diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index 817069615..ec3395060 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -270,6 +270,7 @@ class Rotate:
                     "Bilinear",
                     "Bicubic",
                 ],),
+                "expand": (["disabled", "enabled"],),
                 "fill_color": ("STRING", {"default": "#000000"}),
             },
         }
@@ -279,9 +280,8 @@ class Rotate:
 
     CATEGORY = "image/postprocessing"
 
-    def rotate(self, image: torch.Tensor, angle: int, resample: str, fill_color: str):
-        batch_size, height, width, _ = image.shape
-        result = torch.zeros_like(image)
+    def rotate(self, image: torch.Tensor, angle: int, resample: str, expand: str, fill_color: str):
+        batch_size, _, _, _ = image.shape
 
         resamplers = {
             "Nearest Neighbor": Image.Resampling.NEAREST,
@@ -289,29 +289,30 @@ class Rotate:
             "Bicubic": Image.Resampling.BICUBIC,
         }
 
-        for b in range(batch_size):
-            tensor_image = image[b]
-            img = (tensor_image * 255).to(torch.uint8).numpy()
-            pil_image = Image.fromarray(img, mode='RGB')
-            
-            fill_color = fill_color or "#000000"
+        tensor_image = image[0]
+        img = (tensor_image * 255).to(torch.uint8).numpy()
+        pil_image = Image.fromarray(img, mode='RGB')
+        
+        expand = True if expand == "enabled" else False
+        fill_color = fill_color or "#000000"
 
-            def parse_palette(color_str):
-                if re.match(r'^#[a-fA-F0-9]{6}$', color_str) or color_str.lower() in ImageColor.colormap:
-                    return ImageColor.getrgb(color_str)
+        def parse_palette(color_str):
+            if re.match(r'^#[a-fA-F0-9]{6}$', color_str) or color_str.lower() in ImageColor.colormap:
+                return ImageColor.getrgb(color_str)
 
-                color_rgb = re.match(r'^\(?(\d{1,3}),(\d{1,3}),(\d{1,3})\)?$', color_str)
-                if color_rgb and int(color_rgb.group(1)) <= 255 and int(color_rgb.group(2)) <= 255 and int(color_rgb.group(3)) <= 255:
-                    return tuple(map(int, re.findall(r'\d{1,3}', color_str)))
-                else:
-                    raise ValueError(f"Invalid color format: {color_str}")
+            color_rgb = re.match(r'^\(?(\d{1,3}),(\d{1,3}),(\d{1,3})\)?$', color_str)
+            if color_rgb and int(color_rgb.group(1)) <= 255 and int(color_rgb.group(2)) <= 255 and int(color_rgb.group(3)) <= 255:
+                return tuple(map(int, re.findall(r'\d{1,3}', color_str)))
+            else:
+                raise ValueError(f"Invalid color format: {color_str}")
 
-            color = fill_color.replace(" ", "")
-            color = parse_palette(color)
-            rotated_image = pil_image.rotate(angle=angle, resample=resamplers[resample], expand=False, fillcolor=color)
-
-            rotated_array = torch.tensor(np.array(rotated_image.convert("RGB"))).float() / 255
-            result[b] = rotated_array
+        color = fill_color.replace(" ", "")
+        color = parse_palette(color)
+        rotated_image = pil_image.rotate(angle=angle, resample=resamplers[resample], expand=expand, fillcolor=color)
+        height, width = rotated_image.size
+        result = torch.zeros(batch_size, height, width, 3)
+        rotated_array = torch.tensor(np.array(rotated_image.convert("RGB"))).float() / 255
+        result[0] = rotated_array
 
         return (result,)