106 lines
3.2 KiB
Markdown
106 lines
3.2 KiB
Markdown
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# Documentation
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- Class name: `ImageNoiseBeta`
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- Category: `image/noise`
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- Output node: `False`
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ImageNoiseBeta节点用于向图像引入基于贝塔分布的噪声，可以模拟各种噪声效果。该节点能够调整噪声强度，并根据颜色通道选择性地应用噪声，支持单色和彩色噪声应用。
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# Input types
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## Required
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- **`images`**
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- 需要应用贝塔噪声的输入图像。这个参数对于定义将要被修改的基础内容至关重要。
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- Comfy dtype: `IMAGE`
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- Python dtype: `torch.Tensor`
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- **`a`**
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- 指定贝塔分布的alpha参数，影响噪声分布曲线的形状。
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- Comfy dtype: `INT`
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- Python dtype: `int`
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- **`b`**
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- 指定贝塔分布的beta参数，同样影响噪声分布曲线的形状。
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- Comfy dtype: `INT`
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- Python dtype: `int`
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- **`monochromatic`**
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- 决定噪声是应该以单色方式应用于所有通道，还是单独应用于每个通道。
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- Comfy dtype: `COMBO[STRING]`
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- Python dtype: `bool`
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- **`invert`**
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- 控制是否应该反转噪声效果，提供不同的视觉效果。
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- Comfy dtype: `COMBO[STRING]`
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- Python dtype: `bool`
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- **`channels`**
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- 指定应该应用噪声的颜色通道，允许选择性地应用噪声。
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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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- **`image`**
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- 应用了基于贝塔分布噪声的输出图像，反映了指定的强度和通道选择。
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- Comfy dtype: `IMAGE`
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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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class ImageNoiseBeta:
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def __init__(self):
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pass
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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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"images": ("IMAGE",),
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"a": ("INT", {
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"default": 1,
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"min": 1,
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}),
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"b": ("INT", {
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"default": 1,
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"min": 1,
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}),
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"monochromatic": (["false", "true"],),
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"invert": (["false", "true"],),
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"channels": (["rgb", "rgba", "rg", "rb", "ra", "gb", "ga", "ba", "r", "g", "b", "a"],),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "node"
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CATEGORY = "image/noise"
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def noise(self, images, a, b, monochromatic, invert):
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if monochromatic and images.shape[3] > 1:
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noise = np.random.beta(a, b, images.shape[:3])
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else:
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noise = np.random.beta(a, b, images.shape)
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if monochromatic and images.shape[3] > 1:
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noise = noise[..., np.newaxis].repeat(images.shape[3], -1)
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if invert:
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noise = images - noise
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else:
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noise = images + noise
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noise = noise.astype(images.dtype)
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return noise
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def node(self, images, a, b, monochromatic, invert, channels):
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tensor = images.clone().detach()
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monochromatic = True if monochromatic == "true" else False
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invert = True if invert == "true" else False
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return (channels_layer(tensor, channels, lambda x: self.noise(
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x, a, b, monochromatic, invert
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)),)
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```
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