271 lines
7.8 KiB
Markdown
271 lines
7.8 KiB
Markdown
# Documentation
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- Class name: ImageHub
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- Category: 😺dzNodes/LayerUtility
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- Output node: False
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- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
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从多路输入图片和遮罩中切换其中一组输出，支持9组输入。所有的输入项都是可选项。如果一组输入中只有image或者只有mask, 缺失项将输出为None。
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# Input types
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## Required
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- output
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- 输出。
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- Comfy dtype: INT
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- Python dtype: int
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- random_output
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- 随机输出。
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- Comfy dtype: BOOLEAN
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- Python dtype: bool
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## Optional
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- input1_image
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- 输入1图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- input1_mask
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- 输入1蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- input2_image
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- 输入2图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- input2_mask
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- 输入2蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- input3_image
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- 输入3图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- input3_mask
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- 输入3蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- input4_image
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- 输入4图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- input4_mask
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- 输入4蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- input5_image
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- 输入5图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- input5_mask
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- 输入5蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- input6_image
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- 输入6图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- input6_mask
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- 输入6蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- input7_image
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- 输入7图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- input7_mask
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- 输入7蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- input8_image
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- 输入8图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- input8_mask
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- 输入8蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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- input9_image
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- 输入9图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- input9_mask
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- 输入9蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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# Output types
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- image
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- 输出图片。
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- Comfy dtype: IMAGE
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- Python dtype: torch.Tensor
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- mask
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- 输出蒙版。
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- Comfy dtype: MASK
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- Python dtype: torch.Tensor
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# Usage tips
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- Infra type: CPU
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# Source code
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```python
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class ImageHub:
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def __init__(self):
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pass
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@classmethod
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def INPUT_TYPES(self):
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return {
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"required": {
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"output": ("INT", {"default": 1, "min": 1, "max": 9, "step": 1}),
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"random_output": ("BOOLEAN", {"default": False}),
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},
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"optional": {
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"input1_image": ("IMAGE",),
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"input1_mask": ("MASK",),
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"input2_image": ("IMAGE",),
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"input2_mask": ("MASK",),
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"input3_image": ("IMAGE",),
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"input3_mask": ("MASK",),
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"input4_image": ("IMAGE",),
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"input4_mask": ("MASK",),
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"input5_image": ("IMAGE",),
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"input5_mask": ("MASK",),
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"input6_image": ("IMAGE",),
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"input6_mask": ("MASK",),
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"input7_image": ("IMAGE",),
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"input7_mask": ("MASK",),
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"input8_image": ("IMAGE",),
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"input8_mask": ("MASK",),
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"input9_image": ("IMAGE",),
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"input9_mask": ("MASK",),
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}
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}
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RETURN_TYPES = ("IMAGE", "MASK",)
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RETURN_NAMES = ("image", "mask")
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FUNCTION = 'image_hub'
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CATEGORY = '😺dzNodes/LayerUtility'
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def image_hub(self, output, random_output,
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input1_image=None, input1_mask=None,
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input2_image=None, input2_mask=None,
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input3_image=None, input3_mask=None,
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input4_image=None, input4_mask=None,
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input5_image=None, input5_mask=None,
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input6_image=None, input6_mask=None,
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input7_image=None, input7_mask=None,
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input8_image=None, input8_mask=None,
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input9_image=None, input9_mask=None,
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):
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output_list = []
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if input1_image is not None or input1_mask is not None:
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output_list.append(1)
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if input2_image is not None or input2_mask is not None:
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output_list.append(2)
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if input3_image is not None or input3_mask is not None:
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output_list.append(3)
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if input4_image is not None or input4_mask is not None:
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output_list.append(4)
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if input5_image is not None or input5_mask is not None:
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output_list.append(5)
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if input6_image is not None or input6_mask is not None:
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output_list.append(6)
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if input7_image is not None or input7_mask is not None:
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output_list.append(7)
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if input8_image is not None or input8_mask is not None:
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output_list.append(8)
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if input9_image is not None or input9_mask is not None:
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output_list.append(9)
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log(f"output_list={output_list}")
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if len(output_list) == 0:
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log(f"{NODE_NAME} is skip, because No Input.", message_type='error')
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return (None, None)
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if random_output:
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index = random.randint(1, len(output_list))
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output = output_list[index - 1]
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ret_image = None
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ret_mask = None
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if output == 1:
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if input1_image is not None:
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ret_image = input1_image
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if input1_mask is not None:
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ret_mask = input1_mask
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elif output == 2:
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if input2_image is not None:
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ret_image = input2_image
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if input2_mask is not None:
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ret_mask = input2_mask
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elif output == 3:
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if input3_image is not None:
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ret_image = input3_image
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if input3_mask is not None:
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ret_mask = input3_mask
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elif output == 4:
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if input4_image is not None:
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ret_image = input4_image
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if input4_mask is not None:
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ret_mask = input4_mask
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elif output == 5:
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if input5_image is not None:
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ret_image = input5_image
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if input5_mask is not None:
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ret_mask = input5_mask
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elif output == 6:
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if input6_image is not None:
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ret_image = input6_image
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if input6_mask is not None:
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ret_mask = input6_mask
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elif output == 7:
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if input7_image is not None:
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ret_image = input7_image
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if input7_mask is not None:
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ret_mask = input7_mask
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elif output == 8:
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if input8_image is not None:
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ret_image = input8_image
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if input8_mask is not None:
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ret_mask = input8_mask
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else:
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if input9_image is not None:
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ret_image = input9_image
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if input9_mask is not None:
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ret_mask = input9_mask
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if ret_image is None and ret_mask is None:
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log(f"{NODE_NAME} have {output_list} inputs, output is {output}, but there is no corresponding input.", message_type="error")
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elif ret_image is None:
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log(f"{NODE_NAME} have {output_list} inputs, output is {output}, but image is None.", message_type='finish')
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elif ret_mask is None:
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log(f"{NODE_NAME} have {output_list} inputs, output is {output}, but mask is None.", message_type='finish')
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else:
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log(f"{NODE_NAME} have {output_list} inputs, output is {output}.", message_type='finish')
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return (ret_image, ret_mask)
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``` |