Concat Conditionings¶
Documentation¶
- Class name:
ImpactConcatConditionings
- Category:
ImpactPack/Util
- Output node:
False
The ImpactConcatConditionings node is designed to concatenate multiple conditioning inputs into a single conditioning output. This process involves merging the features of the given conditionings, facilitating the combination of different conditioning elements for enhanced model input preparation.
Input types¶
Required¶
conditioning1
- The primary conditioning input to which additional conditionings will be concatenated. It serves as the base for the concatenation process.
- Comfy dtype:
CONDITIONING
- Python dtype:
List[Tuple[torch.Tensor, Any]]
Output types¶
conditioning
- Comfy dtype:
CONDITIONING
- The result of concatenating the given conditionings into a single, unified conditioning output.
- Python dtype:
List[Tuple[torch.Tensor, Any]]
- Comfy dtype:
Usage tips¶
- Infra type:
GPU
- Common nodes: unknown
Source code¶
class ConcatConditionings:
@classmethod
def INPUT_TYPES(s):
return {"required": {
"conditioning1": ("CONDITIONING", ),
},
}
RETURN_TYPES = ("CONDITIONING", )
FUNCTION = "doit"
CATEGORY = "ImpactPack/Util"
def doit(self, **kwargs):
conditioning_to = list(kwargs.values())[0]
for k, conditioning_from in list(kwargs.items())[1:]:
out = []
if len(conditioning_from) > 1:
print("Warning: ConcatConditionings {k} contains more than 1 cond, only the first one will actually be applied to conditioning1.")
cond_from = conditioning_from[0][0]
for i in range(len(conditioning_to)):
t1 = conditioning_to[i][0]
tw = torch.cat((t1, cond_from), 1)
n = [tw, conditioning_to[i][1].copy()]
out.append(n)
conditioning_to = out
return (out, )