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✈️ CR Module Pipe Loader

Documentation

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

The CR Module Pipe Loader node is designed to aggregate various types of inputs, such as models, conditioning data, latent vectors, and images, into a unified pipeline format. It facilitates the modular assembly of components for generative AI applications, allowing for flexible and dynamic construction of processing pipelines.

Input types

Required

Optional

  • model
    • Represents a model component to be included in the pipeline. It's crucial for defining the generative model that will be used in the pipeline.
    • Comfy dtype: MODEL
    • Python dtype: tuple
  • pos
    • Specifies positive conditioning data to guide the generative process. It affects the direction and characteristics of the generation.
    • Comfy dtype: CONDITIONING
    • Python dtype: tuple
  • neg
    • Specifies negative conditioning data to counterbalance or steer away from certain aspects during generation.
    • Comfy dtype: CONDITIONING
    • Python dtype: tuple
  • latent
    • Provides a latent vector to seed or influence the generative process, offering a way to introduce variability or specific directions.
    • Comfy dtype: LATENT
    • Python dtype: tuple
  • vae
    • Represents a VAE (Variational Autoencoder) component, which can be used for encoding or decoding in the pipeline.
    • Comfy dtype: VAE
    • Python dtype: tuple
  • clip
    • Incorporates a CLIP model for semantic understanding or alignment, enhancing the generative process with textual or visual guidance.
    • Comfy dtype: CLIP
    • Python dtype: tuple
  • controlnet
    • Includes a ControlNet component for additional control over the generation process, allowing for more precise manipulations.
    • Comfy dtype: CONTROL_NET
    • Python dtype: tuple
  • image
    • Includes an image to be processed or used as part of the generative process, adding visual data to the pipeline.
    • Comfy dtype: IMAGE
    • Python dtype: tuple
  • seed
    • Provides a seed value for random number generation, ensuring reproducibility or variability in the generative process.
    • Comfy dtype: INT
    • Python dtype: int

Output types

  • pipe
    • Comfy dtype: PIPE_LINE
    • The assembled pipeline, encapsulating all the provided components in a unified format for further processing.
    • Python dtype: tuple
  • show_help
    • Comfy dtype: STRING
    • A URL to the documentation or help page for the node, offering guidance on its usage and capabilities.
    • Python dtype: str

Usage tips

Source code

class CR_ModulePipeLoader:

    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
            },
            "optional": {
                "model": ("MODEL",),
                "pos": ("CONDITIONING",),
                "neg": ("CONDITIONING",),
                "latent": ("LATENT",),
                "vae": ("VAE",),
                "clip": ("CLIP",),
                "controlnet": ("CONTROL_NET",),
                "image": ("IMAGE",),
                "seed": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff})            
            },
        }

    RETURN_TYPES = ("PIPE_LINE", "STRING", )
    RETURN_NAMES = ("pipe", "show_help", )
    FUNCTION = "pipe_input"
    CATEGORY = icons.get("Comfyroll/Pipe/Module")

    def pipe_input(self, model=0, pos=0, neg=0, latent=0, vae=0, clip=0, controlnet=0, image=0, seed=0):

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

        pipe_line = (model, pos, neg, latent, vae, clip, controlnet, image, seed)

        return (pipe_line, show_help, )