79 lines
2.8 KiB
Markdown
79 lines
2.8 KiB
Markdown
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# Documentation
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- Class name: FloatToMask
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- Category: KJNodes
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- Output node: False
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FloatToMask节点专门用于基于输入的浮点值生成掩码。每个输入值决定了掩码的强度或透明度。该节点设计用于创建具有指定尺寸的掩码批次，非常适合需要基于不同强度级别动态生成掩码的应用场景。
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# Input types
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## Required
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- input_values
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- 表示用于生成掩码的浮点值。列表中的每个值决定了批次中对应掩码的强度，影响其透明度或颜色深度。
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- Comfy dtype: FLOAT
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- Python dtype: List[float] or float
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- width
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- 指定生成掩码的宽度。这个尺寸决定了批次中每个掩码的宽度，直接影响掩码的大小和形状。
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- Comfy dtype: INT
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- Python dtype: int
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- height
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- 指定生成掩码的高度。这个尺寸决定了批次中每个掩码的高度，影响掩码的整体大小和形状。
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- Comfy dtype: INT
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- Python dtype: int
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# Output types
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- mask
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- 输出由基于输入浮点值生成的掩码批次组成，每个掩码具有指定的宽度和高度尺寸。这些掩码可用于各种需要自定义掩码生成的应用。
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- Comfy dtype: MASK
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- Python dtype: 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 FloatToMask:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"input_values": ("FLOAT", {"forceInput": True, "default": 0}),
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"width": ("INT", {"default": 100, "min": 1}),
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"height": ("INT", {"default": 100, "min": 1}),
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},
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}
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RETURN_TYPES = ("MASK",)
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FUNCTION = "execute"
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CATEGORY = "KJNodes"
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DESCRIPTION = """
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Generates a batch of masks based on the input float values.
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The batch size is determined by the length of the input float values.
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Each mask is generated with the specified width and height.
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"""
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def execute(self, input_values, width, height):
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import pandas as pd
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# Ensure input_values is a list
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if isinstance(input_values, (float, int)):
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input_values = [input_values]
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elif isinstance(input_values, pd.Series):
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input_values = input_values.tolist()
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elif isinstance(input_values, list) and all(isinstance(item, list) for item in input_values):
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input_values = [item for sublist in input_values for item in sublist]
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# Generate a batch of masks based on the input_values
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masks = []
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for value in input_values:
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# Assuming value is a float between 0 and 1 representing the mask's intensity
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mask = torch.ones((height, width), dtype=torch.float32) * value
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masks.append(mask)
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masks_out = torch.stack(masks, dim=0)
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return(masks_out,)
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```
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