7.8 KiB
7.8 KiB
Documentation
- Class name: ImageHub
- Category: 😺dzNodes/LayerUtility
- Output node: False
- Repo Ref: https://github.com/chflame163/ComfyUI_LayerStyle
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。
Input types
Required
-
output
- 输出。
- Comfy dtype: INT
- Python dtype: int
-
random_output
- 随机输出。
- Comfy dtype: BOOLEAN
- Python dtype: bool
Optional
-
input1_image
- 输入1图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
input1_mask
- 输入1蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
input2_image
- 输入2图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
input2_mask
- 输入2蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
input3_image
- 输入3图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
input3_mask
- 输入3蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
input4_image
- 输入4图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
input4_mask
- 输入4蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
input5_image
- 输入5图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
input5_mask
- 输入5蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
input6_image
- 输入6图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
input6_mask
- 输入6蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
input7_image
- 输入7图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
input7_mask
- 输入7蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
input8_image
- 输入8图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
input8_mask
- 输入8蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
-
input9_image
- 输入9图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
input9_mask
- 输入9蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
Output types
-
image
- 输出图片。
- Comfy dtype: IMAGE
- Python dtype: torch.Tensor
-
mask
- 输出蒙版。
- Comfy dtype: MASK
- Python dtype: torch.Tensor
Usage tips
- Infra type: CPU
Source code
class ImageHub:
def __init__(self):
pass
@classmethod
def INPUT_TYPES(self):
return {
"required": {
"output": ("INT", {"default": 1, "min": 1, "max": 9, "step": 1}),
"random_output": ("BOOLEAN", {"default": False}),
},
"optional": {
"input1_image": ("IMAGE",),
"input1_mask": ("MASK",),
"input2_image": ("IMAGE",),
"input2_mask": ("MASK",),
"input3_image": ("IMAGE",),
"input3_mask": ("MASK",),
"input4_image": ("IMAGE",),
"input4_mask": ("MASK",),
"input5_image": ("IMAGE",),
"input5_mask": ("MASK",),
"input6_image": ("IMAGE",),
"input6_mask": ("MASK",),
"input7_image": ("IMAGE",),
"input7_mask": ("MASK",),
"input8_image": ("IMAGE",),
"input8_mask": ("MASK",),
"input9_image": ("IMAGE",),
"input9_mask": ("MASK",),
}
}
RETURN_TYPES = ("IMAGE", "MASK",)
RETURN_NAMES = ("image", "mask")
FUNCTION = 'image_hub'
CATEGORY = '😺dzNodes/LayerUtility'
def image_hub(self, output, random_output,
input1_image=None, input1_mask=None,
input2_image=None, input2_mask=None,
input3_image=None, input3_mask=None,
input4_image=None, input4_mask=None,
input5_image=None, input5_mask=None,
input6_image=None, input6_mask=None,
input7_image=None, input7_mask=None,
input8_image=None, input8_mask=None,
input9_image=None, input9_mask=None,
):
output_list = []
if input1_image is not None or input1_mask is not None:
output_list.append(1)
if input2_image is not None or input2_mask is not None:
output_list.append(2)
if input3_image is not None or input3_mask is not None:
output_list.append(3)
if input4_image is not None or input4_mask is not None:
output_list.append(4)
if input5_image is not None or input5_mask is not None:
output_list.append(5)
if input6_image is not None or input6_mask is not None:
output_list.append(6)
if input7_image is not None or input7_mask is not None:
output_list.append(7)
if input8_image is not None or input8_mask is not None:
output_list.append(8)
if input9_image is not None or input9_mask is not None:
output_list.append(9)
log(f"output_list={output_list}")
if len(output_list) == 0:
log(f"{NODE_NAME} is skip, because No Input.", message_type='error')
return (None, None)
if random_output:
index = random.randint(1, len(output_list))
output = output_list[index - 1]
ret_image = None
ret_mask = None
if output == 1:
if input1_image is not None:
ret_image = input1_image
if input1_mask is not None:
ret_mask = input1_mask
elif output == 2:
if input2_image is not None:
ret_image = input2_image
if input2_mask is not None:
ret_mask = input2_mask
elif output == 3:
if input3_image is not None:
ret_image = input3_image
if input3_mask is not None:
ret_mask = input3_mask
elif output == 4:
if input4_image is not None:
ret_image = input4_image
if input4_mask is not None:
ret_mask = input4_mask
elif output == 5:
if input5_image is not None:
ret_image = input5_image
if input5_mask is not None:
ret_mask = input5_mask
elif output == 6:
if input6_image is not None:
ret_image = input6_image
if input6_mask is not None:
ret_mask = input6_mask
elif output == 7:
if input7_image is not None:
ret_image = input7_image
if input7_mask is not None:
ret_mask = input7_mask
elif output == 8:
if input8_image is not None:
ret_image = input8_image
if input8_mask is not None:
ret_mask = input8_mask
else:
if input9_image is not None:
ret_image = input9_image
if input9_mask is not None:
ret_mask = input9_mask
if ret_image is None and ret_mask is None:
log(f"{NODE_NAME} have {output_list} inputs, output is {output}, but there is no corresponding input.", message_type="error")
elif ret_image is None:
log(f"{NODE_NAME} have {output_list} inputs, output is {output}, but image is None.", message_type='finish')
elif ret_mask is None:
log(f"{NODE_NAME} have {output_list} inputs, output is {output}, but mask is None.", message_type='finish')
else:
log(f"{NODE_NAME} have {output_list} inputs, output is {output}.", message_type='finish')
return (ret_image, ret_mask)