51 lines
2.2 KiB
Markdown
51 lines
2.2 KiB
Markdown
# Documentation
|
|
- Class name: ConditioningConcat
|
|
- Category: conditioning
|
|
- Output node: False
|
|
- Repo Ref: https://github.com/comfyanonymous/ComfyUI
|
|
|
|
ConditioningConcat节点旨在将两个条件输入合并为单个输出。它巧妙地将源条件输入与目标条件输入沿指定维度连接起来，确保生成的输出非常适合神经网络架构中的后续处理步骤。
|
|
|
|
# Input types
|
|
## Required
|
|
- conditioning_to
|
|
- 'conditioning_to'参数表示将与另一个条件输入连接的目标条件输入。它在确定最终输出的结构中起着关键作用，因为它决定了附加额外条件信息的基础。
|
|
- Comfy dtype: CONDITIONING
|
|
- Python dtype: List[Tuple[torch.Tensor, Any]]
|
|
- conditioning_from
|
|
- 'conditioning_from'参数提供了将与'conditioning_to'结合的源条件输入。它很重要，因为它为最终连接的输出贡献了额外的上下文或特征，可能增强模型做出明智预测的能力。
|
|
- Comfy dtype: CONDITIONING
|
|
- Python dtype: List[Tuple[torch.Tensor, Any]]
|
|
|
|
# Output types
|
|
- output
|
|
- ConditioningConcat节点的输出是合并后的条件张量，整合了'conditioning_to'和'conditioning_from'输入的元素。这个输出被策略性地构建，以与下游神经网络操作兼容。
|
|
- Comfy dtype: CONDITIONING
|
|
- Python dtype: List[Tuple[torch.Tensor, Any]]
|
|
|
|
# Usage tips
|
|
- Infra type: CPU
|
|
|
|
# Source code
|
|
```
|
|
class ConditioningConcat:
|
|
|
|
@classmethod
|
|
def INPUT_TYPES(s):
|
|
return {'required': {'conditioning_to': ('CONDITIONING',), 'conditioning_from': ('CONDITIONING',)}}
|
|
RETURN_TYPES = ('CONDITIONING',)
|
|
FUNCTION = 'concat'
|
|
CATEGORY = 'conditioning'
|
|
|
|
def concat(self, conditioning_to, conditioning_from):
|
|
out = []
|
|
if len(conditioning_from) > 1:
|
|
logging.warning('Warning: ConditioningConcat conditioning_from contains more than 1 cond, only the first one will actually be applied to conditioning_to.')
|
|
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)
|
|
return (out,)
|
|
``` |