LatentMultiply

Documentation

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

The LatentMultiply node is designed to scale the latent representation of samples by a specified multiplier. This operation allows for the adjustment of the intensity or magnitude of features within the latent space, enabling fine-tuning of generated content or the exploration of variations within a given latent direction.

Input types

Required

• samples
  • The 'samples' parameter represents the latent representations to be scaled. It is crucial for defining the input data on which the multiplication operation will be performed.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]
• multiplier
  • The 'multiplier' parameter specifies the scaling factor to be applied to the latent samples. It plays a key role in adjusting the magnitude of the latent features, allowing for nuanced control over the generated output.
  • Comfy dtype: FLOAT
  • Python dtype: float

Output types

• latent
  • Comfy dtype: LATENT
  • The output is a modified version of the input latent samples, scaled by the specified multiplier. This allows for the exploration of variations within the latent space by adjusting the intensity of its features.
  • Python dtype: Tuple[Dict[str, torch.Tensor]]

Usage tips

• Infra type: GPU
• Common nodes: unknown

Source code

class LatentMultiply:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": { "samples": ("LATENT",),
                              "multiplier": ("FLOAT", {"default": 1.0, "min": -10.0, "max": 10.0, "step": 0.01}),
                             }}

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

    CATEGORY = "latent/advanced"

    def op(self, samples, multiplier):
        samples_out = samples.copy()

        s1 = samples["samples"]
        samples_out["samples"] = s1 * multiplier
        return (samples_out,)