diff --git a/comfy_extras/nodes_post_processing.py b/comfy_extras/nodes_post_processing.py
index cbc9bf158..33af1595f 100644
--- a/comfy_extras/nodes_post_processing.py
+++ b/comfy_extras/nodes_post_processing.py
@@ -266,11 +266,7 @@ class Rotate:
                     "max": 360,
                     "step": 0.1
                 }),
-                "resample": ([
-                    "Nearest Neighbor",
-                    "Bilinear",
-                    "Bicubic",
-                ],),
+                "resample": (["nearest neighbor", "bilinear", "bicubic"],),
                 "expand": (["disabled", "enabled"],),
                 "center_x": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
                 "center_y": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
@@ -290,9 +286,9 @@ class Rotate:
         batch_size, height, width, _ = image.shape
 
         resamplers = {
-            "Nearest Neighbor": Image.Resampling.NEAREST,
-            "Bilinear": Image.Resampling.BILINEAR,
-            "Bicubic": Image.Resampling.BICUBIC,
+            "nearest neighbor": Image.Resampling.NEAREST,
+            "bilinear": Image.Resampling.BILINEAR,
+            "bicubic": Image.Resampling.BICUBIC,
         }
 
         tensor_image = image[0]
@@ -430,6 +426,7 @@ class Composite:
                 "image_b": ("IMAGE",),
                 "x": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
                 "y": ("INT", {"default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION}),
+                "resample": (["nearest neighbor", "box", "bilinear", "bicubic", "hamming", "lanczos"],),
             },
             "optional": {
                 "mask": ("MASK",),
@@ -441,7 +438,16 @@ class Composite:
 
     CATEGORY = "image/postprocessing"
 
-    def composite(self, image_a: torch.Tensor, image_b: torch.Tensor, x: int, y: int, mask: torch.Tensor = None):
+    def composite(self, image_a: torch.Tensor, image_b: torch.Tensor, x: int, y: int, resample: str, mask: torch.Tensor = None):
+        resamplers = {
+            "nearest neighbor": Image.Resampling.NEAREST,
+            "bilinear": Image.Resampling.BILINEAR,
+            "bicubic": Image.Resampling.BICUBIC,
+            "box": Image.Resampling.BOX,
+            "hamming": Image.Resampling.HAMMING,
+            "lanczos": Image.Resampling.LANCZOS,
+        }
+
         batch_size, height, width, _ = image_a.shape
         result = torch.zeros_like(image_a)
 
@@ -455,6 +461,8 @@ class Composite:
             pil_image_a = Image.fromarray(img_a, mode='RGB')
             pil_image_b = Image.fromarray(img_b, mode='RGB')
             pil_image_mask = Image.fromarray(img_mask, mode='L')
+            if pil_image_mask.size != pil_image_b.size:
+                pil_image_mask = pil_image_mask.resize(pil_image_b.size, resamplers[resample])
 
             pil_image_a.paste(pil_image_b, (x, y), pil_image_mask)