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

# Documentation
- Class name: ImageCompositeAbsolute
- Category: image/composite
- Output node: False

ImageCompositeAbsolute 节点用于通过精确定位两个输入图像来创建复合图像。它根据给定的坐标、容器尺寸和指定的合成方法来处理这些图像的精确放置和混合，从而实现复杂的视觉布局创作。

# Input types
## Required
- images_a
    - 要在容器内合成的第一个图像。它在复合图像的图层顺序和视觉效果中起着关键作用。
    - Comfy dtype: IMAGE
    - Python dtype: numpy.ndarray
- images_b
    - 要在容器内合成的第二个图像，为最终复合图像增添复杂性和深度。
    - Comfy dtype: IMAGE
    - Python dtype: numpy.ndarray
- images_a_x
    - 第一个图像在容器内左上角的 x 坐标，定义其水平位置。
    - Comfy dtype: INT
    - Python dtype: int
- images_a_y
    - 第一个图像在容器内左上角的 y 坐标，定义其垂直位置。
    - Comfy dtype: INT
    - Python dtype: int
- images_b_x
    - 第二个图像在容器内左上角的 x 坐标，定义其水平位置。
    - Comfy dtype: INT
    - Python dtype: int
- images_b_y
    - 第二个图像在容器内左上角的 y 坐标，定义其垂直位置。
    - Comfy dtype: INT
    - Python dtype: int
- container_width
    - 指定用于合成输入图像的容器宽度。它决定了合成的水平边界。
    - Comfy dtype: INT
    - Python dtype: int
- container_height
    - 指定用于合成输入图像的容器高度。它决定了合成的垂直边界。
    - Comfy dtype: INT
    - Python dtype: int
- background
    - 确定将哪个输入图像（images_a 或 images_b）用作复合图像的背景。这个选择会影响最终图像的图层和视觉效果。
    - Comfy dtype: COMBO[STRING]
    - Python dtype: str
- method
    - 指定要使用的合成方法，影响图像在容器内的混合方式。
    - Comfy dtype: COMBO[STRING]
    - Python dtype: str

# Output types
- image
    - 根据指定参数和方法合成输入图像后得到的结果图像。
    - Comfy dtype: IMAGE
    - Python dtype: numpy.ndarray

# Usage tips
- Infra type: CPU
- Common nodes:
    - [PreviewImage](https://github.com/comfyanonymous/ComfyUI/blob/master/nodes/image.py)
    - VAEEncode
    - [ImageScale](https://github.com/comfyanonymous/ComfyUI/blob/master/nodes/image.py)

# Source code
python
class ImageCompositeAbsolute:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "images_a": ("IMAGE",),
                "images_b": ("IMAGE",),
                "images_a_x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "images_a_y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "images_b_x": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "images_b_y": ("INT", {"default": 0, "min": 0, "max": MAX_RESOLUTION, "step": 1}),
                "container_width": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 1}),
                "container_height": ("INT", {"default": 512, "min": 16, "max": MAX_RESOLUTION, "step": 1}),
                "background": (["images_a", "images_b"],),
                "method": (["normal", "multiply", "screen", "overlay", "soft_light"],)
            }
        }

    RETURN_TYPES = ("IMAGE",)
    FUNCTION = "composite"

    CATEGORY = "image/composite"

    def composite(self, images_a, images_b, images_a_x, images_a_y, images_b_x, images_b_y, container_width, container_height, background, method):
        images_a = images_a.clone().movedim(-1,1)
        images_b = images_b.clone().movedim(-1,1)

        if background == "images_a":
            results = []
            canvas = torch.zeros((images_a.shape[0], 4, container_height, container_width), dtype=torch.float32, device="cpu")
            canvas[:, :, images_a_y:images_a_y + images_a.shape[2], images_a_x:images_a_x + images_a.shape[3]] = images_a
            images_b = torch.nn.functional.pad(images_b, (images_b_x, container_width - images_b_x - images_b.shape[3], images_b_y, container_height - images_b_y - images_b.shape[2]), "constant", 0)
            images_b = images_b[:, :, :container_height, :container_width]
            results.append(canvas)
            results.append(images_b)
        else:
            results = []
            canvas = torch.zeros((images_b.shape[0], 4, container_height, container_width), dtype=torch.float32, device="cpu")
            canvas[:, :, images_b_y:images_b_y + images_b.shape[2], images_b_x:images_b_x + images_b.shape[3]] = images_b
            images_a = torch.nn.functional.pad(images_a, (images_a_x, container_width - images_a_x - images_a.shape[3], images_a_y, container_height - images_a_y - images_a.shape[2]), "constant", 0)
            images_a = images_a[:, :, :container_height, :container_width]
            results.append(canvas)
            results.append(images_a)

        if method == "normal":
            return (self.normal(results[0], results[1]).movedim(1,-1),)
        elif method == "multiply":
            return (self.multiply(results[0], results[1]).movedim(1,-1),)
        elif method == "screen":
            return (self.screen(results[0], results[1]).movedim(1,-1),)
        elif method == "overlay":
            return (self.overlay(results[0], results[1]).movedim(1,-1),)
        elif method == "soft_light":
            return (self.soft_light(results[0], results[1]).movedim(1,-1),)

    def normal(self, bg, fg):
        out = torch.where(fg[:,:4] == 0, bg, fg[:,:4])
        out[:, 3:4] = 1 - (1 - fg[:, 3:4]) * (1 - bg[:, 3:4])
        return out

    def multiply(self, bg, fg):
        out = fg * bg
        out[:, 3:4] = 1 - (1 - fg[:, 3:4]) * (1 - bg[:, 3:4])
        return out

    def screen(self, bg, fg):
        out = bg + fg - (bg * fg)
        out[:, 3:4] = 1 - (1 - fg[:, 3:4]) * (1 - bg[:, 3:4])
        return out

    def overlay(self, bg, fg):
        def f(bg, fg):
            return torch.where(bg <= 0.5, 2 * bg * fg, 1 - 2 * (1 - bg) * (1 - fg))
        out = fg.clone()
        out[:, :3] = f(bg[:, :3], fg[:, :3])
        out[:, 3:4] = 1 - (1 - fg[:, 3:4]) * (1 - bg[:, 3:4])
        return out

    def soft_light(self, bg, fg):
        def f(bg, fg):
            return torch.where(fg <= 0.5, bg - (1 - 2 * fg) * bg * (1 - bg), bg + (2 * fg - 1) * (self.d(bg) - bg))

        def d(x):
            return torch.where(x <= 0.25, ((16 * x - 12) * x + 4) * x, torch.sqrt(x))

        self.d = d
        out = fg.clone()
        out[:, :3] = f(bg[:, :3], fg[:, :3])
        out[:, 3:4] = 1 - (1 - fg[:, 3:4]) * (1 - bg[:, 3:4])
        return out
```