127 lines
5.5 KiB
Markdown
127 lines
5.5 KiB
Markdown
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# Documentation
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- Class name: ConditioningSetMaskAndCombine5
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- Category: KJNodes/masking/conditioning
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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ConditioningSetMaskAndCombine5节点用于应用蒙版到条件数据，并以不同强度组合多组条件和蒙版。它允许根据应用的蒙版有选择地增强或抑制条件数据中的特征，从而实现生成模型的复杂条件设置场景。此节点通过对多组输入进行蒙版和组合，为生成过程提供了精细的控制。
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# Input types
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## Required
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- positive_i
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- 指定一组要应用蒙版并组合的正面条件数据。应用蒙版的强度会影响这些特征在最终结果中的表现程度。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[torch.Tensor]
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- negative_i
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- 指定一组要应用蒙版并组合的负面条件数据。应用蒙版的强度会影响这些特征在最终结果中被抑制的程度。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[torch.Tensor]
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- mask_i
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- 要应用于条件数据集的蒙版，决定影响的区域。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- mask_i_strength
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- 定义应用于条件数据集的蒙版强度，影响特征增强或抑制的程度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- set_cond_area
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- 决定是否将条件区域设置为蒙版定义的边界，允许对条件效果进行更精确的控制。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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# Output types
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- combined_positive
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- 蒙版应用后的组合正面条件数据集，可用于进一步的生成过程。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[torch.Tensor]
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- combined_negative
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- 蒙版应用后的组合负面条件数据集，指示生成模型中被抑制的特征。
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- Comfy dtype: CONDITIONING
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- Python dtype: List[torch.Tensor]
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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 ConditioningSetMaskAndCombine5:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"positive_1": ("CONDITIONING", ),
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"negative_1": ("CONDITIONING", ),
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"positive_2": ("CONDITIONING", ),
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"negative_2": ("CONDITIONING", ),
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"positive_3": ("CONDITIONING", ),
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"negative_3": ("CONDITIONING", ),
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"positive_4": ("CONDITIONING", ),
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"negative_4": ("CONDITIONING", ),
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"positive_5": ("CONDITIONING", ),
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"negative_5": ("CONDITIONING", ),
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"mask_1": ("MASK", ),
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"mask_2": ("MASK", ),
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"mask_3": ("MASK", ),
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"mask_4": ("MASK", ),
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"mask_5": ("MASK", ),
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"mask_1_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"mask_2_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"mask_3_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"mask_4_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"mask_5_strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 10.0, "step": 0.01}),
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"set_cond_area": (["default", "mask bounds"],),
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}
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}
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RETURN_TYPES = ("CONDITIONING","CONDITIONING",)
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RETURN_NAMES = ("combined_positive", "combined_negative",)
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FUNCTION = "append"
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CATEGORY = "KJNodes/masking/conditioning"
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DESCRIPTION = """
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Bundles multiple conditioning mask and combine nodes into one,functionality is identical to ComfyUI native nodes
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"""
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def append(self, positive_1, negative_1, positive_2, positive_3, positive_4, positive_5, negative_2, negative_3, negative_4, negative_5, mask_1, mask_2, mask_3, mask_4, mask_5, set_cond_area, mask_1_strength, mask_2_strength, mask_3_strength, mask_4_strength, mask_5_strength):
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c = []
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c2 = []
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set_area_to_bounds = False
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if set_cond_area != "default":
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set_area_to_bounds = True
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if len(mask_1.shape) < 3:
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mask_1 = mask_1.unsqueeze(0)
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if len(mask_2.shape) < 3:
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mask_2 = mask_2.unsqueeze(0)
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if len(mask_3.shape) < 3:
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mask_3 = mask_3.unsqueeze(0)
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if len(mask_4.shape) < 3:
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mask_4 = mask_4.unsqueeze(0)
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if len(mask_5.shape) < 3:
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mask_5 = mask_5.unsqueeze(0)
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for t in positive_1:
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append_helper(t, mask_1, c, set_area_to_bounds, mask_1_strength)
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for t in positive_2:
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append_helper(t, mask_2, c, set_area_to_bounds, mask_2_strength)
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for t in positive_3:
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append_helper(t, mask_3, c, set_area_to_bounds, mask_3_strength)
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for t in positive_4:
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append_helper(t, mask_4, c, set_area_to_bounds, mask_4_strength)
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for t in positive_5:
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append_helper(t, mask_5, c, set_area_to_bounds, mask_5_strength)
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for t in negative_1:
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append_helper(t, mask_1, c2, set_area_to_bounds, mask_1_strength)
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for t in negative_2:
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append_helper(t, mask_2, c2, set_area_to_bounds, mask_2_strength)
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for t in negative_3:
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append_helper(t, mask_3, c2, set_area_to_bounds, mask_3_strength)
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for t in negative_4:
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append_helper(t, mask_4, c2, set_area_to_bounds, mask_4_strength)
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for t in negative_5:
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append_helper(t, mask_5, c2, set_area_to_bounds, mask_5_strength)
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return (c, c2)
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```
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