# 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 ```python 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) ```