92 lines
2.8 KiB
Markdown
92 lines
2.8 KiB
Markdown
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# Documentation
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- Class name: ImageGridComposite3x3
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- Category: KJNodes/image
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- Output node: False
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ImageGridComposite3x3节点设计用于将九张输入图像拼接成一个3x3的网格，有效地创建一个复合图像，以结构化布局展示所有输入。
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# Input types
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## Required
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- image1
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- 将被放置在3x3网格左上角的第一张图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- image2
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- 将被放置在3x3网格顶行，紧邻第一张图像的第二张图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- image3
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- 将被放置在3x3网格顶行，紧邻第二张图像的第三张图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- image4
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- 将被放置在3x3网格中间行，从左侧开始的第四张图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- image5
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- 3x3网格的中央图像，被其他八张图像包围。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- image6
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- 将被放置在3x3网格中间行，紧邻第五张图像的第六张图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- image7
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- 将被放置在3x3网格底行，从左侧开始的第七张图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- image8
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- 将被放置在3x3网格底行，紧邻第七张图像的第八张图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- image9
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- 将被放置在3x3网格右下角的第九张也是最后一张图像，完成整个复合图像。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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# Output types
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- image
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- 通过将九张输入图像拼接成3x3网格而形成的复合图像。
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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 ImageGridComposite3x3:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"image1": ("IMAGE",),
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"image2": ("IMAGE",),
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"image3": ("IMAGE",),
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"image4": ("IMAGE",),
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"image5": ("IMAGE",),
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"image6": ("IMAGE",),
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"image7": ("IMAGE",),
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"image8": ("IMAGE",),
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"image9": ("IMAGE",),
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}}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "compositegrid"
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CATEGORY = "KJNodes/image"
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DESCRIPTION = """
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Concatenates the 9 input images into a 3x3 grid.
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"""
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def compositegrid(self, image1, image2, image3, image4, image5, image6, image7, image8, image9):
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top_row = torch.cat((image1, image2, image3), dim=2)
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mid_row = torch.cat((image4, image5, image6), dim=2)
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bottom_row = torch.cat((image7, image8, image9), dim=2)
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grid = torch.cat((top_row, mid_row, bottom_row), dim=1)
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return (grid,)
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```
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