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ToIPAdapterPipe (Inspire)

Documentation

  • Class name: ToIPAdapterPipe __Inspire
  • Category: InspirePack/Util
  • Output node: False

The ToIPAdapterPipe node is designed to create a pipeline that integrates various components such as IP adapters, models, and optional vision and face recognition enhancements into a unified processing flow. This setup facilitates the adaptation and enhancement of input data or models for further processing or analysis.

Input types

Required

  • ipadapter
    • The 'ipadapter' parameter is crucial for specifying the IP adapter component to be used in the pipeline, serving as the foundational element for data or model adaptation.
    • Comfy dtype: IPADAPTER
    • Python dtype: str
  • model
    • The 'model' parameter identifies the specific model to be integrated into the pipeline, enabling its adaptation or enhancement through the IP adapter.
    • Comfy dtype: MODEL
    • Python dtype: str

Optional

  • clip_vision
    • The 'clip_vision' parameter optionally adds vision processing capabilities to the pipeline, leveraging CLIP models for enhanced visual understanding.
    • Comfy dtype: CLIP_VISION
    • Python dtype: str
  • insightface
    • The 'insightface' parameter optionally incorporates face recognition technology into the pipeline, providing advanced facial analysis features.
    • Comfy dtype: INSIGHTFACE
    • Python dtype: str

Output types

  • ipadapter_pipe
    • Comfy dtype: IPADAPTER_PIPE
    • This output represents the assembled pipeline, encapsulating the integrated IP adapter, model, and any optional enhancements for vision and face recognition.
    • Python dtype: tuple

Usage tips

  • Infra type: CPU
  • Common nodes: unknown

Source code

class ToIPAdapterPipe:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "ipadapter": ("IPADAPTER", ),
                "model": ("MODEL",),
            },
            "optional": {
                "clip_vision": ("CLIP_VISION",),
                "insightface": ("INSIGHTFACE",),
            }
        }

    RETURN_TYPES = ("IPADAPTER_PIPE",)
    FUNCTION = "doit"

    CATEGORY = "InspirePack/Util"

    @staticmethod
    def doit(ipadapter, model, clip_vision, insightface=None):
        pipe = ipadapter, model, clip_vision, insightface, lambda x: x

        return (pipe,)