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ConditioningAverage now also averages the pooled output.

This commit is contained in:
comfyanonymous 2023-07-03 21:44:37 -04:00
parent d94ddd8548
commit 3a09fac835
1 changed files with 9 additions and 1 deletions
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10
nodes.py
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@ -82,15 +82,23 @@ class ConditioningAverage :
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, )