Skip to content

ToDetailerPipe

Documentation

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

The ToDetailerPipe node is designed to transform various model components and configurations into a detailed pipeline format. It focuses on enhancing the specificity and impact of model outputs by incorporating additional conditioning and refinement processes.

Input types

Required

  • model
    • The 'model' parameter represents the core model to be included in the detailer pipeline, serving as the foundation for further enhancements and conditioning.
    • Comfy dtype: MODEL
    • Python dtype: str
  • clip
    • The 'clip' parameter specifies the CLIP model to be used in conjunction with the main model for enhanced content understanding and generation.
    • Comfy dtype: CLIP
    • Python dtype: str
  • vae
    • The 'vae' parameter involves a Variational Autoencoder (VAE) model, which is used for generating or modifying content within the pipeline.
    • Comfy dtype: VAE
    • Python dtype: str
  • positive
    • The 'positive' parameter is used for positive conditioning, influencing the model to generate content that aligns with specified positive attributes.
    • Comfy dtype: CONDITIONING
    • Python dtype: str
  • negative
    • The 'negative' parameter is used for negative conditioning, guiding the model to avoid generating content with specified negative attributes.
    • Comfy dtype: CONDITIONING
    • Python dtype: str
  • bbox_detector
    • The 'bbox_detector' parameter specifies the bounding box detector model used for identifying specific areas within images for focused processing or analysis.
    • Comfy dtype: BBOX_DETECTOR
    • Python dtype: str
  • wildcard
    • The 'wildcard' parameter allows for the inclusion of dynamic, user-defined text inputs that can influence the pipeline's processing and output generation.
    • Comfy dtype: STRING
    • Python dtype: str
  • Select to add LoRA
    • This parameter enables the selection of LoRA (Low-Rank Adaptation) techniques to be added to the text, enhancing the model's adaptability and performance on specific tasks.
    • Comfy dtype: COMBO[STRING]
    • Python dtype: List[str]
  • Select to add Wildcard
    • This parameter allows for the selection of predefined wildcard options to be added to the text, introducing variability and customization into the pipeline's output.
    • Comfy dtype: COMBO[STRING]
    • Python dtype: List[str]

Optional

  • sam_model_opt
    • The 'sam_model_opt' parameter specifies an optional SAM (Sharpness-Aware Minimization) model to enhance the detailer pipeline's ability to generate sharp and clear images.
    • Comfy dtype: SAM_MODEL
    • Python dtype: str
  • segm_detector_opt
    • The 'segm_detector_opt' parameter specifies an optional segmentation detector model used for identifying and segmenting specific parts of images for detailed processing.
    • Comfy dtype: SEGM_DETECTOR
    • Python dtype: str
  • detailer_hook
    • The 'detailer_hook' parameter allows for the inclusion of custom processing hooks within the detailer pipeline, enabling tailored modifications or enhancements to the pipeline's operation.
    • Comfy dtype: DETAILER_HOOK
    • Python dtype: str

Output types

  • detailer_pipe
    • Comfy dtype: DETAILER_PIPE
    • The output 'detailer_pipe' represents the comprehensive pipeline configuration, including all model components and settings specified through the input parameters.
    • Python dtype: tuple

Usage tips

Source code

class ToDetailerPipe:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
                     "model": ("MODEL",),
                     "clip": ("CLIP",),
                     "vae": ("VAE",),
                     "positive": ("CONDITIONING",),
                     "negative": ("CONDITIONING",),
                     "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):
        pipe = (kwargs['model'], kwargs['clip'], kwargs['vae'], kwargs['positive'], kwargs['negative'], kwargs['wildcard'], kwargs['bbox_detector'],
                kwargs.get('segm_detector_opt', None), kwargs.get('sam_model_opt', None), kwargs.get('detailer_hook', None),
                kwargs.get('refiner_model', None), kwargs.get('refiner_clip', None),
                kwargs.get('refiner_positive', None), kwargs.get('refiner_negative', None))
        return (pipe, )