diff --git a/nodes.py b/nodes.py
index 8e6825292..abb60675b 100644
--- a/nodes.py
+++ b/nodes.py
@@ -63,31 +63,22 @@ class ConditioningAverage :
     @classmethod
     def INPUT_TYPES(s):
         return {"required": {"conditioning_from": ("CONDITIONING", ), "conditioning_to": ("CONDITIONING", ),
-                              "conditioning_from_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1}),
-                              #"conditioning_to_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.1})
+                              "conditioning_from_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1})
                              }}
     RETURN_TYPES = ("CONDITIONING",)
     FUNCTION = "addWeighted"
 
     CATEGORY = "conditioning"
 
-    #def applyConditions(self, conditioning_from, conditioning_to, conditioning_from_strength, conditioning_to_strength):
-    #    c = []
-    #    for t in conditioning_from:
-    #        averaged = self.averageConditioning(t[0], conditioning_to, conditioning_from_strength, conditioning_to_strength)
-    #        n = [averaged, t[1].clone()]
-    #        c.append(n)
-    #    return (c, )
-
     def addWeighted(self, conditioning_from, conditioning_to, conditioning_from_strength):
         conditioning_to_strength = (1-conditioning_from_strength)
         conditioning_from_tensor = conditioning_from[0][0]
         conditioning_to_tensor = conditioning_to[0][0]
         output = conditioning_from
-        if conditioning_from_tensor.shape[1] > conditioning_to_tensor.shape[1]:
-            conditioning_to_tensor = torch.cat((conditioning_to_tensor, torch.zeros((1,conditioning_from_tensor.shape[1] - conditioning_to_tensor.shape[1],768))), dim=1)
+        if conditioning_from_tensor.shape[0] > conditioning_to_tensor.shape[1]:
+            conditioning_to_tensor = torch.cat((conditioning_to_tensor, torch.zeros((1, conditioning_from_tensor.shape[1] - conditioning_to_tensor.shape[1], conditioning_from_tensor.shape[1].value))), dim=1)
         elif conditioning_to_tensor.shape[1] > conditioning_from_tensor.shape[1]:
-            conditioning_from_tensor = torch.cat((conditioning_from_tensor, torch.zeros((conditioning_to_tensor.shape[1].value,conditioning_to_tensor.shape[1] - conditioning_from_tensor.shape[1],conditioning_from_tensor.shape[1].value))), dim=1)
+            conditioning_from_tensor = torch.cat((conditioning_from_tensor, torch.zeros((1, conditioning_to_tensor.shape[1] - conditioning_from_tensor.shape[1], conditioning_to_tensor.shape[1].value))), dim=1)
         output[0][0] = ((conditioning_from_tensor * conditioning_from_strength) + (conditioning_to_tensor * conditioning_to_strength))
         return (output, )