CavinHuang-comfyui-nodes-docs/nodes-docs/en-US/ComfyUI_Comfyroll_CustomNodes/docs/CR Module Input.md

tags

tags
Image Pipeline

✈️ CR Module Input

Documentation

• Class name: CR Module Input
• Category: 🧩 Comfyroll Studio/✨ Essential/🎷 Pipe/✈️ Module
• Output node: False

The CR_ModuleInput node is designed to facilitate the flow of data through a pipeline by accepting a variety of inputs and flushing them through the system. It serves as a critical junction point in the pipeline, ensuring that data is correctly routed and transformed for subsequent processing stages.

Input types

Required

• pipe
  • The 'pipe' parameter is the primary conduit for data flowing through the node. It encapsulates a variety of data types and structures, acting as a central hub for the input data that will be processed and routed within the pipeline.
  • Comfy dtype: PIPE_LINE
  • Python dtype: Tuple[torch.Tensor, torch.nn.Module, str, str, torch.Tensor, torch.nn.Module, torch.nn.Module, PIL.Image.Image, int]

Output types

• pipe
  • Comfy dtype: PIPE_LINE
  • Represents the aggregated and processed data, ready to be forwarded to the next stage in the pipeline.
  • Python dtype: Tuple[torch.Tensor, torch.nn.Module, str, str, torch.Tensor, torch.nn.Module, torch.nn.Module, PIL.Image.Image, int, str]
• model
  • Comfy dtype: MODEL
  • The model data extracted from the input pipe.
  • Python dtype: torch.nn.Module
• pos
  • Comfy dtype: CONDITIONING
  • Positive conditioning data extracted from the input pipe.
  • Python dtype: str
• neg
  • Comfy dtype: CONDITIONING
  • Negative conditioning data extracted from the input pipe.
  • Python dtype: str
• latent
  • Comfy dtype: LATENT
  • Latent representation data extracted from the input pipe.
  • Python dtype: torch.Tensor
• vae
  • Comfy dtype: VAE
  • VAE model data extracted from the input pipe.
  • Python dtype: torch.nn.Module
• clip
  • Comfy dtype: CLIP
  • CLIP model data extracted from the input pipe.
  • Python dtype: torch.nn.Module
• controlnet
  • Comfy dtype: CONTROL_NET
  • ControlNet data extracted from the input pipe.
  • Python dtype: torch.nn.Module
• image
  • Comfy dtype: IMAGE
  • Image data extracted from the input pipe.
  • Python dtype: PIL.Image.Image
• seed
  • Comfy dtype: INT
  • Seed value extracted from the input pipe.
  • Python dtype: int
• show_help
  • Comfy dtype: STRING
  • A URL providing additional help and documentation for the node.
  • Python dtype: str

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class CR_ModuleInput:
    
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {"pipe": ("PIPE_LINE",)},
            }

    RETURN_TYPES = ("PIPE_LINE", "MODEL", "CONDITIONING", "CONDITIONING", "LATENT", "VAE", "CLIP", "CONTROL_NET", "IMAGE", "INT", "STRING", )
    RETURN_NAMES = ("pipe", "model", "pos", "neg", "latent", "vae", "clip", "controlnet", "image", "seed", "show_help", )
    FUNCTION = "flush"
    CATEGORY = icons.get("Comfyroll/Pipe/Module")
    
    def flush(self, pipe):

        show_help = "https://github.com/Suzie1/ComfyUI_Comfyroll_CustomNodes/wiki/Pipe-Nodes#cr-module-input"

        model, pos, neg, latent, vae, clip, controlnet, image, seed = pipe
        
        return (pipe, model, pos, neg, latent, vae, clip, controlnet, image, seed, show_help, )