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fixed repeating frames after batch using deepcopy

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FizzleDorf 2023-04-30 13:33:08 -04:00
parent c837e492c9
commit 1086d6a0e1
1 changed files with 10 additions and 3 deletions
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13
nodes.py
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@ -1,3 +1,5 @@
from queue import Empty
from turtle import clone
import torch import torch
import os import os
@ -5,9 +7,11 @@ import sys
import json import json
import hashlib import hashlib
import traceback import traceback
import copy
from PIL import Image from PIL import Image
from PIL.PngImagePlugin import PngInfo from PIL.PngImagePlugin import PngInfo
from copy import deepcopy
import numpy as np import numpy as np
@ -74,12 +78,15 @@ class ConditioningAverage :
conditioning_to_strength = (1-conditioning_from_strength) conditioning_to_strength = (1-conditioning_from_strength)
conditioning_from_tensor = conditioning_from[0][0] conditioning_from_tensor = conditioning_from[0][0]
conditioning_to_tensor = conditioning_to[0][0] conditioning_to_tensor = conditioning_to[0][0]
output = conditioning_from
if conditioning_from_tensor.shape[0] > conditioning_to_tensor.shape[1]: output = copy.deepcopy(conditioning_from)
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)
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]: 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) 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)) output[0][0] = ((conditioning_from_tensor * conditioning_from_strength) + (conditioning_to_tensor * conditioning_to_strength))
return (output, ) return (output, )
class ConditioningSetArea: class ConditioningSetArea: