CavinHuang-comfyui-nodes-docs/docs/ImageNoiseBeta.md

Documentation

• Class name: ImageNoiseBeta
• Category: image/noise
• Output node: False

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。

Input types

Required

• images
  • 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。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor
• a
  • 指定贝塔分布的alpha参数，影响噪声分布曲线的形状。
  • Comfy dtype: INT
  • Python dtype: int
• b
  • 指定贝塔分布的beta参数，同样影响噪声分布曲线的形状。
  • Comfy dtype: INT
  • Python dtype: int
• monochromatic
  • 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。
  • Comfy dtype: COMBO[STRING]
  • Python dtype: bool
• invert
  • 控制是否应该反转噪声效果，提供不同的视觉效果。
  • Comfy dtype: COMBO[STRING]
  • Python dtype: bool
• channels
  • 指定应该应用噪声的颜色通道，允许选择性地应用噪声。
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Output types

• image
  • 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。
  • Comfy dtype: IMAGE
  • Python dtype: torch.Tensor

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class ImageNoiseBeta:
    def __init__(self):
        pass

    @classmethod
    def INPUT_TYPES(cls):
        return {
            "required": {
                "images": ("IMAGE",),
                "a": ("INT", {
                    "default": 1,
                    "min": 1,
                }),
                "b": ("INT", {
                    "default": 1,
                    "min": 1,
                }),
                "monochromatic": (["false", "true"],),
                "invert": (["false", "true"],),
                "channels": (["rgb", "rgba", "rg", "rb", "ra", "gb", "ga", "ba", "r", "g", "b", "a"],),
            },
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "node"
    CATEGORY = "image/noise"

    def noise(self, images, a, b, monochromatic, invert):
        if monochromatic and images.shape[3] > 1:
            noise = np.random.beta(a, b, images.shape[:3])
        else:
            noise = np.random.beta(a, b, images.shape)

        if monochromatic and images.shape[3] > 1:
            noise = noise[..., np.newaxis].repeat(images.shape[3], -1)

        if invert:
            noise = images - noise
        else:
            noise = images + noise

        noise = noise.astype(images.dtype)

        return noise

    def node(self, images, a, b, monochromatic, invert, channels):
        tensor = images.clone().detach()

        monochromatic = True if monochromatic == "true" else False
        invert = True if invert == "true" else False

        return (channels_layer(tensor, channels, lambda x: self.noise(
            x, a, b, monochromatic, invert
        )),)