115 lines
3.8 KiB
Markdown
115 lines
3.8 KiB
Markdown
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# Documentation
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- Class name: ImageContainerInheritanceMax
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- Category: image/container
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- Output node: False
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- Repo Ref: https://github.com/comfyanonymous/ComfyUI
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ImageContainerInheritanceMax节点专门用于处理多个图像数据，通过在多个图像之间应用"最大值"操作来管理图像。该节点抽象了处理图像尺寸和像素值的复杂性，便于创建复合图像，其中考虑了所有图像中每个像素的最大值。
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# Input types
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## Required
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- images_a
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- 'images_a'输入类型接受一组待处理的图像，作为操作的一组图像集。
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- Comfy dtype: IMAGE
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- Python dtype: List[torch.Tensor]
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- images_b
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- 'images_b'输入类型接受第二组图像，与'images_a'配合使用，共同确定操作的范围。
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- Comfy dtype: IMAGE
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- Python dtype: List[torch.Tensor]
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- red
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- 指定操作背景或效果的红色分量。
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- Comfy dtype: INT
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- Python dtype: int
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- green
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- 指定操作背景或效果的绿色分量。
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- Comfy dtype: INT
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- Python dtype: int
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- blue
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- 指定操作背景或效果的蓝色分量。
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- Comfy dtype: INT
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- Python dtype: int
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- alpha
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- 定义操作背景或效果的alpha（透明度）级别。
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- Comfy dtype: FLOAT
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- Python dtype: float
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- method
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- 决定了在图像之间应用"最大值"操作的方法。'single'选项将操作应用于创建单个复合图像，'for_each_pair'为每对输入图像生成一个新图像，'for_each_matrix'为'images_a'和'images_b'中的每种可能的图像组合创建一个复合图像，这显著影响了节点的执行和结果。
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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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- 输出是一个单一的图像，代表了在所有输入图像上应用"最大值"操作的复合结果。它封装了输入集中每个像素位置的最高像素值。
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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 ImageContainerInheritanceMax:
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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_a": ("IMAGE",),
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"images_b": ("IMAGE",),
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"red": ("INT", {
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"default": 255,
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"max": 255,
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"step": 1
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}),
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"green": ("INT", {
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"default": 255,
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"max": 255,
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"step": 1
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}),
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"blue": ("INT", {
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"default": 255,
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"max": 255,
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"step": 1
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}),
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"alpha": ("FLOAT", {
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"default": 0.0,
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"max": 1.0,
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"step": 0.01
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}),
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"method": (["single", "for_each_pair", "for_each_matrix"],),
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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/container"
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def node(self, images_a, images_b, red, green, blue, alpha, method):
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img_a_height, img_a_width = images_a[0, :, :, 0].shape
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img_b_height, img_b_width = images_b[0, :, :, 0].shape
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width = max(img_a_width, img_b_width)
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height = max(img_a_height, img_b_height)
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image = create_rgba_image(width, height, (red, green, blue, int(alpha * 255))).image_to_tensor()
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if method == "single":
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return (image.unsqueeze(0),)
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elif method == "for_each_pair":
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length = len(images_a)
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images = torch.zeros(length, height, width, 4)
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else:
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length = len(images_a) * len(images_b)
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images = torch.zeros(length, height, width, 4)
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images[:, :, :] = image
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return (images,)
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```
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