Slerp Latents

Documentation

• Class name: BNK_SlerpLatent
• Category: latent
• Output node: False

The BNK_SlerpLatent node performs spherical linear interpolation (SLERP) between two latent spaces, allowing for smooth transitions and interpolations between different latent representations. It can optionally apply a mask to selectively interpolate parts of the latent spaces.

Input types

Required

• latents1
  • The first set of latent representations to interpolate from. It serves as the starting point for the interpolation process.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]
• factor
  • A scalar factor determining the interpolation's progress between the two latent spaces. A factor of 0 returns the first latent space, while a factor of 1 returns the second.
  • Comfy dtype: FLOAT
  • Python dtype: float

Optional

• latents2
  • The second set of latent representations to interpolate towards. This optional parameter allows for interpolation between two distinct latent spaces.
  • Comfy dtype: LATENT
  • Python dtype: Optional[Dict[str, torch.Tensor]]
• mask
  • An optional mask to apply during interpolation, enabling selective blending of the latent spaces based on the mask's pattern.
  • Comfy dtype: MASK
  • Python dtype: Optional[torch.Tensor]

Output types

• latent
  • Comfy dtype: LATENT
  • The interpolated latent representation, smoothly blending between the two input latent spaces according to the specified factor and optional mask.
  • Python dtype: Tuple[Dict[str, torch.Tensor]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class LatentSlerp:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "latents1":("LATENT",),
                "factor": ("FLOAT", {"default": .5, "min": 0.0, "max": 1.0, "step": 0.01}),
            },
            "optional" :{
                "latents2":("LATENT",),
                "mask": ("MASK", ),
            }}

    RETURN_TYPES = ("LATENT",)
    FUNCTION = "slerp_latents"

    CATEGORY = "latent"

    def slerp_latents(self, latents1, factor, latents2=None, mask=None):
        s = latents1.copy()
        if latents2 is None:
            return (s,)
        if latents1["samples"].shape != latents2["samples"].shape:
            print("warning, shapes in LatentSlerp not the same, ignoring")
            return (s,)
        slerped = slerp(factor, latents1["samples"].clone(), latents2["samples"].clone())
        if mask is not None:
            mask = prepare_mask(mask, slerped.shape)
            slerped = mask * slerped + (1-mask) * latents1["samples"]
        s["samples"] = slerped
        return (s,)