57 lines
1.6 KiB
Markdown
57 lines
1.6 KiB
Markdown
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# Documentation
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- Class name: JWMaskSequenceJoin
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- Category: jamesWalker55
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- Output node: False
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该节点旨在将两个掩码序列合并成一个连接的掩码序列。它通过在指定维度上合并输入的掩码序列来操作，有效地将它们组合成一个统一的序列，可用于进一步的处理或分析。
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# Input types
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## Required
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- mask_sequence_i
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- 该参数是输入的掩码序列，将与另一个掩码序列合并。它是节点操作的主要对象，直接影响输出结果的内容和结构。
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- Comfy dtype: MASK_SEQUENCE
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- Python dtype: unknown
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# Output types
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- mask_sequence
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- 输出的mask_sequence是通过连接两个输入掩码序列得到的结果。这个统一的序列可以用于后续需要组合视图的掩码操作。
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- Comfy dtype: MASK_SEQUENCE
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- Python dtype: torch.Tensor
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## Usage tips
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- Infra type: `GPU`
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- Common nodes: unknown
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## Source code
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```python
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@register_node("JWMaskSequenceFromMask", "Mask Sequence From Mask")
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class _:
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CATEGORY = "jamesWalker55"
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INPUT_TYPES = lambda: {
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"required": {
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"mask": ("MASK",),
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"batch_size": ("INT", {"default": 1, "min": 1, "step": 1}),
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}
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}
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RETURN_TYPES = ("MASK_SEQUENCE",)
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FUNCTION = "execute"
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def execute(
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self,
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mask: torch.Tensor,
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batch_size: int,
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):
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assert isinstance(mask, torch.Tensor)
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assert isinstance(batch_size, int)
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assert len(mask.shape) == 2
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mask_seq = mask.reshape((1, 1, *mask.shape))
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mask_seq = mask_seq.repeat(batch_size, 1, 1, 1)
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return (mask_seq,)
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```
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