Noise Layer [Add Weighted] 🎭🅐🅓

Documentation

• Class name: ADE_NoiseLayerAddWeighted
• Category: Animate Diff 🎭🅐🅓/noise layers
• Output node: False

The ADE_NoiseLayerAddWeighted node specializes in enhancing the noise layering process by applying a weighted addition of noise to an existing noise layer. It leverages a balance multiplier to fine-tune the impact of new noise on the existing noise structure, aiming to achieve a more controlled and nuanced noise integration.

Input types

Required

• batch_offset
  • Specifies the offset for batch processing, affecting how noise is applied across different batches.
  • Comfy dtype: INT
  • Python dtype: int
• noise_type
  • Defines the type of noise to be added, influencing the characteristics of the noise applied.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• seed_gen_override
  • Allows for overriding the default seed generation mechanism, enabling custom noise generation patterns.
  • Comfy dtype: COMBO[STRING]
  • Python dtype: str
• seed_offset
  • Determines the offset applied to the seed value, facilitating varied noise generation outcomes.
  • Comfy dtype: INT
  • Python dtype: int
• noise_weight
  • Controls the weight of the new noise being added, allowing for adjustment of the noise's influence on the existing layer.
  • Comfy dtype: FLOAT
  • Python dtype: float
• balance_multiplier
  • Adjusts the balance between the old and new noise, fine-tuning the overall effect of noise addition.
  • Comfy dtype: FLOAT
  • Python dtype: float

Optional

• prev_noise_layers
  • Represents the previous state of noise layers, enabling sequential layering of noise for complex effects.
  • Comfy dtype: NOISE_LAYERS
  • Python dtype: NoiseLayerGroup
• mask_optional
  • An optional mask that can be applied to selectively influence the noise addition process, providing further control over the noise characteristics.
  • Comfy dtype: MASK
  • Python dtype: Tensor
• seed_override
  • Provides an option to override the seed value used for noise generation, offering control over the randomness aspect.
  • Comfy dtype: INT
  • Python dtype: int

Output types

• noise_layers
  • Comfy dtype: NOISE_LAYERS
  • Outputs the updated noise layer group after the addition of the weighted noise layer, reflecting the new state of noise layering.
  • Python dtype: NoiseLayerGroup

Usage tips

• Infra type: CPU
• Common nodes: unknown

Source code

class NoiseLayerAddWeightedNode:
    @classmethod
    def INPUT_TYPES(s):
        return {
            "required": {
                "batch_offset": ("INT", {"default": 0, "min": 0, "max": BIGMAX}),
                "noise_type": (NoiseLayerType.LIST,),
                "seed_gen_override": (SeedNoiseGeneration.LIST_WITH_OVERRIDE,),
                "seed_offset": ("INT", {"default": 0, "min": BIGMIN, "max": BIGMAX}),
                "noise_weight": ("FLOAT", {"default": 0.5, "min": 0.0, "max": 10.0, "step": 0.001}),
                "balance_multiplier": ("FLOAT", {"default": 1.0, "min": 0.0, "step": 0.001}),
            },
            "optional": {
                "prev_noise_layers": ("NOISE_LAYERS",),
                "mask_optional": ("MASK",),
                "seed_override": ("INT", {"default": 0, "min": 0, "max": 0xffffffffffffffff, "forceInput": True}),
            }
        }

    RETURN_TYPES = ("NOISE_LAYERS",)
    CATEGORY = "Animate Diff 🎭🅐🅓/noise layers"
    FUNCTION = "create_layers"

    def create_layers(self, batch_offset: int, noise_type: str, seed_gen_override: str, seed_offset: int,
                      noise_weight: float, balance_multiplier: float,
                      prev_noise_layers: NoiseLayerGroup=None, mask_optional: Tensor=None, seed_override: int=None,):
        # prepare prev_noise_layers
        if prev_noise_layers is None:
            prev_noise_layers = NoiseLayerGroup()
        prev_noise_layers = prev_noise_layers.clone()
        # create layer
        layer = NoiseLayerAddWeighted(noise_type=noise_type, batch_offset=batch_offset, seed_gen_override=seed_gen_override, seed_offset=seed_offset,
                              seed_override=seed_override, mask=mask_optional,
                              noise_weight=noise_weight, balance_multiplier=balance_multiplier)
        prev_noise_layers.add_to_start(layer)
        return (prev_noise_layers,)