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