优化app.py文件，移除演示模式相关代码，简化图像生成逻辑，确保在GPU模式下正常初始化DreamRenderer管道。同时更新README.md中的sdk版本至5.31.0，并添加硬件配置说明。

README.md

@@ -4,10 +4,11 @@ emoji: 🎨
 colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 4.44.0
+sdk_version: 5.31.0
 app_file: app.py
 pinned: false
 license: apache-2.0
+hardware: zero-gpu-medium
 ---
 
 # DreamRenderer: Multi-Instance Attribute Control 🎨

app.py

@@ -6,13 +6,8 @@ from PIL import Image, ImageDraw
 import json
 import warnings
 from typing import Optional
-
-try:
-    from dream_renderer import DreamRendererPipeline
-    DEMO_MODE = False
-except ImportError:
-    print("Warning: dream_renderer 模块未找到。将使用演示模式。")
-    DEMO_MODE = True
+from dream_renderer import DreamRendererPipeline
+DEMO_MODE = False
 
 warnings.filterwarnings("ignore")
 
@@ -53,96 +48,65 @@ def create_demo_image(prompt: str, bbox_data: list, width: int = 512, height: in
     
     return image
 
-# 根据是否是演示模式决定是否使用GPU装饰器
-if DEMO_MODE:
-    def initialize_pipeline():
-        """初始化DreamRenderer管道（演示模式）"""
-        return "✅ 演示模式已启动！（未加载实际模型）"
-    
-    def generate_image_with_bbox(prompt: str, negative_prompt: str,
-                               num_inference_steps: int, guidance_scale: float, 
-                               width: int, height: int, seed: int, use_seed: bool):
-        """使用边界框生成图像（演示模式）"""
-        global current_bbox_data
-        
-        if not prompt.strip():
-            return None, "❌ 请输入提示词！"
-        
-        try:
-            # 创建演示图像
-            image = create_demo_image(prompt, current_bbox_data, width, height)
-            
-            info = f"✅ 演示图像生成成功！\n"
-            info += f"🔸 使用边界框: {len(current_bbox_data)}个\n"
-            info += f"🔸 推理步数: {num_inference_steps}\n"
-            info += f"🔸 引导强度: {guidance_scale}\n"
-            info += f"🔸 图像尺寸: {width}×{height}\n"
-            info += f"🔸 模式: 演示模式（非实际AI生成）\n"
-            if use_seed:
-                info += f"🔸 随机种子: {seed}"
-            
-            return image, info
-        except Exception as e:
-            return None, f"❌ 生成演示图像时出错: {str(e)}"
-else:
-    @spaces.GPU
-    def initialize_pipeline():
-        """初始化DreamRenderer管道"""
-        global pipeline
-        try:
-            if pipeline is None:
-                pipeline = DreamRendererPipeline()
-                # 预加载模型以节省时间
-                success = pipeline.load_model()
-                if success:
-                    return "✅ DreamRenderer管道已成功初始化并加载模型！"
-                else:
-                    return "⚠️ DreamRenderer管道已初始化，但模型加载失败。将使用演示模式。"
+
+@spaces.GPU
+def initialize_pipeline():
+    """初始化DreamRenderer管道"""
+    global pipeline
+    try:
+        if pipeline is None:
+            pipeline = DreamRendererPipeline()
+            # 预加载模型以节省时间
+            success = pipeline.load_model()
+            if success:
+                return "✅ DreamRenderer管道已成功初始化并加载模型！"
             else:
-                return "✅ DreamRenderer管道已经初始化完成！"
-        except Exception as e:
-            return f"❌ 初始化失败: {str(e)}"
+                return "⚠️ DreamRenderer管道已初始化，但模型加载失败。将使用演示模式。"
+        else:
+            return "✅ DreamRenderer管道已经初始化完成！"
+    except Exception as e:
+        return f"❌ 初始化失败: {str(e)}"
 
-    @spaces.GPU
-    def generate_image_with_bbox(prompt: str, negative_prompt: str,
-                               num_inference_steps: int, guidance_scale: float, 
-                               width: int, height: int, seed: int, use_seed: bool):
-        """使用边界框生成图像"""
-        global pipeline, current_bbox_data
+@spaces.GPU
+def generate_image_with_bbox(prompt: str, negative_prompt: str,
+                            num_inference_steps: int, guidance_scale: float, 
+                            width: int, height: int, seed: int, use_seed: bool):
+    """使用边界框生成图像"""
+    global pipeline, current_bbox_data
+    
+    if pipeline is None:
+        return None, "❌ 请先初始化DreamRenderer管道！"
+    
+    if not prompt.strip():
+        return None, "❌ 请输入提示词！"
+    
+    try:
+        # 设置种子
+        actual_seed = seed if use_seed else None
         
-        if pipeline is None:
-            return None, "❌ 请先初始化DreamRenderer管道！"
+        # 生成图像
+        image = pipeline.generate_image(
+            prompt=prompt,
+            bbox_data=current_bbox_data,
+            negative_prompt=negative_prompt,
+            num_inference_steps=num_inference_steps,
+            guidance_scale=guidance_scale,
+            width=width,
+            height=height,
+            seed=actual_seed
+        )
         
-        if not prompt.strip():
-            return None, "❌ 请输入提示词！"
+        info = f"✅ 图像生成成功！\n"
+        info += f"🔸 使用边界框: {len(current_bbox_data)}个\n"
+        info += f"🔸 推理步数: {num_inference_steps}\n"
+        info += f"🔸 引导强度: {guidance_scale}\n"
+        info += f"🔸 图像尺寸: {width}×{height}\n"
+        if actual_seed is not None:
+            info += f"🔸 随机种子: {actual_seed}"
         
-        try:
-            # 设置种子
-            actual_seed = seed if use_seed else None
-            
-            # 生成图像
-            image = pipeline.generate_image(
-                prompt=prompt,
-                bbox_data=current_bbox_data,
-                negative_prompt=negative_prompt,
-                num_inference_steps=num_inference_steps,
-                guidance_scale=guidance_scale,
-                width=width,
-                height=height,
-                seed=actual_seed
-            )
-            
-            info = f"✅ 图像生成成功！\n"
-            info += f"🔸 使用边界框: {len(current_bbox_data)}个\n"
-            info += f"🔸 推理步数: {num_inference_steps}\n"
-            info += f"🔸 引导强度: {guidance_scale}\n"
-            info += f"🔸 图像尺寸: {width}×{height}\n"
-            if actual_seed is not None:
-                info += f"🔸 随机种子: {actual_seed}"
-            
-            return image, info
-        except Exception as e:
-            return None, f"❌ 生成图像时出错: {str(e)}"
+        return image, info
+    except Exception as e:
+        return None, f"❌ 生成图像时出错: {str(e)}"
 
 def load_bbox_component():
     """加载边界框绘制组件"""