diff --git a/image.py b/image.py
index 15ee324..1d7cdd0 100644
--- a/image.py
+++ b/image.py
@@ -237,6 +237,91 @@ class ImageCompositeFromMaskBatch:
 
         return (out, )
 
+class ImageComposite:
+    @classmethod
+    def INPUT_TYPES(s):
+        return {
+            "required": {
+                "destination": ("IMAGE",),
+                "source": ("IMAGE",),
+                "x": ("INT", { "default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1 }),
+                "y": ("INT", { "default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1 }),
+                "offset_x": ("INT", { "default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1 }),
+                "offset_y": ("INT", { "default": 0, "min": -MAX_RESOLUTION, "max": MAX_RESOLUTION, "step": 1 }),
+            },
+            "optional": {
+                "mask": ("MASK",),
+            }
+        }
+
+    RETURN_TYPES = ("IMAGE",)
+    FUNCTION = "execute"
+    CATEGORY = "essentials/image manipulation"
+
+    def execute(self, destination, source, x, y, offset_x, offset_y, mask=None):
+        if mask is None:
+            mask = torch.ones_like(source)[:,:,:,0]
+        
+        mask = mask.unsqueeze(-1).repeat(1, 1, 1, 3)
+
+        if mask.shape[1:3] != source.shape[1:3]:
+            mask = F.interpolate(mask.permute([0, 3, 1, 2]), size=(source.shape[1], source.shape[2]), mode='bicubic')
+            mask = mask.permute([0, 2, 3, 1])
+        
+        if mask.shape[0] > source.shape[0]:
+            mask = mask[:source.shape[0]]
+        elif mask.shape[0] < source.shape[0]:
+            mask = torch.cat((mask, mask[-1:].repeat((source.shape[0]-mask.shape[0], 1, 1, 1))), dim=0)
+        
+        if destination.shape[0] > source.shape[0]:
+            destination = destination[:source.shape[0]]
+        elif destination.shape[0] < source.shape[0]:
+            destination = torch.cat((destination, destination[-1:].repeat((source.shape[0]-destination.shape[0], 1, 1, 1))), dim=0)
+        
+        if not isinstance(x, list):
+            x = [x]
+        if not isinstance(y, list):
+            y = [y]
+        
+        if len(x) < destination.shape[0]:
+            x = x + [x[-1]] * (destination.shape[0] - len(x))
+        if len(y) < destination.shape[0]:
+            y = y + [y[-1]] * (destination.shape[0] - len(y))
+        
+        x = [i + offset_x for i in x]
+        y = [i + offset_y for i in y]
+
+        output = []
+        for i in range(destination.shape[0]):
+            d = destination[i].clone()
+            s = source[i]
+            m = mask[i]
+
+            if x[i]+source.shape[2] > destination.shape[2]:
+                s = s[:, :, :destination.shape[2]-x[i], :]
+                m = m[:, :, :destination.shape[2]-x[i], :]
+            if y[i]+source.shape[1] > destination.shape[1]:
+                s = s[:, :destination.shape[1]-y[i], :, :]
+                m = m[:destination.shape[1]-y[i], :, :]
+            
+            #output.append(s * m + d[y[i]:y[i]+s.shape[0], x[i]:x[i]+s.shape[1], :] * (1 - m))
+            d[y[i]:y[i]+s.shape[0], x[i]:x[i]+s.shape[1], :] = s * m + d[y[i]:y[i]+s.shape[0], x[i]:x[i]+s.shape[1], :] * (1 - m)
+            output.append(d)
+        
+        output = torch.stack(output)
+
+        # apply the source to the destination at XY position using the mask
+        #for i in range(destination.shape[0]):
+        #    output[i, y[i]:y[i]+source.shape[1], x[i]:x[i]+source.shape[2], :] = source * mask + destination[i, y[i]:y[i]+source.shape[1], x[i]:x[i]+source.shape[2], :] * (1 - mask)
+
+        #for x_, y_ in zip(x, y):
+        #    output[:, y_:y_+source.shape[1], x_:x_+source.shape[2], :] = source * mask + destination[:, y_:y_+source.shape[1], x_:x_+source.shape[2], :] * (1 - mask)
+
+        #output[:, y:y+source.shape[1], x:x+source.shape[2], :] = source * mask + destination[:, y:y+source.shape[1], x:x+source.shape[2], :] * (1 - mask)
+        #output = destination * (1 - mask) + source * mask
+
+        return (output,)
+
 class ImageResize:
     @classmethod
     def INPUT_TYPES(s):
@@ -1238,7 +1323,7 @@ class NoiseFromImage:
 
     RETURN_TYPES = ("IMAGE",)
     FUNCTION = "execute"
-    CATEGORY = "essentials"
+    CATEGORY = "essentials/image utils"
 
     def execute(self, image, noise_size, color_noise, mask_strength, mask_scale_diff, mask_contrast, noise_strenght, saturation, contrast, blur, noise_mask=None):
         torch.manual_seed(0)
@@ -1330,6 +1415,7 @@ IMAGE_CLASS_MAPPINGS = {
 
     # Image manipulation
     "ImageCompositeFromMaskBatch+": ImageCompositeFromMaskBatch,
+    "ImageComposite+": ImageComposite,
     "ImageCrop+": ImageCrop,
     "ImageFlip+": ImageFlip,
     "ImageRandomTransform+": ImageRandomTransform,
@@ -1371,6 +1457,7 @@ IMAGE_NAME_MAPPINGS = {
 
     # Image manipulation
     "ImageCompositeFromMaskBatch+": "🔧 Image Composite From Mask Batch",
+    "ImageComposite+": "🔧 Image Composite",
     "ImageCrop+": "🔧 Image Crop",
     "ImageFlip+": "🔧 Image Flip",
     "ImageRandomTransform+": "🔧 Image Random Transform",