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FizzleDorf 2023-04-13 03:54:26 -04:00
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commit 77a6c6c861
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nodes.py
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@ -58,6 +58,30 @@ class ConditioningCombine:
def combine(self, conditioning_1, conditioning_2): def combine(self, conditioning_1, conditioning_2):
return (conditioning_1 + conditioning_2, ) return (conditioning_1 + conditioning_2, )
class ConditioningAddWeighted:
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", ),
"conditioning_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1}),
"conditioning_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1})
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "addWeighted"
CATEGORY = "conditioning"
def addWeighted(self, conditioning_1, conditioning_2, conditioning_1_strength, conditioning_2_strength):
conditioning_1_tensor = conditioning_1[0][0]
conditioning_2_tensor = conditioning_2[0][0]
output = conditioning_1
if conditioning_1_tensor.shape[1] > conditioning_2_tensor.shape[1]:
conditioning_2_tensor = torch.cat((conditioning_2_tensor, torch.zeros((1,conditioning_1_tensor.shape[1] - conditioning_2_tensor.shape[1],768))), dim=1)
elif conditioning_2_tensor.shape[1] > conditioning_1_tensor.shape[1]:
conditioning_1_tensor = torch.cat((conditioning_1_tensor, torch.zeros((1,conditioning_2_tensor.shape[1] - conditioning_1_tensor.shape[1],768))), dim=1)
output[0][0] = ((conditioning_1_tensor * conditioning_1_strength) + (conditioning_2_tensor * conditioning_2_strength))/(conditioning_1_strength + conditioning_2_strength)
return (output, )
class ConditioningSetArea: class ConditioningSetArea:
@classmethod @classmethod
def INPUT_TYPES(s): def INPUT_TYPES(s):