StableCascade_StageB_Conditioning

Documentation

• Class name: StableCascade_StageB_Conditioning
• Category: conditioning/stable_cascade
• Output node: False

This node is designed for conditioning in the context of a stable cascade process, specifically at stage B. It integrates prior information from a later stage (stage C) into the conditioning data, preparing it for subsequent processing steps.

Input types

Required

• conditioning
  • The conditioning data to be modified, incorporating prior information from stage C for enhanced processing.
  • Comfy dtype: CONDITIONING
  • Python dtype: List[Tuple[Any, Dict[str, Any]]]
• stage_c
  • The prior information from stage C, used to enrich the conditioning data with relevant context.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]

Output types

• conditioning
  • Comfy dtype: CONDITIONING
  • The modified conditioning data, now augmented with prior information from stage C.
  • Python dtype: List[Tuple[Any, Dict[str, Any]]]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class StableCascade_StageB_Conditioning:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "conditioning": ("CONDITIONING",),
                              "stage_c": ("LATENT",),
                             }}
    RETURN_TYPES = ("CONDITIONING",)

    FUNCTION = "set_prior"

    CATEGORY = "conditioning/stable_cascade"

    def set_prior(self, conditioning, stage_c):
        c = []
        for t in conditioning:
            d = t[1].copy()
            d['stable_cascade_prior'] = stage_c['samples']
            n = [t[0], d]
            c.append(n)
        return (c, )