diff --git a/nodes/image_nodes.py b/nodes/image_nodes.py
index 08e12ef..1e45b7e 100644
--- a/nodes/image_nodes.py
+++ b/nodes/image_nodes.py
@@ -217,13 +217,13 @@ class ImageConcanate:
         }}
 
     RETURN_TYPES = ("IMAGE",)
-    FUNCTION = "concanate"
+    FUNCTION = "concatenate"
     CATEGORY = "KJNodes/image"
     DESCRIPTION = """
 Concatenates the image2 to image1 in the specified direction.
 """
 
-    def concanate(self, image1, image2, direction, match_image_size, first_image_shape=None):
+    def concatenate(self, image1, image2, direction, match_image_size, first_image_shape=None):
         # Check if the batch sizes are different
         batch_size1 = image1.shape[0]
         batch_size2 = image2.shape[0]
@@ -231,12 +231,16 @@ Concatenates the image2 to image1 in the specified direction.
         if batch_size1 != batch_size2:
             # Calculate the number of repetitions needed
             max_batch_size = max(batch_size1, batch_size2)
-            repeats1 = max_batch_size // batch_size1
-            repeats2 = max_batch_size // batch_size2
+            repeats1 = max_batch_size - batch_size1
+            repeats2 = max_batch_size - batch_size2
             
-            # Repeat the images to match the largest batch size
-            image1 = image1.repeat(repeats1, 1, 1, 1)
-            image2 = image2.repeat(repeats2, 1, 1, 1)
+            # Repeat the last image to match the largest batch size
+            if repeats1 > 0:
+                last_image1 = image1[-1].unsqueeze(0).repeat(repeats1, 1, 1, 1)
+                image1 = torch.cat([image1, last_image1], dim=0)
+            if repeats2 > 0:
+                last_image2 = image2[-1].unsqueeze(0).repeat(repeats2, 1, 1, 1)
+                image2 = torch.cat([image2, last_image2], dim=0)
 
         if match_image_size:
             # Use first_image_shape if provided; otherwise, default to image1's shape
@@ -1845,7 +1849,7 @@ with the **inputcount** and clicking update.
             first_image_shape = image.shape
         for c in range(1, inputcount):
             new_image = kwargs[f"image_{c + 1}"]
-            image, = ImageConcanate.concanate(self, image, new_image, direction, match_image_size, first_image_shape=first_image_shape)
+            image, = ImageConcanate.concatenate(self, image, new_image, direction, match_image_size, first_image_shape=first_image_shape)
         first_image_shape = None
         return (image,)
 
diff --git a/nodes/model_optimization_nodes.py b/nodes/model_optimization_nodes.py
index 43fb7de..410268e 100644
--- a/nodes/model_optimization_nodes.py
+++ b/nodes/model_optimization_nodes.py
@@ -475,6 +475,7 @@ class TorchCompileCosmosModel:
                     "fullgraph": ("BOOLEAN", {"default": False, "tooltip": "Enable full graph mode"}),
                     "mode": (["default", "max-autotune", "max-autotune-no-cudagraphs", "reduce-overhead"], {"default": "default"}),
                     "dynamic": ("BOOLEAN", {"default": False, "tooltip": "Enable dynamic mode"}),
+                    "dynamo_cache_size_limit": ("INT", {"default": 64, "tooltip": "Set the dynamo cache size limit"}),
                 }}
     RETURN_TYPES = ("MODEL",)
     FUNCTION = "patch"
@@ -482,10 +483,11 @@ class TorchCompileCosmosModel:
     CATEGORY = "KJNodes/experimental"
     EXPERIMENTAL = True
 
-    def patch(self, model, backend, mode, fullgraph, dynamic):
+    def patch(self, model, backend, mode, fullgraph, dynamic, dynamo_cache_size_limit):
         
         m = model.clone()
         diffusion_model = m.get_model_object("diffusion_model")
+        torch._dynamo.config.cache_size_limit = dynamo_cache_size_limit
         
         if not self._compiled:
             try: