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Pack SDXL Tuple

Documentation

  • Class name: Pack SDXL Tuple
  • Category: Efficiency Nodes/Misc
  • Output node: False

The Pack SDXL Tuple node is designed to aggregate multiple model and conditioning parameters into a single, structured tuple. This facilitates the efficient handling and transfer of a comprehensive set of parameters between different stages of a generative AI pipeline, particularly in scenarios involving base and refiner models along with their respective conditioning inputs.

Input types

Required

  • base_model
    • Represents the base generative model to be included in the tuple, playing a crucial role in the initial stages of generation.
    • Comfy dtype: MODEL
    • Python dtype: str
  • base_clip
    • Specifies the base CLIP model used for guiding the generation towards the desired outcome.
    • Comfy dtype: CLIP
    • Python dtype: str
  • base_positive
    • Defines positive conditioning inputs for the base model, influencing the direction of content generation.
    • Comfy dtype: CONDITIONING
    • Python dtype: str
  • base_negative
    • Describes negative conditioning inputs for the base model, used to steer away from undesired content.
    • Comfy dtype: CONDITIONING
    • Python dtype: str
  • refiner_model
    • Represents the refiner model that fine-tunes or enhances the output of the base model.
    • Comfy dtype: MODEL
    • Python dtype: str
  • refiner_clip
    • Specifies the refiner CLIP model used for additional guidance in the refining stage.
    • Comfy dtype: CLIP
    • Python dtype: str
  • refiner_positive
    • Defines positive conditioning inputs for the refiner model, further directing the refinement process.
    • Comfy dtype: CONDITIONING
    • Python dtype: str
  • refiner_negative
    • Describes negative conditioning inputs for the refiner model, helping to eliminate undesired aspects in the refinement stage.
    • Comfy dtype: CONDITIONING
    • Python dtype: str

Output types

  • SDXL_TUPLE
    • Comfy dtype: SDXL_TUPLE
    • The structured tuple containing all specified models and conditioning inputs, ready for use in subsequent processing stages.
    • Python dtype: Tuple[str, str, str, str, str, str, str, str]

Usage tips

Source code

class TSC_Pack_SDXL_Tuple:

    @classmethod
    def INPUT_TYPES(cls):
        return {"required": {"base_model": ("MODEL",),
                             "base_clip": ("CLIP",),
                             "base_positive": ("CONDITIONING",),
                             "base_negative": ("CONDITIONING",),
                             "refiner_model": ("MODEL",),
                             "refiner_clip": ("CLIP",),
                             "refiner_positive": ("CONDITIONING",),
                             "refiner_negative": ("CONDITIONING",),},}

    RETURN_TYPES = ("SDXL_TUPLE",)
    RETURN_NAMES = ("SDXL_TUPLE",)
    FUNCTION = "pack_sdxl_tuple"
    CATEGORY = "Efficiency Nodes/Misc"

    def pack_sdxl_tuple(self, base_model, base_clip, base_positive, base_negative,
                        refiner_model, refiner_clip, refiner_positive, refiner_negative):
        return ((base_model, base_clip, base_positive, base_negative,
                 refiner_model, refiner_clip, refiner_positive, refiner_negative),)