Update app.py

app.py

@@ -1,154 +1,866 @@
+# ===== 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 DiffusionPipeline
+from PIL import Image
+import traceback
 import numpy as np
-import random
+import cv2
+import imageio
+from pathlib import Path
+import tempfile
+import shutil
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
+# ===== 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")
+
+# ===== 修复后的配置 =====
+STYLE_PRESETS = {
+    "None": "",
+    "Cinematic": "cinematic lighting, dramatic composition, film grain, professional cinematography, movie scene, high production value",
+    "Anime": "anime style, detailed animation, high quality anime, cel animation, vibrant anime colors, smooth animation",
+    "Realistic": "photorealistic, ultra-detailed, natural lighting, realistic motion, lifelike animation, high fidelity",
+    "Fantasy": "fantasy style, magical atmosphere, ethereal lighting, mystical effects, enchanted scene",
+    "Artistic": "artistic style, creative composition, unique visual style, expressive animation, stylized rendering"
+}
+
+# 固定模型配置 - 使用官方Diffusers兼容版本
+FIXED_MODEL = "Wan-AI/Wan2.2-T2V-A14B-Diffusers"  # 最新版本
+# 备用选择: "Wan-AI/Wan2.1-T2V-14B-Diffusers"
 
-device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
+# 质量增强提示词 - 适配视频
+QUALITY_ENHANCERS = [
+    "high quality video", "(masterpiece:1.3)", "(best quality:1.2)", "smooth animation",
+    "detailed motion", "fluid movement", "professional video quality", "high resolution",
+    "consistent lighting", "stable composition", "(perfect anatomy:1.1)", "natural motion",
+    "cinematic quality", "detailed textures", "smooth transitions"
 ]
 
-css = """
-#col-container {
-    margin: 0 auto;
-    max-width: 640px;
+# 修复后的风格专用增强词 - 视频优化
+STYLE_ENHANCERS = {
+    "Cinematic": [
+        "cinematic shot", "(cinematic quality:1.3)", "movie scene", "film lighting", 
+        "dramatic composition", "professional cinematography", "cinematic motion", "film grain",
+        "dynamic camera work", "cinematic storytelling"
+    ],
+    "Anime": [
+        "anime animation", "(high quality anime:1.3)", "detailed anime", "smooth anime motion", 
+        "cel animation style", "(anime video:1.2)", "vibrant anime colors", "anime cinematics",
+        "japanese animation", "fluid anime movement"
+    ],
+    "Realistic": [
+        "realistic video", "(photorealistic animation:1.3)", "natural motion", "lifelike movement", 
+        "realistic lighting", "(realistic video:1.2)", "natural dynamics", "authentic motion",
+        "real-world physics", "documentary style"
+    ],
+    "Fantasy": [
+        "fantasy animation", "(magical video:1.3)", "mystical motion", "ethereal effects", 
+        "enchanted scene", "magical atmosphere", "fantasy cinematics", "surreal animation",
+        "otherworldly movement", "magical realism"
+    ],
+    "Artistic": [
+        "artistic video", "(creative animation:1.3)", "stylized motion", "artistic composition", 
+        "unique visual style", "expressive animation", "creative cinematics", "artistic movement",
+        "stylized rendering", "avant-garde video"
+    ]
 }
-"""
 
-with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
-
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
+# 视频参数配置
+VIDEO_CONFIG = {
+    "default_duration": 4.0,  # 秒
+    "max_duration": 8.0,
+    "default_fps": 24,
+    "max_fps": 30,
+    "default_width": 512,
+    "default_height": 512,
+    "max_resolution": 768
+}
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+SAVE_DIR = "generated_videos"
+os.makedirs(SAVE_DIR, exist_ok=True)
 
-        result = gr.Image(label="Result", show_label=False)
+# ===== 模型相关变量 =====
+pipeline = None
+compel_processor = None
+device = None
+model_loaded = False
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
+def initialize_model():
+    """优化的模型初始化函数"""
+    global pipeline, compel_processor, device, model_loaded
+    
+    if model_loaded and pipeline is not None:
+        return True
+    
+    try:
+        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        print(f"🖥️ Using device: {device}")
+        
+        print(f"📦 Loading Official Wan T2V model: {FIXED_MODEL}")
+        
+        # 基础模型加载 - 使用官方Wan Pipeline
+        try:
+            from diffusers import WanPipeline
+            pipeline = WanPipeline.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
+            )
+        except ImportError:
+            # 如果WanPipeline不可用，使用通用DiffusionPipeline
+            print("⚠️ WanPipeline not found, using DiffusionPipeline")
+            pipeline = DiffusionPipeline.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,
+                trust_remote_code=True
             )
+        
+        pipeline = pipeline.to(device)
+        
+        # GPU优化
+        if torch.cuda.is_available():
+            try:
+                pipeline.enable_vae_slicing()
+                pipeline.enable_attention_slicing()
+                try:
+                    pipeline.enable_xformers_memory_efficient_attention()
+                except:
+                    pass
+                # 视频生成特有的内存优化
+                if hasattr(pipeline, 'enable_sequential_cpu_offload'):
+                    pipeline.enable_sequential_cpu_offload()
+            except Exception as mem_error:
+                print(f"⚠️ Memory optimization warning: {mem_error}")
+        
+        # 初始化Compel
+        if COMPEL_AVAILABLE and hasattr(pipeline, 'tokenizer'):
+            try:
+                compel_processor = Compel(
+                    tokenizer=pipeline.tokenizer,
+                    text_encoder=pipeline.text_encoder,
+                    returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED,
+                    truncate_long_prompts=False
+                )
+                print("✅ Long prompt processor (Compel) initialized successfully")
+            except Exception as compel_error:
+                print(f"⚠️ Compel initialization failed: {compel_error}")
+                compel_processor = None
+        
+        model_loaded = True
+        print("✅ T2V Model initialization complete")
+        return True
+        
+    except Exception as e:
+        print(f"❌ Critical model loading error: {e}")
+        print(traceback.format_exc())
+        model_loaded = False
+        return False
+
+def enhance_prompt(prompt: str, style: str) -> str:
+    """修复后的增强提示词函数 - 视频优化"""
+    if not prompt or prompt.strip() == "":
+        return ""
+    
+    enhanced_parts = [prompt.strip()]
+    
+    # 添加风格前缀
+    style_prefix = STYLE_PRESETS.get(style, "")
+    if style_prefix and style != "None":
+        enhanced_parts.insert(0, style_prefix)
+    
+    # 添加风格特定增强词
+    if style in STYLE_ENHANCERS and style != "None":
+        style_terms = ", ".join(STYLE_ENHANCERS[style])
+        enhanced_parts.append(style_terms)
+    
+    # 添加质量增强词
+    quality_terms = ", ".join(QUALITY_ENHANCERS)
+    enhanced_parts.append(quality_terms)
+    
+    enhanced_prompt = ", ".join(filter(None, enhanced_parts))
+    
+    print(f"🎨 Style: {style}")
+    print(f"📝 Original prompt: {prompt[:100]}...")
+    print(f"✨ Enhanced prompt: {enhanced_prompt[:150]}...")
+    
+    return enhanced_prompt
+
+def process_long_prompt(prompt, negative_prompt=""):
+    """处理长提示词"""
+    if not compel_processor:
+        return None, None
+    
+    try:
+        conditioning = compel_processor([prompt, negative_prompt])
+        return conditioning, None
+    except Exception as e:
+        print(f"Long prompt processing failed: {e}")
+        return None, None
 
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+def apply_spaces_decorator(func):
+    """应用spaces装饰器 - 增加超时时间"""
+    if SPACES_AVAILABLE:
+        return spaces.GPU(duration=120)(func)  # 2分钟超时
+    return func
+
+def create_metadata_content(prompt, enhanced_prompt, seed, steps, cfg_scale, width, height, duration, fps, style):
+    """创建元数据内容 - 视频版本"""
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    return f"""Generated Video Metadata
+======================
+Timestamp: {timestamp}
+Original Prompt: {prompt}
+Enhanced Prompt: {enhanced_prompt}
+Seed: {seed}
+Steps: {steps}
+CFG Scale: {cfg_scale}
+Dimensions: {width}×{height}
+Duration: {duration}s
+FPS: {fps}
+Style: {style}
+Model: NSFW_Wan_14b
+Total Frames: {int(duration * fps)}
+"""
+
+def frames_to_video(frames, output_path, fps=24, format="mp4"):
+    """将帧序列转换为视频文件"""
+    try:
+        if format.lower() == "gif":
+            # 生成GIF
+            imageio.mimsave(output_path, frames, fps=fps, loop=0)
+        else:
+            # 生成MP4
+            writer = imageio.get_writer(output_path, fps=fps, codec='libx264', quality=8)
+            for frame in frames:
+                if isinstance(frame, Image.Image):
+                    frame = np.array(frame)
+                writer.append_data(frame)
+            writer.close()
+        
+        return True
+    except Exception as e:
+        print(f"Video creation error: {e}")
+        return False
+
+@apply_spaces_decorator
+def generate_video(prompt: str, style: str, negative_prompt: str = "", steps: int = 20, cfg_scale: float = 7.0, 
+                  seed: int = -1, width: int = 512, height: int = 512, duration: float = 4.0, fps: int = 24,
+                  progress=gr.Progress()):
+    """视频生成函数"""
+    if not prompt or prompt.strip() == "":
+        return None, None, "", ""
+    
+    # 初始化模型
+    progress(0.1, desc="Loading T2V model...")
+    if not initialize_model():
+        return None, None, "", "❌ Failed to load T2V model"
+    
+    progress(0.2, desc="Processing prompt...")
+    
+    try:
+        # 处理seed
+        if seed == -1:
+            seed = random.randint(0, np.iinfo(np.int32).max)
+        
+        # 增强提示词
+        enhanced_prompt = enhance_prompt(prompt.strip(), style)
+        
+        # 增强负面提示词
+        if not negative_prompt.strip():
+            negative_prompt = "(low quality, worst quality:1.4), (bad anatomy:1.2), blurry, watermark, signature, text, error, distorted motion, choppy animation, inconsistent lighting, frame drops, stuttering"
+        
+        # 计算帧数
+        num_frames = int(duration * fps)
+        print(f"🎬 Generating {num_frames} frames at {fps} FPS for {duration}s video")
+        
+        # 生成参数
+        generator = torch.Generator(device).manual_seed(seed)
+        
+        progress(0.3, desc=f"Generating {duration}s video...")
+        
+        # 长提示词处理
+        use_long_prompt = len(enhanced_prompt.split()) > 60 or len(enhanced_prompt) > 300
+        
+        generation_kwargs = {
+            "num_inference_steps": steps,
+            "guidance_scale": cfg_scale,
+            "width": width,
+            "height": height,
+            "num_frames": num_frames,
+            "generator": generator
+        }
+        
+        if use_long_prompt and compel_processor:
+            conditioning, _ = process_long_prompt(enhanced_prompt, negative_prompt)
+            if conditioning is not None:
+                result = pipeline(
+                    prompt_embeds=conditioning[0:1],
+                    negative_prompt_embeds=conditioning[1:2],
+                    **generation_kwargs
+                )
+            else:
+                result = pipeline(
+                    prompt=enhanced_prompt,
+                    negative_prompt=negative_prompt,
+                    **generation_kwargs
+                )
+        else:
+            result = pipeline(
+                prompt=enhanced_prompt,
+                negative_prompt=negative_prompt,
+                **generation_kwargs
             )
+        
+        progress(0.8, desc="Processing video output...")
+        
+        # 获取视��帧
+        if hasattr(result, 'frames') and result.frames is not None:
+            frames = result.frames[0]  # 取第一个批次
+        elif hasattr(result, 'images') and result.images is not None:
+            frames = result.images
+        else:
+            return None, None, "", "❌ No frames generated from model"
+        
+        print(f"📹 Generated {len(frames)} frames")
+        
+        # 创建临时文件
+        with tempfile.TemporaryDirectory() as temp_dir:
+            # 生成MP4
+            mp4_path = os.path.join(temp_dir, f"video_{seed}.mp4")
+            mp4_success = frames_to_video(frames, mp4_path, fps=fps, format="mp4")
+            
+            # 生成GIF
+            gif_path = os.path.join(temp_dir, f"video_{seed}.gif")
+            gif_success = frames_to_video(frames, gif_path, fps=fps, format="gif")
+            
+            if not mp4_success and not gif_success:
+                return None, None, "", "❌ Failed to create video files"
+            
+            # 复制到永久目录
+            final_mp4_path = None
+            final_gif_path = None
+            
+            if mp4_success:
+                final_mp4_path = os.path.join(SAVE_DIR, f"video_{seed}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.mp4")
+                shutil.copy2(mp4_path, final_mp4_path)
+            
+            if gif_success:
+                final_gif_path = os.path.join(SAVE_DIR, f"video_{seed}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.gif")
+                shutil.copy2(gif_path, final_gif_path)
+        
+        progress(0.95, desc="Creating metadata...")
+        
+        # 创建元数据内容
+        metadata_content = create_metadata_content(
+            prompt, enhanced_prompt, seed, steps, cfg_scale, width, height, duration, fps, style
+        )
+        
+        progress(1.0, desc="Complete!")
+        
+        # 生成信息显示
+        generation_info = f"Style: {style} | Seed: {seed} | Size: {width}×{height} | Duration: {duration}s | FPS: {fps} | Frames: {num_frames}"
+        
+        # 返回MP4路径用于显示，同时提供两种格式的下载
+        return final_mp4_path, final_gif_path, generation_info, metadata_content
+        
+    except Exception as e:
+        error_msg = str(e)
+        print(f"Generation error: {error_msg}")
+        print(traceback.format_exc())
+        return None, None, "", f"❌ Generation failed: {error_msg}"
+
+# ===== CSS样式 - 视频版本 =====
+css = """
+/* 全局容器 */
+.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;
+}
+
+/* 主要内容区域 */
+.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;
+}
+
+/* 简化标题 */
+.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;
+}
+
+/* 简化警告信息 */
+.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;
+}
+
+/* 输入框样式 - 修复背景色 */
+.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;
+}
+
+/* 右侧控制区域 - 修复背景色 */
+.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;
+}
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
+.controls-section label {
+    font-weight: 600 !important;
+    color: #2d2d2d !important;
+    margin-bottom: 8px !important;
+}
+
+/* 修复单选按钮和输入框背景 */
+.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;
+}
+
+.controls-section select {
+    background: rgba(255, 255, 255, 0.9) !important;
+    border: 1px solid #bb6ded !important;
+    border-radius: 6px !important;
+    padding: 8px !important;
+    color: #2d2d2d !important;
+}
+
+/* 生成按钮 */
+.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;
+}
+
+/* 视频输出区域 */
+.video-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;
+}
+
+/* 下载按钮样式 */
+.download-btn {
+    background: linear-gradient(45deg, #28a745, #20c997) !important;
+    color: white !important;
+    border: none !important;
+    padding: 10px 20px !important;
+    border-radius: 20px !important;
+    font-size: 14px !important;
+    font-weight: bold !important;
+    margin: 5px !important;
+    cursor: pointer !important;
+    transition: all 0.3s ease !important;
+}
 
+.download-btn:hover {
+    transform: translateY(-1px) !important;
+    box-shadow: 0 5px 15px rgba(40, 167, 69, 0.4) !important;
+}
+
+/* 视频信息区域 */
+.video-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;
+}
+
+/* 元数据区域样式 */
+.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;
+}
+
+/* 滑块样式 */
+.slider-container input[type="range"] {
+    accent-color: #bb6ded !important;
+}
+
+/* 响应式设计 */
+@media (max-width: 768px) {
+    .main-content {
+        margin: 10px !important;
+        padding: 15px !important;
+    }
+    
+    .title {
+        font-size: 1.5rem !important;
+    }
+}
+
+/* 强制覆盖Gradio默认样式 */
+.gradio-container .gr-textbox, 
+.gradio-container .gr-radio-group,
+.gradio-container .gr-slider,
+.gradio-container .gr-number {
+    background: rgba(255, 255, 255, 0.95) !important;
+    border: 1px solid rgba(187, 109, 237, 0.5) !important;
+    border-radius: 8px !important;
+}
+
+.gradio-container .gr-radio-group label {
+    color: #2d2d2d !important;
+    background: transparent !important;
+}
+"""
+
+# ===== 创建UI =====
+def create_interface():
+    with gr.Blocks(css=css, title="Adult NSFW AI Video Generator") as interface:
+        with gr.Column(elem_classes=["main-content"]):
+            # 简化标题
+            gr.HTML('<div class="title">🎬 Adult NSFW AI Video Generator</div>')
+            
+            # 简化警告信息
+            gr.HTML('''
+                <div class="warning-box">
+                    ⚠️ 18+ CONTENT WARNING ⚠️ | T2V Model: NSFW_Wan_14b
+                </div>
+            ''')
+            
+            # 主要输入区域
             with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+                # 左侧：提示词输入
+                with gr.Column(scale=2):
+                    prompt_input = gr.Textbox(
+                        label="Detailed Video Prompt",
+                        placeholder="Describe the video scene you want to generate...",
+                        lines=12,
+                        elem_classes=["prompt-box"]
+                    )
+                    
+                    negative_prompt_input = gr.Textbox(
+                        label="Negative Prompt (Optional)",
+                        placeholder="Things you don't want in the video...",
+                        lines=4,
+                        elem_classes=["prompt-box"]
+                    )
+                
+                # 右侧：控制选项
+                with gr.Column(scale=1):
+                    # Style选项
+                    with gr.Group(elem_classes=["controls-section"]):
+                        style_input = gr.Radio(
+                            label="Style Preset",
+                            choices=list(STYLE_PRESETS.keys()),
+                            value="Cinematic"
+                        )
+                    
+                    # 视频参数
+                    with gr.Group(elem_classes=["controls-section"]):
+                        gr.HTML("<b>📹 Video Settings</b>")
+                        duration_input = gr.Slider(
+                            label=f"Duration (seconds)",
+                            minimum=1.0,
+                            maximum=VIDEO_CONFIG["max_duration"],
+                            value=VIDEO_CONFIG["default_duration"],
+                            step=0.5
+                        )
+                        
+                        fps_input = gr.Slider(
+                            label="FPS (Frames per Second)",
+                            minimum=12,
+                            maximum=VIDEO_CONFIG["max_fps"],
+                            value=VIDEO_CONFIG["default_fps"],
+                            step=6
+                        )
+                    
+                    # 分辨率设置
+                    with gr.Group(elem_classes=["controls-section"]):
+                        gr.HTML("<b>📐 Resolution</b>")
+                        width_input = gr.Slider(
+                            label="Width",
+                            minimum=256,
+                            maximum=VIDEO_CONFIG["max_resolution"],
+                            value=VIDEO_CONFIG["default_width"],
+                            step=64
+                        )
+                        
+                        height_input = gr.Slider(
+                            label="Height",
+                            minimum=256,
+                            maximum=VIDEO_CONFIG["max_resolution"],
+                            value=VIDEO_CONFIG["default_height"],
+                            step=64
+                        )
+                    
+                    # Seed选项
+                    with gr.Group(elem_classes=["controls-section"]):
+                        seed_input = gr.Number(
+                            label="Seed (-1 for random)",
+                            value=-1,
+                            precision=0
+                        )
+                    
+                    # 高级参数
+                    with gr.Group(elem_classes=["controls-section"]):
+                        gr.HTML("<b>⚙️ Advanced</b>")
+                        steps_input = gr.Slider(
+                            label="Steps",
+                            minimum=10,
+                            maximum=30,
+                            value=20,
+                            step=1
+                        )
+                        
+                        cfg_input = gr.Slider(
+                            label="CFG Scale",
+                            minimum=1.0,
+                            maximum=15.0,
+                            value=7.0,
+                            step=0.1
+                        )
+                    
+                    # 生成按钮
+                    generate_button = gr.Button(
+                        "🎬 GENERATE VIDEO",
+                        elem_classes=["generate-btn"],
+                        variant="primary"
+                    )
+            
+            # 视频输出区域
+            with gr.Row():
+                video_output = gr.Video(
+                    label="Generated Video (MP4)",
+                    elem_classes=["video-output"],
+                    show_label=True,
+                    height=400
                 )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
+            
+            # 下载区域和信息显示
+            with gr.Row():
+                with gr.Column(scale=1):
+                    mp4_download = gr.File(
+                        label="📱 Download MP4",
+                        visible=False
+                    )
+                with gr.Column(scale=1):
+                    gif_download = gr.File(
+                        label="🎞️ Download GIF",
+                        visible=False
+                    )
+            
+            # 视频信息显示
+            with gr.Row():
+                generation_info = gr.Textbox(
+                    label="Generation Info",
+                    interactive=False,
+                    elem_classes=["video-info"],
+                    show_label=True,
+                    visible=False
                 )
-
+            
+            # 元数据显示区域
             with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
+                metadata_display = gr.Textbox(
+                    label="Video Metadata (Copy to save)",
+                    interactive=True,
+                    elem_classes=["metadata-box"],
+                    show_label=True,
+                    lines=15,
+                    visible=False,
+                    placeholder="Generated video metadata will appear here..."
                 )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
+        
+        # 生成视频的主要函数
+        def on_generate(prompt, style, neg_prompt, steps, cfg, seed, width, height, duration, fps):
+            mp4_path, gif_path, info, metadata = generate_video(
+                prompt, style, neg_prompt, steps, cfg, seed, width, height, duration, fps
+            )
+            
+            if mp4_path is not None:
+                return (
+                    mp4_path,  # 视频输出
+                    mp4_path if mp4_path and os.path.exists(mp4_path) else None,  # MP4下载
+                    gif_path if gif_path and os.path.exists(gif_path) else None,  # GIF下载
+                    gr.update(visible=True, value=info),  # 显示生成信息
+                    gr.update(visible=True, value=metadata),  # 显示元数据
+                    gr.update(visible=True),  # 显示MP4下载
+                    gr.update(visible=True)   # 显示GIF下载
                 )
+            else:
+                return (
+                    None, 
+                    None,
+                    None,
+                    gr.update(visible=True, value=info if info else "Generation failed"),
+                    gr.update(visible=False),
+                    gr.update(visible=False),
+                    gr.update(visible=False)
+                )
+        
+        # 绑定生成事件
+        generate_button.click(
+            fn=on_generate,
+            inputs=[
+                prompt_input, style_input, negative_prompt_input, 
+                steps_input, cfg_input, seed_input, width_input, height_input,
+                duration_input, fps_input
+            ],
+            outputs=[
+                video_output, mp4_download, gif_download,
+                generation_info, metadata_display,
+                mp4_download, gif_download
+            ],
+            show_progress=True
+        )
+        
+        # 支持Enter键触发
+        prompt_input.submit(
+            fn=on_generate,
+            inputs=[
+                prompt_input, style_input, negative_prompt_input, 
+                steps_input, cfg_input, seed_input, width_input, height_input,
+                duration_input, fps_input
+            ],
+            outputs=[
+                video_output, mp4_download, gif_download,
+                generation_info, metadata_display,
+                mp4_download, gif_download
+            ],
+            show_progress=True
+        )
+        
+        # 启动时显示欢迎信息
+        interface.load(
+            fn=lambda: (
+                None, None, None,
+                gr.update(visible=False), 
+                gr.update(visible=False),
+                gr.update(visible=False),
+                gr.update(visible=False)
+            ),
+            outputs=[
+                video_output, mp4_download, gif_download,
+                generation_info, metadata_display,
+                mp4_download, gif_download
+            ]
+        )
+    
+    return interface
 
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
-
+# ===== 启动应用 =====
 if __name__ == "__main__":
-    demo.launch()
+    print("🎬 Starting NSFW Video Generator...")
+    print(f"🔧 Official Wan T2V Model: {FIXED_MODEL}")
+    print("🔧 Using official Wan-AI Diffusers-compatible model")
+    print(f"🔧 Default Duration: {VIDEO_CONFIG['default_duration']}s")
+    print(f"🔧 Default Resolution: {VIDEO_CONFIG['default_width']}×{VIDEO_CONFIG['default_height']}")
+    print(f"🔧 Spaces GPU: {'✅ Available' if SPACES_AVAILABLE else '❌ Not Available'}")
+    print(f"🔧 Compel Library: {'✅ Available' if COMPEL_AVAILABLE else '❌ Not Available'}")
+    print(f"🔧 CUDA: {'✅ Available' if torch.cuda.is_available() else '❌ Not Available'}")
+    
+    app = create_interface()
+    app.queue(max_size=5, default_concurrency_limit=1)  # 降低并发以节省GPU
+    
+    app.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        show_error=True,
+        share=False
+    )