mirrors/ComfyUI
1
0
Fork 0
mirror of https://github.com/comfyanonymous/ComfyUI.git synced 2025-09-12 04:27:21 +00:00
Code Issues Packages Projects Releases Wiki Activity

removed comments and prints. Attempted to apply to every cond in list again but no luck

Browse Source
This commit is contained in:
FizzleDorf
2023-04-29 16:02:24 -04:00
parent 03a08e86d3
commit c837e492c9
1 changed files with 4 additions and 13 deletions
Download Patch File Download Diff File Expand all files Collapse all files

17
nodes.py
View File

@@ -63,31 +63,22 @@ class ConditioningAverage :
@classmethod
def INPUT_TYPES(s):
return {"required": {"conditioning_from": ("CONDITIONING", ), "conditioning_to": ("CONDITIONING", ),
"conditioning_from_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1}),
#"conditioning_to_strength": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 10.0, "step": 0.1})
"conditioning_from_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.1})
}}
RETURN_TYPES = ("CONDITIONING",)
FUNCTION = "addWeighted"
CATEGORY = "conditioning"
#def applyConditions(self, conditioning_from, conditioning_to, conditioning_from_strength, conditioning_to_strength):
# c = []
# for t in conditioning_from:
# averaged = self.averageConditioning(t[0], conditioning_to, conditioning_from_strength, conditioning_to_strength)
# n = [averaged, t[1].clone()]
# c.append(n)
# return (c, )
def addWeighted(self, conditioning_from, conditioning_to, conditioning_from_strength):
conditioning_to_strength = (1-conditioning_from_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)
if conditioning_from_tensor.shape[0] > 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]:
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)
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))
return (output, )