LatentAdd

Documentation

• Class name: LatentAdd
• Category: latent/advanced
• Output node: False

The LatentAdd node is designed for the addition of two latent representations. It facilitates the combination of features or characteristics encoded in these representations by performing element-wise addition.

Input types

Required

• samples1
  • The first set of latent samples to be added. It represents one of the inputs whose features are to be combined with another set of latent samples.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]
• samples2
  • The second set of latent samples to be added. It serves as the other input whose features are combined with the first set of latent samples through element-wise addition.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]

Output types

• latent
  • Comfy dtype: LATENT
  • The result of the element-wise addition of two latent samples, representing a new set of latent samples that combines the features of both inputs.
  • Python dtype: Tuple[Dict[str, torch.Tensor]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class LatentAdd:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples1": ("LATENT",), "samples2": ("LATENT",)}}

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

    CATEGORY = "latent/advanced"

    def op(self, samples1, samples2):
        samples_out = samples1.copy()

        s1 = samples1["samples"]
        s2 = samples2["samples"]

        s2 = reshape_latent_to(s1.shape, s2)
        samples_out["samples"] = s1 + s2
        return (samples_out,)