Update app.py

app.py

@@ -180,43 +180,53 @@ def adjust_size_to_multiple_of_8(width: int, height: int) -> tuple[int, int]:
 def calculate_dimensions(aspect_ratio: str, base_size: int = 512) -> tuple[int, int]:
     """선택된 비율에 따라 이미지 크기 계산"""
     if aspect_ratio == "1:1":
-        return base_size, base_size
+        width, height = base_size, base_size
     elif aspect_ratio == "16:9":
-        return base_size * 16 // 9, base_size
+        width, height = base_size * 16 // 9, base_size
     elif aspect_ratio == "9:16":
-        return base_size, base_size * 16 // 9
+        width, height = base_size, base_size * 16 // 9
     elif aspect_ratio == "4:3":
-        return base_size * 4 // 3, base_size
-    return base_size, base_size
+        width, height = base_size * 4 // 3, base_size
+    else:
+        width, height = base_size, base_size
+        
+    # 8의 배수로 조정
+    return adjust_size_to_multiple_of_8(width, height)
 
-@spaces.GPU(duration=20)  # 40초에서 20초로 감소
 def generate_background(prompt: str, aspect_ratio: str) -> Image.Image:
     try:
+        # 이미지 크기 계산 및 8의 배수로 조정
         width, height = calculate_dimensions(aspect_ratio)
-        width, height = adjust_size_to_multiple_of_8(width, height)
         
+        # 최대 크기 제한 적용
         max_size = 768
         if width > max_size or height > max_size:
             ratio = max_size / max(width, height)
             width = int(width * ratio)
             height = int(height * ratio)
-            
-        with torch.inference_mode():
-            image = pipe(
-                prompt=prompt,
-                width=width,
-                height=height,
-                num_inference_steps=8,
-                guidance_scale=4.0
-            ).images[0]
-            
-        return image
+            # 다시 8의 배수로 조정
+            width, height = adjust_size_to_multiple_of_8(width, height)
         
+        print(f"Generating background with size: {width}x{height}")
+        
+        with timer("Background generation"):
+            try:
+                with torch.inference_mode():
+                    image = pipe(
+                        prompt=prompt,
+                        width=width,
+                        height=height,
+                        num_inference_steps=8,
+                        guidance_scale=4.0
+                    ).images[0]
+            except Exception as e:
+                print(f"Pipeline error: {str(e)}")
+                return Image.new('RGB', (width, height), 'white')
+                
+        return image
     except Exception as e:
         print(f"Background generation error: {str(e)}")
-        return Image.new('RGB', (width, height), 'white')
-    finally:
-        clear_memory()
+        return Image.new('RGB', (512, 512), 'white')
 
 def create_position_grid():
     return """