111 lines
4.9 KiB
Markdown
111 lines
4.9 KiB
Markdown
# Documentation
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- Class name: CreateFadeMask
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- Category: KJNodes/deprecated
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- Output node: False
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- Repo Ref: https://github.com/kijai/ComfyUI-KJNodes.git
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CreateFadeMask 节点旨在生成一系列渐变遮罩，平滑地从一种不透明度过渡到另一种。它利用插值技术创建视觉上吸引人的渐变效果，特别适用于视觉效果和图像处理应用。
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# Input types
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## Required
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- invert
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- ‘invert’参数决定是否需要反转渐变遮罩，这对于某些需要反转不透明度过渡的视觉效果至关重要。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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- frames
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- ‘frames’参数指定渐变遮罩序列中的帧数，这直接影响渐变效果的持续时间和间隔。
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- Comfy dtype: INT
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- Python dtype: int
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- width
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- ‘width’参数设置渐变遮罩中每个帧的宽度，影响输出的整体尺寸。
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- Comfy dtype: INT
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- Python dtype: int
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- height
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- ‘height’参数定义渐变遮罩中每个帧的高度，这对于保持输出的纵横比和视觉一致性至关重要。
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- Comfy dtype: INT
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- Python dtype: int
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- interpolation
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- ‘interpolation’参数选择用于渐变效果的插值类型，这可以显著改变不透明度级别之间过渡的外观。
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- Comfy dtype: COMBO['linear', 'ease_in', 'ease_out', 'ease_in_out']
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- Python dtype: str
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- start_level
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- ‘start_level’参数设定渐变遮罩的初始不透明度级别，这是确定渐变效果起始点的关键因素。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- midpoint_level
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- ‘midpoint_level’参数指示渐变遮罩序列中点的不透明度级别，有助于控制过渡中心处的渐变速度。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- end_level
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- ‘end_level’参数决定渐变遮罩的最终不透明度级别，这决定了渐变效果如何结束。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- midpoint_frame
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- ‘midpoint_frame’参数指定渐变效果中点发生的帧，允许对过渡的时机进行精确控制。
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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‘mask’是一系列渐变遮罩，它们代表了从开始级别到结束级别的不透明度过渡，根据指定的参数创建。
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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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# Source code
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```
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class CreateFadeMask:
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RETURN_TYPES = ('MASK',)
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FUNCTION = 'createfademask'
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CATEGORY = 'KJNodes/deprecated'
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@classmethod
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def INPUT_TYPES(s):
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return {'required': {'invert': ('BOOLEAN', {'default': False}), 'frames': ('INT', {'default': 2, 'min': 2, 'max': 255, 'step': 1}), 'width': ('INT', {'default': 256, 'min': 16, 'max': 4096, 'step': 1}), 'height': ('INT', {'default': 256, 'min': 16, 'max': 4096, 'step': 1}), 'interpolation': (['linear', 'ease_in', 'ease_out', 'ease_in_out'],), 'start_level': ('FLOAT', {'default': 1.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'midpoint_level': ('FLOAT', {'default': 0.5, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'end_level': ('FLOAT', {'default': 0.0, 'min': 0.0, 'max': 1.0, 'step': 0.01}), 'midpoint_frame': ('INT', {'default': 0, 'min': 0, 'max': 4096, 'step': 1})}}
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def createfademask(self, frames, width, height, invert, interpolation, start_level, midpoint_level, end_level, midpoint_frame):
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def ease_in(t):
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return t * t
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def ease_out(t):
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return 1 - (1 - t) * (1 - t)
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def ease_in_out(t):
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return 3 * t * t - 2 * t * t * t
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batch_size = frames
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out = []
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image_batch = np.zeros((batch_size, height, width), dtype=np.float32)
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if midpoint_frame == 0:
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midpoint_frame = batch_size // 2
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for i in range(batch_size):
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if i <= midpoint_frame:
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t = i / midpoint_frame
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if interpolation == 'ease_in':
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t = ease_in(t)
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elif interpolation == 'ease_out':
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t = ease_out(t)
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elif interpolation == 'ease_in_out':
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t = ease_in_out(t)
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color = start_level - t * (start_level - midpoint_level)
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else:
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t = (i - midpoint_frame) / (batch_size - midpoint_frame)
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if interpolation == 'ease_in':
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t = ease_in(t)
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elif interpolation == 'ease_out':
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t = ease_out(t)
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elif interpolation == 'ease_in_out':
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t = ease_in_out(t)
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color = midpoint_level - t * (midpoint_level - end_level)
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color = np.clip(color, 0, 255)
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image = np.full((height, width), color, dtype=np.float32)
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image_batch[i] = image
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output = torch.from_numpy(image_batch)
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mask = output
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out.append(mask)
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if invert:
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return (1.0 - torch.cat(out, dim=0),)
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return (torch.cat(out, dim=0),)
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``` |