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✈️ 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, )