71 lines
2.7 KiB
Markdown
71 lines
2.7 KiB
Markdown
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# Documentation
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- Class name: Apply ControlNet Stack
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- Category: Efficiency Nodes/Stackers
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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Apply ControlNet Stack节点旨在通过应用一系列控制网络来增强正面和负面提示的条件。这个过程允许根据指定的控制网络、图像和强度参数动态调整提示，从而优化条件以实现更有针对性和更有效的生成结果。
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# Input types
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## Required
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- positive
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- 需要调节的正面提示。它作为控制网络堆栈应用的初始输入，影响生成过程朝向期望的属性或主题。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[str, Dict]]
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- negative
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- 需要调节的负面提示。它作为正面提示的对应项，引导生成过程远离不期望的属性或主题。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[str, Dict]]
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## Optional
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- cnet_stack
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- 一个可选的控制网络堆栈及相关参数，按顺序应用于正面和负面提示。这个堆栈能够实现复杂的、多层次的条件调整。
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- Comfy dtype: CONTROL_NET_STACK
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- Python dtype: Optional[List[Tuple[ControlNet, torch.Tensor, float, float, float]]]
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# Output types
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- CONDITIONING+
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- 经过控制网络堆栈应用后的条件化正面提示。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[str, Dict]]
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- CONDITIONING-
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- 经过控制网络堆栈应用后的条件化负面提示。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[Tuple[str, Dict]]
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## Usage tips
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- Infra type: `CPU`
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- Common nodes: unknown
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## Source code
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```python
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class TSC_Apply_ControlNet_Stack:
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@classmethod
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def INPUT_TYPES(cls):
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return {"required": {"positive": ("CONDITIONING",),
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"negative": ("CONDITIONING",)},
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"optional": {"cnet_stack": ("CONTROL_NET_STACK",)}
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}
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RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
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RETURN_NAMES = ("CONDITIONING+","CONDITIONING-",)
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FUNCTION = "apply_cnet_stack"
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CATEGORY = "Efficiency Nodes/Stackers"
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def apply_cnet_stack(self, positive, negative, cnet_stack=None):
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if cnet_stack is None:
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return (positive, negative)
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for control_net_tuple in cnet_stack:
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control_net, image, strength, start_percent, end_percent = control_net_tuple
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controlnet_conditioning = ControlNetApplyAdvanced().apply_controlnet(positive, negative, control_net, image,
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strength, start_percent, end_percent)
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positive, negative = controlnet_conditioning[0], controlnet_conditioning[1]
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return (positive, negative, )
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```
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