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parameters renamed for clarity

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FizzleDorf
2023-04-13 11:10:05 -04:00
parent e4f9e5ae90
commit 41a5cd83b3
1 changed files with 12 additions and 12 deletions
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nodes.py
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@@ -61,9 +61,9 @@ class ConditioningCombine:
class ConditioningAddWeighted:
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning_1": ("CONDITIONING", ), "conditioning_2": ("CONDITIONING", ),
"conditioning_1_strength": ("FLOAT", {"default": 10.0, "min": 0.0, "max": 10.0, "step": 0.1}),
"conditioning_2_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.1})
return {"required": {"conditioning_from": ("CONDITIONING", ), "conditioning_to": ("CONDITIONING", ),
"conditioning_from_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.1}),
"conditioning_to_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.1})
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "addWeighted"
@@ -71,15 +71,15 @@ class ConditioningAddWeighted:
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((conditioning_2_tensor.shape[1].value,conditioning_2_tensor.shape[1] - conditioning_1_tensor.shape[1],conditioning_1_tensor.shape[1].value))), dim=1)
output[0][0] = ((conditioning_1_tensor * conditioning_1_strength) + (conditioning_2_tensor * conditioning_2_strength))
def addWeighted(self, conditioning_from, conditioning_to, conditioning_from_strength, conditioning_to_strength):
conditioning_from_tensor = conditioning_from[0][0]
conditioning_to_tensor = conditioning_to[0][0]
output = 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],768))), dim=1)
elif conditioning_to_tensor.shape[1] > conditioning_from_tensor.shape[1]:
conditioning_from_tensor = torch.cat((conditioning_from_tensor, torch.zeros((conditioning_to_tensor.shape[1].value,conditioning_to_tensor.shape[1] - conditioning_from_tensor.shape[1],conditioning_from_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: