161 lines
6.3 KiB
Markdown
161 lines
6.3 KiB
Markdown
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# Documentation
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- Class name: OffsetMask
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- Category: KJNodes/masking
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- Output node: False
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OffsetMask节点旨在通过应用指定的偏移量、旋转和复制因子来操纵和转换蒙版。它能够创建给定蒙版的多个变体，允许动态调整位置和方向，这在图像处理和增强任务中特别有用。
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# Input types
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## Required
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- mask
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- 要进行转换的输入蒙版或蒙版批次。这个参数是节点操作的核心，作为所有后续转换的基础。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- x
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- 指定要应用于蒙版的水平偏移量。这个参数控制蒙版的横向位移。
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- Comfy dtype: INT
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- Python dtype: int
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- y
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- 指定要应用于蒙版的垂直偏移量。这个参数控制蒙版的纵向位移。
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- Comfy dtype: INT
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- Python dtype: int
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- angle
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- 定义旋转蒙版的角度（以度为单位）。这个参数允许对蒙版进行旋转调整。
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- Comfy dtype: INT
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- Python dtype: int
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- duplication_factor
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- 蒙版被复制以形成批次的次数。这个参数允许从单个输入创建多个蒙版变体。
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- Comfy dtype: INT
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- Python dtype: int
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- roll
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- 确定在偏移过程中是否应用边缘包裹。这个布尔参数影响蒙版在其边界处的操作方式。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- incremental
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- 指示是否应该逐步应用偏移。这个布尔参数影响蒙版转换的方法。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- padding_mode
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- 指定在转换蒙版时要使用的填充模式。这个参数影响在偏移过程中如何处理蒙版的边缘。
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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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- mask
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- 应用指定的偏移量、旋转和复制因子后的转换后的蒙版或蒙版批次。
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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 OffsetMask:
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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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"mask": ("MASK",),
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"x": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
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"y": ("INT", { "default": 0, "min": -4096, "max": MAX_RESOLUTION, "step": 1, "display": "number" }),
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"angle": ("INT", { "default": 0, "min": -360, "max": 360, "step": 1, "display": "number" }),
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"duplication_factor": ("INT", { "default": 1, "min": 1, "max": 1000, "step": 1, "display": "number" }),
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"roll": ("BOOLEAN", { "default": False }),
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"incremental": ("BOOLEAN", { "default": False }),
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"padding_mode": (
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[
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'empty',
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'border',
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'reflection',
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], {
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"default": 'empty'
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}),
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}
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}
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RETURN_TYPES = ("MASK",)
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RETURN_NAMES = ("mask",)
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FUNCTION = "offset"
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CATEGORY = "KJNodes/masking"
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DESCRIPTION = """
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Offsets the mask by the specified amount.
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- mask: Input mask or mask batch
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- x: Horizontal offset
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- y: Vertical offset
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- angle: Angle in degrees
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- roll: roll edge wrapping
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- duplication_factor: Number of times to duplicate the mask to form a batch
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- border padding_mode: Padding mode for the mask
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"""
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def offset(self, mask, x, y, angle, roll=False, incremental=False, duplication_factor=1, padding_mode="empty"):
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# Create duplicates of the mask batch
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mask = mask.repeat(duplication_factor, 1, 1).clone()
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batch_size, height, width = mask.shape
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if angle != 0 and incremental:
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for i in range(batch_size):
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rotation_angle = angle * (i+1)
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mask[i] = TF.rotate(mask[i].unsqueeze(0), rotation_angle).squeeze(0)
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elif angle > 0:
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for i in range(batch_size):
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mask[i] = TF.rotate(mask[i].unsqueeze(0), angle).squeeze(0)
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if roll:
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if incremental:
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for i in range(batch_size):
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shift_x = min(x*(i+1), width-1)
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shift_y = min(y*(i+1), height-1)
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if shift_x != 0:
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mask[i] = torch.roll(mask[i], shifts=shift_x, dims=1)
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if shift_y != 0:
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mask[i] = torch.roll(mask[i], shifts=shift_y, dims=0)
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else:
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shift_x = min(x, width-1)
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shift_y = min(y, height-1)
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if shift_x != 0:
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mask = torch.roll(mask, shifts=shift_x, dims=2)
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if shift_y != 0:
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mask = torch.roll(mask, shifts=shift_y, dims=1)
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else:
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for i in range(batch_size):
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if incremental:
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temp_x = min(x * (i+1), width-1)
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temp_y = min(y * (i+1), height-1)
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else:
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temp_x = min(x, width-1)
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temp_y = min(y, height-1)
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if temp_x > 0:
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if padding_mode == 'empty':
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mask[i] = torch.cat([torch.zeros((height, temp_x)), mask[i, :, :-temp_x]], dim=1)
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elif padding_mode in ['replicate', 'reflect']:
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mask[i] = F.pad(mask[i, :, :-temp_x], (0, temp_x), mode=padding_mode)
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elif temp_x < 0:
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if padding_mode == 'empty':
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mask[i] = torch.cat([mask[i, :, :temp_x], torch.zeros((height, -temp_x))], dim=1)
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elif padding_mode in ['replicate', 'reflect']:
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mask[i] = F.pad(mask[i, :, -temp_x:], (temp_x, 0), mode=padding_mode)
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if temp_y > 0:
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if padding_mode == 'empty':
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mask[i] = torch.cat([torch.zeros((temp_y, width)), mask[i, :-temp_y, :]], dim=0)
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elif padding_mode in ['replicate', 'reflect']:
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mask[i] = F.pad(mask[i, :-temp_y, :], (0, temp_y), mode=padding_mode)
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elif temp_y < 0:
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if padding_mode == 'empty':
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mask[i] = torch.cat([mask[i, :temp_y, :], torch.zeros((-temp_y, width))], dim=0)
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elif padding_mode in ['replicate', 'reflect']:
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mask[i] = F.pad(mask[i, -temp_y:, :], (temp_y, 0), mode=padding_mode)
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return mask,
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```
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