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HED Preprocessor Provider (SEGS)

Documentation

  • Class name: HEDPreprocessor_Provider_for_SEGS __Inspire
  • Category: InspirePack/SEGS/ControlNet
  • Output node: False

This node provides a preprocessor for SEGS (semantic edge guided synthesis) using the HED (Holistically-Nested Edge Detection) algorithm. It is designed to preprocess images by detecting edges in a holistic manner, enhancing the input for SEGS applications.

Input types

Required

  • safe
    • Determines whether the preprocessing should be performed in a safe mode, which may affect the edge detection results.
    • Comfy dtype: BOOLEAN
    • Python dtype: bool

Output types

  • segs_preprocessor
    • Comfy dtype: SEGS_PREPROCESSOR
    • The output is a preprocessed object ready for SEGS applications, specifically tailored for edge detection enhancements.
    • Python dtype: object

Usage tips

  • Infra type: CPU
  • Common nodes: unknown

Source code

class HEDPreprocessor_Provider_for_SEGS:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "safe": ("BOOLEAN", {"default": True, "label_on": "enable", "label_off": "disable"})
            }
        }
    RETURN_TYPES = ("SEGS_PREPROCESSOR",)
    FUNCTION = "doit"

    CATEGORY = "InspirePack/SEGS/ControlNet"

    def doit(self, safe):
        obj = HED_Preprocessor_wrapper(safe, "HEDPreprocessor")
        return (obj, )