75 lines
2.8 KiB
Markdown
75 lines
2.8 KiB
Markdown
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# Documentation
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- Class name: ImageBatchMultiple+
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- Category: essentials
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- Output node: False
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- Repo Ref: https://github.com/shleemr/ComfyUI-Custom-Nodes
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ImageBatchMultiple+ 节点旨在将多个图像合并为一个批次，便于需要批量处理图像的操作。该节点抽象了处理多种图像格式和尺寸的复杂性，确保它们能够兼容批量操作。
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# Input types
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## Required
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- image_i
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- 代表要包含在批次中的图像。根据需要调整其格式和大小，以确保与批次中的其他图像兼容。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- method
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- 指定将图像合并到批次中的方法，确保所有图像的一致性和兼容性。
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- Comfy dtype: COMBO[STRING]
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- Python dtype: str
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## Optional
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# Output types
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- image
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- 输出是一个由输入图像组合而成的图像批次。这个批次可用于进一步的图像处理或分析任务。
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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 ImageBatchMultiple:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"image_1": ("IMAGE",),
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"image_2": ("IMAGE",),
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"method": (["nearest-exact", "bilinear", "area", "bicubic", "lanczos"], { "default": "lanczos" }),
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}, "optional": {
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"image_3": ("IMAGE",),
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"image_4": ("IMAGE",),
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"image_5": ("IMAGE",),
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},
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "execute"
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CATEGORY = "essentials"
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def execute(self, image_1, image_2, method, image_3=None, image_4=None, image_5=None):
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if image_1.shape[1:] != image_2.shape[1:]:
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image_2 = comfy.utils.common_upscale(image_2.movedim(-1,1), image_1.shape[2], image_1.shape[1], method, "center").movedim(1,-1)
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out = torch.cat((image_1, image_2), dim=0)
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if image_3 is not None:
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if image_1.shape[1:] != image_3.shape[1:]:
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image_3 = comfy.utils.common_upscale(image_3.movedim(-1,1), image_1.shape[2], image_1.shape[1], method, "center").movedim(1,-1)
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out = torch.cat((out, image_3), dim=0)
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if image_4 is not None:
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if image_1.shape[1:] != image_4.shape[1:]:
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image_4 = comfy.utils.common_upscale(image_4.movedim(-1,1), image_1.shape[2], image_1.shape[1], method, "center").movedim(1,-1)
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out = torch.cat((out, image_4), dim=0)
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if image_5 is not None:
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if image_1.shape[1:] != image_5.shape[1:]:
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image_5 = comfy.utils.common_upscale(image_5.movedim(-1,1), image_1.shape[2], image_1.shape[1], method, "center").movedim(1,-1)
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out = torch.cat((out, image_5), dim=0)
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return (out,)
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```
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