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noise

Classes:

  • AddNoise –
  • GrainFactoryBicubic –

    Bicubic scaler originally implemented in GrainFactory with a sharp parameter.

  • Grainer –

    Enum representing different grain/noise generation algorithms.

  • ScalerTwoPasses –

    Abstract scaler class that applies scaling in two passes.

Attributes:

EdgeLimits module-attribute 

EdgeLimits = tuple[
    float | Sequence[float] | bool, float | Sequence[float] | bool
]

Tuple representing lower and upper edge limits for each plane.

Format: (low, high)

Each element can be: - A float: the same limit is applied to all planes. - A sequence of floats: individual limits for each plane. - True: use the default legal range per plane. - False: no limits are applied.

GrainerLike module-attribute 

GrainerLike: TypeAlias = Grainer | GrainerPartial

Grainer-like type, which can be a single grainer or a partial grainer.

LanczosTwoPasses module-attribute 

LanczosTwoPasses = ScalerTwoPasses[Lanczos]

Lanczos resizer that applies scaling in two passes.

AbstractGrainer

Abstract grainer base class.

Methods:

__call__ 

__call__(clip: VideoNode, /, **kwargs: Any) -> VideoNode | GrainerPartial
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def __call__(self, clip: vs.VideoNode, /, **kwargs: Any) -> vs.VideoNode | GrainerPartial:
    raise NotImplementedError

AddNoise 

AddNoise(
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any
)

Bases: AddNoiseBase

Classes:

Methods:

Attributes:

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def __init__(
    self,
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any,
) -> None:
    self.strength = strength
    self.size = size
    self.sharp = sharp
    self.dynamic = dynamic
    self.temporal_average = temporal_average
    self.postprocess = postprocess
    self.protect_chroma = protect_chroma
    self.luma_scaling = luma_scaling
    self.fade_limits = fade_limits
    self.neutral_out = neutral_out
    self.kwargs = kwargs

dynamic instance-attribute 

dynamic = dynamic

fade_limits instance-attribute 

fade_limits = fade_limits

kwargs instance-attribute 

kwargs = kwargs

luma_scaling instance-attribute 

luma_scaling = luma_scaling

neutral_out instance-attribute 

neutral_out = neutral_out

postprocess instance-attribute 

postprocess = postprocess

protect_chroma instance-attribute 

protect_chroma = protect_chroma

sharp instance-attribute 

sharp = sharp

size instance-attribute 

size = size

strength instance-attribute 

strength = strength

temporal_average instance-attribute 

temporal_average = temporal_average

FBM_SIMPLEX 

FBM_SIMPLEX(
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any
)

Bases: AddNoiseBase

Methods:

Attributes:

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def __init__(
    self,
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any,
) -> None:
    self.strength = strength
    self.size = size
    self.sharp = sharp
    self.dynamic = dynamic
    self.temporal_average = temporal_average
    self.postprocess = postprocess
    self.protect_chroma = protect_chroma
    self.luma_scaling = luma_scaling
    self.fade_limits = fade_limits
    self.neutral_out = neutral_out
    self.kwargs = kwargs

dynamic instance-attribute 

dynamic = dynamic

fade_limits instance-attribute 

fade_limits = fade_limits

kwargs instance-attribute 

kwargs = kwargs

luma_scaling instance-attribute 

luma_scaling = luma_scaling

neutral_out instance-attribute 

neutral_out = neutral_out

postprocess instance-attribute 

postprocess = postprocess

protect_chroma instance-attribute 

protect_chroma = protect_chroma

sharp instance-attribute 

sharp = sharp

size instance-attribute 

size = size

strength instance-attribute 

strength = strength

temporal_average instance-attribute 

temporal_average = temporal_average

grain 

grain(
    clip: VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self
def grain(
    self,
    clip: vs.VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any,
) -> vs.VideoNode:
    scaler = (
        GrainFactoryBicubic(self.sharp + 50)
        if isinstance(self.sharp, (float, int))
        else ScalerTwoPasses[Scaler.from_param(self.sharp)]  # type: ignore[misc]
    )
    return Grainer(self._noise_type)(  # type: ignore[misc]
        clip,
        fallback(strength, self.strength),
        not fallback(dynamic, self.dynamic),
        self.size,
        scaler,  # type: ignore[arg-type]
        self.temporal_average,
        self.postprocess,
        self.fade_limits,
        self.protect_chroma,
        self.luma_scaling,
        neutral_out=self.neutral_out,
        **self.kwargs | kwargs,
    )

GAUSS 

GAUSS(
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any
)

Bases: AddNoiseBase

Methods:

Attributes:

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def __init__(
    self,
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any,
) -> None:
    self.strength = strength
    self.size = size
    self.sharp = sharp
    self.dynamic = dynamic
    self.temporal_average = temporal_average
    self.postprocess = postprocess
    self.protect_chroma = protect_chroma
    self.luma_scaling = luma_scaling
    self.fade_limits = fade_limits
    self.neutral_out = neutral_out
    self.kwargs = kwargs

dynamic instance-attribute 

dynamic = dynamic

fade_limits instance-attribute 

fade_limits = fade_limits

kwargs instance-attribute 

kwargs = kwargs

luma_scaling instance-attribute 

luma_scaling = luma_scaling

neutral_out instance-attribute 

neutral_out = neutral_out

postprocess instance-attribute 

postprocess = postprocess

protect_chroma instance-attribute 

protect_chroma = protect_chroma

sharp instance-attribute 

sharp = sharp

size instance-attribute 

size = size

strength instance-attribute 

strength = strength

temporal_average instance-attribute 

temporal_average = temporal_average

grain 

grain(
    clip: VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self
def grain(
    self,
    clip: vs.VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any,
) -> vs.VideoNode:
    scaler = (
        GrainFactoryBicubic(self.sharp + 50)
        if isinstance(self.sharp, (float, int))
        else ScalerTwoPasses[Scaler.from_param(self.sharp)]  # type: ignore[misc]
    )
    return Grainer(self._noise_type)(  # type: ignore[misc]
        clip,
        fallback(strength, self.strength),
        not fallback(dynamic, self.dynamic),
        self.size,
        scaler,  # type: ignore[arg-type]
        self.temporal_average,
        self.postprocess,
        self.fade_limits,
        self.protect_chroma,
        self.luma_scaling,
        neutral_out=self.neutral_out,
        **self.kwargs | kwargs,
    )

PERLIN 

PERLIN(
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any
)

Bases: AddNoiseBase

Methods:

Attributes:

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def __init__(
    self,
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any,
) -> None:
    self.strength = strength
    self.size = size
    self.sharp = sharp
    self.dynamic = dynamic
    self.temporal_average = temporal_average
    self.postprocess = postprocess
    self.protect_chroma = protect_chroma
    self.luma_scaling = luma_scaling
    self.fade_limits = fade_limits
    self.neutral_out = neutral_out
    self.kwargs = kwargs

dynamic instance-attribute 

dynamic = dynamic

fade_limits instance-attribute 

fade_limits = fade_limits

kwargs instance-attribute 

kwargs = kwargs

luma_scaling instance-attribute 

luma_scaling = luma_scaling

neutral_out instance-attribute 

neutral_out = neutral_out

postprocess instance-attribute 

postprocess = postprocess

protect_chroma instance-attribute 

protect_chroma = protect_chroma

sharp instance-attribute 

sharp = sharp

size instance-attribute 

size = size

strength instance-attribute 

strength = strength

temporal_average instance-attribute 

temporal_average = temporal_average

grain 

grain(
    clip: VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self
def grain(
    self,
    clip: vs.VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any,
) -> vs.VideoNode:
    scaler = (
        GrainFactoryBicubic(self.sharp + 50)
        if isinstance(self.sharp, (float, int))
        else ScalerTwoPasses[Scaler.from_param(self.sharp)]  # type: ignore[misc]
    )
    return Grainer(self._noise_type)(  # type: ignore[misc]
        clip,
        fallback(strength, self.strength),
        not fallback(dynamic, self.dynamic),
        self.size,
        scaler,  # type: ignore[arg-type]
        self.temporal_average,
        self.postprocess,
        self.fade_limits,
        self.protect_chroma,
        self.luma_scaling,
        neutral_out=self.neutral_out,
        **self.kwargs | kwargs,
    )

POISSON 

POISSON(
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any
)

Bases: AddNoiseBase

Methods:

Attributes:

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def __init__(
    self,
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any,
) -> None:
    self.strength = strength
    self.size = size
    self.sharp = sharp
    self.dynamic = dynamic
    self.temporal_average = temporal_average
    self.postprocess = postprocess
    self.protect_chroma = protect_chroma
    self.luma_scaling = luma_scaling
    self.fade_limits = fade_limits
    self.neutral_out = neutral_out
    self.kwargs = kwargs

dynamic instance-attribute 

dynamic = dynamic

fade_limits instance-attribute 

fade_limits = fade_limits

kwargs instance-attribute 

kwargs = kwargs

luma_scaling instance-attribute 

luma_scaling = luma_scaling

neutral_out instance-attribute 

neutral_out = neutral_out

postprocess instance-attribute 

postprocess = postprocess

protect_chroma instance-attribute 

protect_chroma = protect_chroma

sharp instance-attribute 

sharp = sharp

size instance-attribute 

size = size

strength instance-attribute 

strength = strength

temporal_average instance-attribute 

temporal_average = temporal_average

grain 

grain(
    clip: VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self
def grain(
    self,
    clip: vs.VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any,
) -> vs.VideoNode:
    scaler = (
        GrainFactoryBicubic(self.sharp + 50)
        if isinstance(self.sharp, (float, int))
        else ScalerTwoPasses[Scaler.from_param(self.sharp)]  # type: ignore[misc]
    )
    return Grainer(self._noise_type)(  # type: ignore[misc]
        clip,
        fallback(strength, self.strength),
        not fallback(dynamic, self.dynamic),
        self.size,
        scaler,  # type: ignore[arg-type]
        self.temporal_average,
        self.postprocess,
        self.fade_limits,
        self.protect_chroma,
        self.luma_scaling,
        neutral_out=self.neutral_out,
        **self.kwargs | kwargs,
    )

SIMPLEX 

SIMPLEX(
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any
)

Bases: AddNoiseBase

Methods:

Attributes:

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def __init__(
    self,
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any,
) -> None:
    self.strength = strength
    self.size = size
    self.sharp = sharp
    self.dynamic = dynamic
    self.temporal_average = temporal_average
    self.postprocess = postprocess
    self.protect_chroma = protect_chroma
    self.luma_scaling = luma_scaling
    self.fade_limits = fade_limits
    self.neutral_out = neutral_out
    self.kwargs = kwargs

dynamic instance-attribute 

dynamic = dynamic

fade_limits instance-attribute 

fade_limits = fade_limits

kwargs instance-attribute 

kwargs = kwargs

luma_scaling instance-attribute 

luma_scaling = luma_scaling

neutral_out instance-attribute 

neutral_out = neutral_out

postprocess instance-attribute 

postprocess = postprocess

protect_chroma instance-attribute 

protect_chroma = protect_chroma

sharp instance-attribute 

sharp = sharp

size instance-attribute 

size = size

strength instance-attribute 

strength = strength

temporal_average instance-attribute 

temporal_average = temporal_average

grain 

grain(
    clip: VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self
def grain(
    self,
    clip: vs.VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any,
) -> vs.VideoNode:
    scaler = (
        GrainFactoryBicubic(self.sharp + 50)
        if isinstance(self.sharp, (float, int))
        else ScalerTwoPasses[Scaler.from_param(self.sharp)]  # type: ignore[misc]
    )
    return Grainer(self._noise_type)(  # type: ignore[misc]
        clip,
        fallback(strength, self.strength),
        not fallback(dynamic, self.dynamic),
        self.size,
        scaler,  # type: ignore[arg-type]
        self.temporal_average,
        self.postprocess,
        self.fade_limits,
        self.protect_chroma,
        self.luma_scaling,
        neutral_out=self.neutral_out,
        **self.kwargs | kwargs,
    )

grain 

grain(
    clip: VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any
) -> VideoNode
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@inject_self
def grain(
    self,
    clip: vs.VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any,
) -> vs.VideoNode:
    scaler = (
        GrainFactoryBicubic(self.sharp + 50)
        if isinstance(self.sharp, (float, int))
        else ScalerTwoPasses[Scaler.from_param(self.sharp)]  # type: ignore[misc]
    )
    return Grainer(self._noise_type)(  # type: ignore[misc]
        clip,
        fallback(strength, self.strength),
        not fallback(dynamic, self.dynamic),
        self.size,
        scaler,  # type: ignore[arg-type]
        self.temporal_average,
        self.postprocess,
        self.fade_limits,
        self.protect_chroma,
        self.luma_scaling,
        neutral_out=self.neutral_out,
        **self.kwargs | kwargs,
    )

AddNoiseBase 

AddNoiseBase(
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any
)

Methods:

Attributes:

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def __init__(
    self,
    strength: float | tuple[float, float] = 0.25,
    size: float | tuple[float, float] = (1.0, 1.0),
    sharp: float | ScalerLike = Lanczos,
    dynamic: bool = True,
    temporal_average: int | tuple[float, int] = (0.0, 1),
    postprocess: Any | None = None,
    protect_chroma: bool = True,
    luma_scaling: float | None = None,
    fade_limits: bool | Any = True,
    *,
    matrix: Any | None = None,
    kernel: Any = None,
    neutral_out: bool = False,
    **kwargs: Any,
) -> None:
    self.strength = strength
    self.size = size
    self.sharp = sharp
    self.dynamic = dynamic
    self.temporal_average = temporal_average
    self.postprocess = postprocess
    self.protect_chroma = protect_chroma
    self.luma_scaling = luma_scaling
    self.fade_limits = fade_limits
    self.neutral_out = neutral_out
    self.kwargs = kwargs

dynamic instance-attribute 

dynamic = dynamic

fade_limits instance-attribute 

fade_limits = fade_limits

kwargs instance-attribute 

kwargs = kwargs

luma_scaling instance-attribute 

luma_scaling = luma_scaling

neutral_out instance-attribute 

neutral_out = neutral_out

postprocess instance-attribute 

postprocess = postprocess

protect_chroma instance-attribute 

protect_chroma = protect_chroma

sharp instance-attribute 

sharp = sharp

size instance-attribute 

size = size

strength instance-attribute 

strength = strength

temporal_average instance-attribute 

temporal_average = temporal_average

grain 

grain(
    clip: VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any
) -> VideoNode
Source code 
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@inject_self
def grain(
    self,
    clip: vs.VideoNode,
    strength: float | tuple[float, float] | None = None,
    dynamic: bool | None = None,
    **kwargs: Any,
) -> vs.VideoNode:
    scaler = (
        GrainFactoryBicubic(self.sharp + 50)
        if isinstance(self.sharp, (float, int))
        else ScalerTwoPasses[Scaler.from_param(self.sharp)]  # type: ignore[misc]
    )
    return Grainer(self._noise_type)(  # type: ignore[misc]
        clip,
        fallback(strength, self.strength),
        not fallback(dynamic, self.dynamic),
        self.size,
        scaler,  # type: ignore[arg-type]
        self.temporal_average,
        self.postprocess,
        self.fade_limits,
        self.protect_chroma,
        self.luma_scaling,
        neutral_out=self.neutral_out,
        **self.kwargs | kwargs,
    )

GrainFactoryBicubic 

GrainFactoryBicubic(sharp: float = 50, **kwargs: Any)

Bases: BicubicAuto

Bicubic scaler originally implemented in GrainFactory with a sharp parameter.

Initialize the scaler with optional arguments.

Parameters:

  • sharp

    (float, default: 50 ) –

    Sharpness of the scaler. Defaults to 50 which corresponds to Catrom scaling.

  • **kwargs

    (Any, default: {} ) –

    Keyword arguments that configure the internal scaling behavior.

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def __init__(self, sharp: float = 50, **kwargs: Any) -> None:
    """
    Initialize the scaler with optional arguments.

    Args:
        sharp: Sharpness of the scaler. Defaults to 50 which corresponds to Catrom scaling.
        **kwargs: Keyword arguments that configure the internal scaling behavior.
    """
    super().__init__(sharp / -50 + 1, None, **kwargs)

Grainer

Bases: AbstractGrainer, CustomEnum

Enum representing different grain/noise generation algorithms.

Methods:

  • __call__ –

    Apply grain to a clip using the selected graining method.

Attributes:

FBM_SIMPLEX class-attribute instance-attribute 

FBM_SIMPLEX = 3

Fractional Brownian Motion based on Simplex noise. Built-in vs-noise plugin.

GAUSS class-attribute instance-attribute 

GAUSS = 0

Gaussian noise. Built-in vs-noise plugin.

PERLIN class-attribute instance-attribute 

PERLIN = 1

Perlin noise. Built-in vs-noise plugin.

PLACEBO class-attribute instance-attribute 

PLACEBO = auto()

Grain effect provided by the libplacebo rendering library.

POISSON class-attribute instance-attribute 

POISSON = 4

Poisson-distributed noise. Built-in vs-noise plugin.

SIMPLEX class-attribute instance-attribute 

SIMPLEX = 2

Simplex noise. Built-in vs-noise plugin.

__call__ 

__call__(
    clip: VideoNode,
    /,
    strength: float | tuple[float, float] = ...,
    static: bool = False,
    scale: float | tuple[float, float] = 1.0,
    scaler: ScalerLike = LanczosTwoPasses,
    temporal: float | tuple[float, int] = (0.0, 0),
    post_process: _PostProcessFunc | Iterable[_PostProcessFunc] | None = None,
    protect_edges: bool | EdgeLimits = True,
    protect_neutral_chroma: bool | None = None,
    luma_scaling: float | None = None,
    **kwargs: Any,
) -> VideoNode

__call__(
    *,
    strength: float | tuple[float, float] = ...,
    static: bool = False,
    scale: float | tuple[float, float] = 1.0,
    scaler: ScalerLike = LanczosTwoPasses,
    temporal: float | tuple[float, int] = (0.0, 0),
    post_process: _PostProcessFunc | Iterable[_PostProcessFunc] | None = None,
    protect_edges: bool | EdgeLimits = True,
    protect_neutral_chroma: bool | None = None,
    luma_scaling: float | None = None,
    **kwargs: Any
) -> GrainerPartial

__call__(
    clip: VideoNode,
    /,
    strength: float | tuple[float, float] = ...,
    static: bool = False,
    scale: float | tuple[float, float] = 1.0,
    scaler: ScalerLike = LanczosTwoPasses,
    temporal: float | tuple[float, int] = (0.0, 0),
    post_process: _PostProcessFunc | Iterable[_PostProcessFunc] | None = None,
    protect_edges: bool | EdgeLimits = True,
    protect_neutral_chroma: bool | None = None,
    luma_scaling: float | None = None,
    *,
    size: int | tuple[float | None, float | None] | None = (2.0, 2.0),
    **kwargs: Any,
) -> VideoNode

__call__(
    *,
    strength: float | tuple[float, float] = ...,
    static: bool = False,
    scale: float | tuple[float, float] = 1.0,
    scaler: ScalerLike = LanczosTwoPasses,
    temporal: float | tuple[float, int] = (0.0, 0),
    post_process: _PostProcessFunc | Iterable[_PostProcessFunc] | None = None,
    protect_edges: bool | EdgeLimits = True,
    protect_neutral_chroma: bool | None = None,
    luma_scaling: float | None = None,
    size: int | tuple[float | None, float | None] | None = (2.0, 2.0),
    **kwargs: Any
) -> GrainerPartial

__call__(
    clip: VideoNode,
    /,
    strength: float | Sequence[float] = ...,
    *,
    scale: float | tuple[float, float] = 1.0,
    scaler: ScalerLike = LanczosTwoPasses,
    temporal: float | tuple[float, int] = (0.0, 0),
    post_process: _PostProcessFunc | Iterable[_PostProcessFunc] | None = None,
    protect_edges: bool | EdgeLimits = True,
    protect_neutral_chroma: bool | None = None,
    luma_scaling: float | None = None,
    **kwargs: Any,
) -> VideoNode

__call__(
    *,
    strength: float | Sequence[float] = ...,
    scale: float | tuple[float, float] = 1.0,
    scaler: ScalerLike = LanczosTwoPasses,
    temporal: float | tuple[float, int] = (0.0, 0),
    post_process: _PostProcessFunc | Iterable[_PostProcessFunc] | None = None,
    protect_edges: bool | EdgeLimits = True,
    protect_neutral_chroma: bool | None = None,
    luma_scaling: float | None = None,
    **kwargs: Any
) -> GrainerPartial

__call__(
    clip: VideoNode,
    /,
    strength: float | tuple[float, float] = ...,
    static: bool = False,
    scale: float | tuple[float, float] = 1.0,
    scaler: ScalerLike = LanczosTwoPasses,
    temporal: float | tuple[float, int] = (0.0, 0),
    post_process: _PostProcessFunc | Iterable[_PostProcessFunc] | None = None,
    protect_edges: bool | EdgeLimits = True,
    protect_neutral_chroma: bool | None = None,
    luma_scaling: float | None = None,
    **kwargs: Any,
) -> VideoNode

__call__(
    *,
    strength: float | tuple[float, float] = ...,
    static: bool = False,
    scale: float | tuple[float, float] = 1.0,
    scaler: ScalerLike = LanczosTwoPasses,
    temporal: float | tuple[float, int] = (0.0, 0),
    post_process: _PostProcessFunc | Iterable[_PostProcessFunc] | None = None,
    protect_edges: bool | EdgeLimits = True,
    protect_neutral_chroma: bool | None = None,
    luma_scaling: float | None = None,
    **kwargs: Any
) -> GrainerPartial

__call__(
    clip: VideoNode | MissingT = MISSING,
    /,
    strength: float | Sequence[float] = 0,
    static: bool = False,
    scale: float | tuple[float, float] = 1.0,
    scaler: ScalerLike = LanczosTwoPasses,
    temporal: float | tuple[float, int] = (0.0, 0),
    post_process: _PostProcessFunc | Iterable[_PostProcessFunc] | None = None,
    protect_edges: bool | EdgeLimits = True,
    protect_neutral_chroma: bool | None = None,
    luma_scaling: float | None = None,
    **kwargs: Any,
) -> VideoNode | GrainerPartial

Apply grain to a clip using the selected graining method.

If no clip is passed, a partially applied grainer with the provided arguments is returned instead.

Example usage 
# For PERLIN, SIMPLEX, and FBM_SIMPLEX, it is recommended to use `size` instead of `scale`,
# as `size` allows for direct internal customization of each grain type.
grained = Grainer.PERLIN(clip, (1.65, 0.65), temporal=(0.25, 2), luma_scaling=4, size=3.0, seed=333)

Parameters:

  • clip

    (VideoNode | MissingT, default: MISSING ) –

    The input clip to apply grain to. If omitted, returns a partially applied grainer.

  • strength

    (float | Sequence[float], default: 0 ) –

    Grain strength. A single float applies uniform strength to all planes. A sequence allows per-plane control.

  • static

    (bool, default: False ) –

    If True, the grain pattern is static (unchanging across frames).

  • scale

    (float | tuple[float, float], default: 1.0 ) –

    Scaling divisor for the grain layer. Can be a float (uniform scaling) or a tuple (width, height scaling).

  • scaler

    (ScalerLike, default: LanczosTwoPasses ) –

    Scaler used to resize the grain layer when scale is not 1.0.

  • temporal

    (float | tuple[float, int], default: (0.0, 0) ) –

    Temporal grain smoothing parameters. Either a float (weight) or a tuple of (weight, radius).

  • post_process

    (_PostProcessFunc | Iterable[_PostProcessFunc] | None, default: None ) –

    One or more functions applied after grain generation (and temporal smoothing, if used).

  • protect_edges

    (bool | EdgeLimits, default: True ) –

    Protects edge regions of each plane from graining.

    • True: Use legal range based on clip format.
    • False: Disable edge protection.
    • Tuple: Specify custom edge limits per plane (see EdgeLimits).

  • protect_neutral_chroma

    (bool | None, default: None ) –

    Whether to disable graining on neutral chroma.

  • luma_scaling

    (float | None, default: None ) –

    Sensitivity of the luma-adaptive graining mask. Higher values reduce grain in brighter areas; negative values invert behavior.

  • **kwargs

    (Any, default: {} ) –

    Additional arguments to pass to the graining function or additional advanced options:

    • temporal_avg_func: Temporal average function to use instead of the default standard mean.
    • protect_edges_blend: Blend range (float) to soften edge protection thresholds.
    • protect_neutral_chroma_blend: Blend range (float) for neutral chroma protection.
    • neutral_out: (Boolean) Output the neutral layer instead of the merged clip.

Returns:

  • VideoNode | GrainerPartial –

    Grained video clip, or a GrainerPartial if clip is not provided.

Source code 
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def __call__(
    self,
    clip: vs.VideoNode | MissingT = MISSING,
    /,
    strength: float | Sequence[float] = 0,
    static: bool = False,
    scale: float | tuple[float, float] = 1.0,
    scaler: ScalerLike = LanczosTwoPasses,
    temporal: float | tuple[float, int] = (0.0, 0),
    post_process: _PostProcessFunc | Iterable[_PostProcessFunc] | None = None,
    protect_edges: bool | EdgeLimits = True,
    protect_neutral_chroma: bool | None = None,
    luma_scaling: float | None = None,
    **kwargs: Any,
) -> vs.VideoNode | GrainerPartial:
    """
    Apply grain to a clip using the selected graining method.

    If no clip is passed, a partially applied grainer with the provided arguments is returned instead.

    Example usage:
        ```py
        # For PERLIN, SIMPLEX, and FBM_SIMPLEX, it is recommended to use `size` instead of `scale`,
        # as `size` allows for direct internal customization of each grain type.
        grained = Grainer.PERLIN(clip, (1.65, 0.65), temporal=(0.25, 2), luma_scaling=4, size=3.0, seed=333)
        ```

    Args:
        clip:
            The input clip to apply grain to. If omitted, returns a partially applied grainer.
        strength:
            Grain strength.
            A single float applies uniform strength to all planes.
            A sequence allows per-plane control.

        static:
            If True, the grain pattern is static (unchanging across frames).
        scale:
            Scaling divisor for the grain layer.
            Can be a float (uniform scaling) or a tuple (width, height scaling).
        scaler:
            Scaler used to resize the grain layer when `scale` is not 1.0.
        temporal:
            Temporal grain smoothing parameters. Either a float (weight) or a tuple of (weight, radius).
        post_process:
            One or more functions applied after grain generation (and temporal smoothing, if used).
        protect_edges:
            Protects edge regions of each plane from graining.

               - True: Use legal range based on clip format.
               - False: Disable edge protection.
               - Tuple: Specify custom edge limits per plane (see `EdgeLimits`).

        protect_neutral_chroma:
            Whether to disable graining on neutral chroma.
        luma_scaling:
            Sensitivity of the luma-adaptive graining mask.
            Higher values reduce grain in brighter areas; negative values invert behavior.
        **kwargs:
            Additional arguments to pass to the graining function or additional advanced options:

               - ``temporal_avg_func``: Temporal average function to use instead of the default standard mean.
               - ``protect_edges_blend``: Blend range (float) to soften edge protection thresholds.
               - ``protect_neutral_chroma_blend``: Blend range (float) for neutral chroma protection.
               - ``neutral_out``: (Boolean) Output the neutral layer instead of the merged clip.

    Returns:
        Grained video clip, or a `GrainerPartial` if `clip` is not provided.
    """
    kwargs.update(
        strength=strength,
        scale=scale,
        scaler=scaler,
        temporal=temporal,
        protect_edges=protect_edges,
        post_process=post_process,
        protect_neutral_chroma=protect_neutral_chroma,
        luma_scaling=luma_scaling,
    )

    if clip is MISSING:
        return GrainerPartial(self, **kwargs)

    assert check_variable(clip, self.name)

    if self == Grainer.PLACEBO:
        assert static is False, "PlaceboGrain does not support static noise!"

        grained = _apply_grainer(
            clip,
            lambda clip, strength, planes, **kwds: placebo_deband(
                clip, 8, 0.0, strength, planes, iterations=1, **kwds
            ),
            **kwargs,
            func=self.name,
        )
    else:
        if not isinstance(size := kwargs.pop("size", (None, None)), tuple):
            size = (size, size)

        kwargs.update(xsize=size[0], ysize=size[1])

        grained = _apply_grainer(
            clip,
            lambda clip, strength, planes, **kwds: core.noise.Add(
                clip,
                *strength[:2],  # type: ignore[misc]
                type=self.value,
                constant=static,
                **kwds,
            ),
            **kwargs,
            func=self.name,
        )

    return grained

GrainerPartial 

GrainerPartial(grainer: Grainer, **kwargs: Any)

Bases: AbstractGrainer

A partially-applied grainer wrapper.

Stores a grainer function, allowing it to be reused with different clips.

Parameters:

  • grainer

    (Grainer) –

    Grainer enumeration.

  • **kwargs

    (Any, default: {} ) –

    Arguments for the specified grainer.

Methods:

  • __call__ –

    Apply the grainer to the given clip with optional argument overrides.

Attributes:

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def __init__(self, grainer: Grainer, **kwargs: Any) -> None:
    """
    Stores a grainer function, allowing it to be reused with different clips.

    Args:
        grainer: [Grainer][vsdeband.noise.Grainer] enumeration.
        **kwargs: Arguments for the specified grainer.
    """
    self.grainer = grainer
    self.kwargs = kwargs

grainer instance-attribute 

grainer = grainer

kwargs instance-attribute 

kwargs = kwargs

__call__ 

__call__(clip: VideoNode, /, **kwargs: Any) -> VideoNode

Apply the grainer to the given clip with optional argument overrides.

Parameters:

  • clip

    (VideoNode) –

    Clip to be processed.

  • **kwargs

    (Any, default: {} ) –

    Additional keyword arguments to override or extend the stored ones.

Returns:

  • VideoNode –

    Processed clip.

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def __call__(self, clip: vs.VideoNode, /, **kwargs: Any) -> vs.VideoNode:
    """
    Apply the grainer to the given clip with optional argument overrides.

    Args:
        clip: Clip to be processed.
        **kwargs: Additional keyword arguments to override or extend the stored ones.

    Returns:
        Processed clip.
    """
    return self.grainer(clip, **self.kwargs | kwargs)

ScalerTwoPasses

Bases: BaseScalerSpecializer[_ScalerT], Scaler

Abstract scaler class that applies scaling in two passes.

Methods:

scale 

scale(
    clip: VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any
) -> VideoNode | ConstantFormatVideoNode
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def scale(
    self,
    clip: vs.VideoNode,
    width: int | None = None,
    height: int | None = None,
    shift: tuple[TopShift, LeftShift] = (0, 0),
    **kwargs: Any,
) -> vs.VideoNode | ConstantFormatVideoNode:
    assert check_variable(clip, self.__class__)

    width, height = self._wh_norm(clip, width, height)

    if width / clip.width > 1.5 or height / clip.height > 1.5:
        # If the scale is too big, we need to scale it in two passes, else the window
        # will be too big and the grain will be dampened down too much
        mod = max(clip.format.subsampling_w, clip.format.subsampling_h) << 1
        clip = super().scale(
            clip, mod_x((width + clip.width) / 2, mod), mod_x((height + clip.height) / 2, mod), **kwargs
        )

    return super().scale(clip, width, height, (0, 0), **kwargs)