models.py

@@ -1,10 +0,0 @@
-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')

Raw_Alexander/requirements.txt

@@ -1,27 +0,0 @@
-# ===== 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

Raw_Alexander/run_sample.py

@@ -1,54 +0,0 @@
-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)

Raw_Alexander/run_sample_small.py

@@ -1,33 +0,0 @@
-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)

Raw_Alexander/run_small_log.txt

@@ -1,20 +0,0 @@
-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