Inject Noise

Documentation

• Class name: BNK_InjectNoise
• Category: latent/noise
• Output node: False

The InjectNoise node is designed to augment latent representations with noise, offering a mechanism to introduce variability and potentially enhance the generation quality of models. It supports the injection of custom noise and the application of a mask to selectively apply noise, thereby providing a versatile tool for manipulating latent spaces.

Input types

Required

• latents
  • The primary latent representation to which noise will be added. This parameter is crucial for defining the base structure that the noise will modify.
  • Comfy dtype: LATENT
  • Python dtype: Dict[str, torch.Tensor]
• strength
  • Determines the intensity of the noise applied to the latents. A higher value results in more pronounced noise effects.
  • Comfy dtype: FLOAT
  • Python dtype: float

Optional

• noise
  • An optional parameter allowing for the injection of custom noise into the latent representation.
  • Comfy dtype: LATENT
  • Python dtype: Optional[Dict[str, torch.Tensor]]
• mask
  • An optional mask that can be applied to selectively introduce noise to specific areas of the latent representation.
  • Comfy dtype: MASK
  • Python dtype: Optional[torch.Tensor]

Output types

• latent
  • Comfy dtype: LATENT
  • The modified latent representation after noise injection, potentially with selective application based on an optional mask.
  • Python dtype: Dict[str, torch.Tensor]

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class InjectNoise:
    @classmethod
    def INPUT_TYPES(s):
        return {"required": {
            "latents":("LATENT",),

            "strength": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 200.0, "step": 0.01}),
            },
            "optional":{
                "noise":  ("LATENT",),
                "mask": ("MASK", ),
            }}

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

    CATEGORY = "latent/noise"

    def inject_noise(self, latents, strength, noise=None, mask=None):
        s = latents.copy()
        if noise is None:
            return (s,)
        if latents["samples"].shape != noise["samples"].shape:
            print("warning, shapes in InjectNoise not the same, ignoring")
            return (s,)
        noised = s["samples"].clone() + noise["samples"].clone() * strength
        if mask is not None:
            mask = prepare_mask(mask, noised.shape)
            noised = mask * noised + (1-mask) * latents["samples"]
        s["samples"] = noised
        return (s,)