BasicPipe -> DetailerPipe

Documentation

• Class name: BasicPipeToDetailerPipe
• Category: ImpactPack/Pipe
• Output node: False

This node is designed to transform a basic pipeline configuration into a more detailed pipeline configuration, enhancing its capabilities and allowing for more complex operations.

Input types

Required

• basic_pipe
  • The basic pipeline configuration to be transformed into a detailed pipeline configuration. It serves as the foundation for the enhancement process.
  • Comfy dtype: BASIC_PIPE
  • Python dtype: Tuple[MODEL, CLIP, VAE, CONDITIONING, CONDITIONING]
• bbox_detector
  • A bounding box detector component to be included in the detailed pipeline configuration.
  • Comfy dtype: BBOX_DETECTOR
  • Python dtype: BBOX_DETECTOR
• wildcard
  • A wildcard string that allows for dynamic customization of the detailed pipeline configuration.
  • Comfy dtype: STRING
  • Python dtype: str
• Select to add LoRA
  • Allows the selection of a LoRA component to be added to the detailed pipeline configuration, enhancing its functionality.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• Select to add Wildcard
  • Enables the selection of an additional wildcard component to be added to the detailed pipeline configuration for further customization.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str

Optional

• sam_model_opt
  • An optional SAM model component that can be included in the detailed pipeline configuration for enhanced modeling capabilities.
  • Comfy dtype: SAM_MODEL
  • Python dtype: SAM_MODEL
• segm_detector_opt
  • An optional segmentation detector component that can be included in the detailed pipeline configuration for improved segmentation capabilities.
  • Comfy dtype: SEGM_DETECTOR
  • Python dtype: SEGM_DETECTOR
• detailer_hook
  • An optional hook component that can be included in the detailed pipeline configuration for customized processing and enhancements.
  • Comfy dtype: DETAILER_HOOK
  • Python dtype: DETAILER_HOOK

Output types

• detailer_pipe
  • Comfy dtype: DETAILER_PIPE
  • The resulting detailed pipeline configuration, which includes the basic pipeline components along with the specified enhancements.
  • Python dtype: Tuple[MODEL, CLIP, VAE, CONDITIONING, CONDITIONING, STRING, BBOX_DETECTOR, SEGM_DETECTOR, SAM_MODEL, DETAILER_HOOK, NoneType, NoneType, NoneType, NoneType]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class BasicPipeToDetailerPipe:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {"basic_pipe": ("BASIC_PIPE",),
                             "bbox_detector": ("BBOX_DETECTOR", ),
                             "wildcard": ("STRING", {"multiline": True, "dynamicPrompts": False}),
                             "Select to add LoRA": (["Select the LoRA to add to the text"] + folder_paths.get_filename_list("loras"),),
                             "Select to add Wildcard": (["Select the Wildcard to add to the text"],),
                             },
                "optional": {
                    "sam_model_opt": ("SAM_MODEL", ),
                    "segm_detector_opt": ("SEGM_DETECTOR",),
                    "detailer_hook": ("DETAILER_HOOK",),
                    },
                }

    RETURN_TYPES = ("DETAILER_PIPE", )
    RETURN_NAMES = ("detailer_pipe", )
    FUNCTION = "doit"

    CATEGORY = "ImpactPack/Pipe"

    def doit(self, *args, **kwargs):
        basic_pipe = kwargs['basic_pipe']
        bbox_detector = kwargs['bbox_detector']
        wildcard = kwargs['wildcard']
        sam_model_opt = kwargs.get('sam_model_opt', None)
        segm_detector_opt = kwargs.get('segm_detector_opt', None)
        detailer_hook = kwargs.get('detailer_hook', None)

        model, clip, vae, positive, negative = basic_pipe
        pipe = model, clip, vae, positive, negative, wildcard, bbox_detector, segm_detector_opt, sam_model_opt, detailer_hook, None, None, None, None
        return (pipe, )