fixed repeating frames after batch using deepcopy

nodes.py

@@ -1,3 +1,5 @@
+from queue import Empty
+from turtle import clone
 import torch
 
 import os
@@ -5,9 +7,11 @@ import sys
 import json
 import hashlib
 import traceback
+import copy
 
 from PIL import Image
 from PIL.PngImagePlugin import PngInfo
+from copy import deepcopy
 import numpy as np
 
 
@@ -74,12 +78,15 @@ class ConditioningAverage :
         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[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)
+        
+        output = copy.deepcopy(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], 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((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, )
 
 class ConditioningSetArea: