ConditioningAverage now also averages the pooled output.

nodes.py

@@ -82,15 +82,23 @@ def addWeighted(self, conditioning_to, conditioning_from, conditioning_to_streng
             print("Warning: ConditioningAverage conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.")
 
         cond_from = conditioning_from[0][0]
+        pooled_output_from = conditioning_from[0][1].get("pooled_output", None)
 
         for i in range(len(conditioning_to)):
             t1 = conditioning_to[i][0]
+            pooled_output_to = conditioning_to[i][1].get("pooled_output", pooled_output_from)
             t0 = cond_from[:,:t1.shape[1]]
             if t0.shape[1] < t1.shape[1]:
                 t0 = torch.cat([t0] + [torch.zeros((1, (t1.shape[1] - t0.shape[1]), t1.shape[2]))], dim=1)
 
             tw = torch.mul(t1, conditioning_to_strength) + torch.mul(t0, (1.0 - conditioning_to_strength))
-            n = [tw, conditioning_to[i][1].copy()]
+            t_to = conditioning_to[i][1].copy()
+            if pooled_output_from is not None and pooled_output_to is not None:
+                t_to["pooled_output"] = torch.mul(pooled_output_to, conditioning_to_strength) + torch.mul(pooled_output_from, (1.0 - conditioning_to_strength))
+            elif pooled_output_from is not None:
+                t_to["pooled_output"] = pooled_output_from
+
+            n = [tw, t_to]
             out.append(n)
         return (out, )