97 lines
3.0 KiB
Markdown
97 lines
3.0 KiB
Markdown
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# Documentation
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- Class name: ImageNoiseBytes
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- Category: image/noise
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- Output node: False
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ImageNoiseBytes节点向图像应用特定类型的噪声变换。它根据提供的参数在一个或多个通道上操作像素值，用可调整为单色或彩色变化并可选择性反转的噪声效果增强图像。
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# Input types
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## Required
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- images
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- 将应用噪声效果的输入图像。此参数对于定义进行噪声变换的基础至关重要。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- monochromatic
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- 决定噪声效果是应该在所有通道上以单色方式应用，还是在每个通道上单独应用，从而影响噪声效果的视觉效果。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: bool
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- invert
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- 指定是否应反转噪声效果，改变图像中噪声效果的视觉对比度。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: bool
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- channels
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- 定义将应用噪声效果的图像的特定通道，允许在选定的颜色通道上进行有针对性的噪声应用。
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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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- 应用了噪声效果的转换后的图像，展示了由噪声参数产生的视觉变化。
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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 ImageNoiseBytes:
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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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"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, monochromatic, invert):
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if monochromatic and images.shape[3] > 1:
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noise = np.random.bytes(np.prod(images.shape[:3]))
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noise = np.frombuffer(noise, np.uint8)
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noise = np.reshape(noise, images.shape[:3])
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else:
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noise = np.random.bytes(np.prod(images.shape))
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noise = np.frombuffer(noise, np.uint8)
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noise = np.reshape(noise, images.shape)
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noise = noise.astype(images.dtype)
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noise /= 255
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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 = np.clip(noise, 0.0, 1.0)
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return noise
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def node(self, images, 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, monochromatic, invert
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)),)
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```
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