Noisemaker API

Images are float32 tensors (0..1 range), with shape (height, width, channels). Most functions assume seamlessly tiled noise.

This is a pre-1.0 API, and may receive backwards incompatible changes.

noisemaker.generators

Noise generation interface for Noisemaker

noisemaker.generators.basic(freq, shape, ridges=False, sin=0.0, spline_order=<InterpolationType.bicubic: 3>, distrib=<ValueDistribution.uniform: 1>, corners=False, mask=None, mask_inverse=False, mask_static=False, lattice_drift=0.0, color_space=<ColorSpace.hsv: 21>, hue_range=0.125, hue_rotation=None, saturation=1.0, hue_distrib=None, brightness_distrib=None, brightness_freq=None, saturation_distrib=None, speed=1.0, time=0.0, octave_effects=None, octave=1)

Generate a single layer of scaled noise.

Parameters:

• freq (int|list[int]) – Base noise frequency. Int, or list of ints for each spatial dimension
• list[int] – Shape of noise. For 2D noise, this is [height, width, channels]
• ridges (bool) – “Crease” at midpoint values: (1 - abs(n * 2 - 1))
• sin (float) – Apply sin function to noise basis
• spline_order (int) – Spline point count. 0=Constant, 1=Linear, 2=Cosine, 3=Bicubic
• distrib (int|str|ValueDistribution) – Type of noise distribution. See ValueDistribution enum
• corners (bool) – If True, pin values to corners instead of image center
• mask (None|ValueMask) –
• mask_inverse (bool) –
• mask_static (bool) – If True, don’t animate the mask
• lattice_drift (float) – Push away from underlying lattice
• color_space (ColorSpace) –
• hue_range (float) – HSV hue range
• hue_rotation (float|None) – HSV hue bias
• saturation (float) – HSV saturation
• hue_distrib (None|int|str|ValueDistribution) – Override ValueDistribution for hue
• saturation_distrib (None|int|str|ValueDistribution) – Override ValueDistribution for saturation
• brightness_distrib (None|int|str|ValueDistribution) – Override ValueDistribution for brightness
• brightness_freq (None|int|list[int]) – Override frequency for brightness
• speed (float) – Displacement range for Z/W axis (simplex and periodic only)
• time (float) – Time argument for Z/W axis (simplex and periodic only)

Returns:

Tensor

noisemaker.generators.multires(preset, seed, freq=3, shape=None, octaves=1, ridges=False, sin=0.0, spline_order=<InterpolationType.bicubic: 3>, distrib=<ValueDistribution.uniform: 1>, corners=False, mask=None, mask_inverse=False, mask_static=False, lattice_drift=0.0, with_supersample=False, color_space=<ColorSpace.hsv: 21>, hue_range=0.125, hue_rotation=None, saturation=1.0, hue_distrib=None, saturation_distrib=None, brightness_distrib=None, brightness_freq=None, octave_blending=<OctaveBlending.falloff: 0>, octave_effects=None, post_effects=None, with_alpha=False, with_ai=False, final_effects=None, with_upscale=False, stability_model=None, time=0.0, speed=1.0, tensor=None)

Generate multi-resolution value noise. For each octave: freq increases, amplitude decreases.

Parameters:

• preset (Preset) – The Preset object being rendered
• seed (int) – The current seed (informational only, use value.set_seed to set seed)
• freq (int|list[int]) – Bottom layer frequency. Int, or list of ints for each spatial dimension
• list[int] – Shape of noise. For 2D noise, this is [height, width, channels]
• octaves (int) – Octave count. Number of multi-res layers. Typically 1-8
• ridges (bool) – Per-octave “crease” at midpoint values: (1 - abs(n * 2 - 1))
• post_ridges (bool) – Post-reduce “crease” at midpoint values: (1 - abs(n * 2 - 1))
• sin (float) – Apply sin function to noise basis
• spline_order (int) – Spline point count. 0=Constant, 1=Linear, 2=Cosine, 3=Bicubic
• distrib (int|ValueDistribution) – Type of noise distribution. See ValueDistribution enum
• corners (bool) – If True, pin values to corners instead of image center
• mask (None|ValueMask) –
• mask_inverse (bool) –
• mask_static (bool) – If True, don’t animate the mask
• lattice_drift (float) – Push away from underlying lattice
• with_supersample (bool) – Use x2 supersampling
• color_space (ColorSpace) –
• hue_range (float) – HSV hue range
• hue_rotation (float|None) – HSV hue bias
• saturation (float) – HSV saturation
• hue_distrib (None|ValueDistribution) – Override ValueDistribution for HSV hue
• saturation_distrib (None|ValueDistribution) – Override ValueDistribution for HSV saturation
• brightness_distrib (None|ValueDistribution) – Override ValueDistribution for HSV brightness
• brightness_freq (None|int|list[int]) – Override frequency for HSV brightness
• octave_blending (OctaveBlendingMethod|int) – Method for flattening octave values
• octave_effects (list[callable]) – A list of composer lambdas to invoke per-octave
• post_effects (list[callable]) – A list of composer lambdas to invoke after flattening layers
• with_alpha (bool) – Include alpha channel
• with_ai (bool) – AI: Apply image-to-image before the final effects pass
• final_effects (list[callable]) – A list of composer lambdas to invoke after everything else
• with_upscale (bool) – AI: x2 upscale final results
• stability_model (str) – AI: Override the default stability.ai model
• speed (float) – Displacement range for Z/W axis (simplex and periodic only)
• time (float) – Time argument for Z/W axis (simplex and periodic only)

Returns:

Tensor

noisemaker.value

Low-level value noise functions

noisemaker.value.blend(a, b, g)

Blend a and b values with linear interpolation.

Parameters:

• a (Tensor) –
• b (Tensor) –
• g (float|Tensor) – Blending gradient a to b (0..1)

Return Tensor:

noisemaker.value.blend_cosine(a, b, g)

Blend a and b values with cosine interpolation.

Parameters:

• a (Tensor) –
• b (Tensor) –
• g (float|Tensor) – Blending gradient a to b (0..1)

Return Tensor:

noisemaker.value.blend_cubic(a, b, c, d, g)

Blend b and c values with bi-cubic interpolation.

Parameters:

• a (Tensor) –
• b (Tensor) –
• c (Tensor) –
• d (Tensor) –
• g (float|Tensor) – Blending gradient b to c (0..1)

Return Tensor:

noisemaker.value.clamp01(tensor)

noisemaker.value.coerce_enum(value, cls)

Attempt to coerce a given string or int value into an Enum instance.

noisemaker.value.column_index(shape)

Generate a Y index for the given tensor.

[
  [ 0, 0, 0, ... ],
  [ 1, 1, 1, ... ],
  [ n, n, n, ... ],
  ...
  [ height-1, height-1, height-1, ... ]
]

Parameters:shape (list[int]) – Returns:Tensor of shape (height, width)

noisemaker.value.convolve(tensor, shape, kernel=None, with_normalize=True, alpha=1.0, time=0.0, speed=1.0)

Apply a convolution kernel to an image tensor.

image = convolve(image, shape, ValueMask.conv2d_shadow)

Parameters:

• tensor (Tensor) – An image tensor.
• shape (list[int]) –
• kernel (ValueMask) – See conv2d_* members in ValueMask enum
• with_normalize (bool) – Normalize output (True)

Paral float alpha:  

Alpha blending amount

Returns:

Tensor

noisemaker.value.distance(a, b, metric=<DistanceMetric.euclidean: 1>, sdf_sides=5)

Compute the distance from a to b, using the specified metric.

Parameters:

• a (Tensor) –
• b (Tensor) –
• metric (DistanceMetric|int|str) – Distance metric

Returns:

Tensor

noisemaker.value.freq_for_shape(freq, shape)

Given a base frequency as int, generate noise frequencies for each spatial dimension.

Parameters:

• freq (int) – Base frequency
• shape (list[int]) – List of spatial dimensions, e.g. [height, width]

noisemaker.value.normalize(tensor, signed_range=False)

Squeeze the given Tensor into a range between 0 and 1.

Parameters:

• tensor (Tensor) – An image tensor.
• signed_range (bool) – Use a range between -1 and 1.

Returns:

Tensor

noisemaker.value.normalized_sine(value)

noisemaker.value.offset(tensor, shape, x=0, y=0)

noisemaker.value.periodic_value(time, value)

Coerce the received value to animate smoothly between time values 0 and 1, by applying a sine function and scaling the result.

Parameters:

• time (float) –
• value (float|Tensor) –

noisemaker.value.pin_corners(tensor, shape, freq, corners)

Pin values to image corners, or align with image center, as per the given “corners” arg.

noisemaker.value.proportional_downsample(tensor, shape, new_shape)

Given a new shape which is evenly divisible by the old shape, shrink the image by averaging pixel values.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• new_shape (list[int]) –

noisemaker.value.refract(tensor, shape, displacement=0.5, reference_x=None, reference_y=None, warp_freq=None, spline_order=<InterpolationType.bicubic: 3>, from_derivative=False, signed_range=True, time=0.0, speed=1.0, y_from_offset=False)

Apply displacement from pixel values.

Parameters:

• tensor (Tensor) – An image tensor.
• shape (list[int]) –
• displacement (float) –
• reference_x (Tensor) – An optional horizontal displacement map.
• reference_y (Tensor) – An optional vertical displacement map.
• warp_freq (list[int]) – If given, generate new reference_x and reference_y noise with this base frequency.
• spline_order (int) – Interpolation for warp effect only. 0=Constant, 1=Linear, 2=Cosine, 3=Bicubic
• from_derivative (bool) – If True, generate X and Y offsets from noise derivatives.
• signed_range (bool) – Scale displacement values from -1..1 instead of 0..1
• y_from_offset (bool) – If True, derive Y offsets from offsetting the image

Returns:

Tensor

noisemaker.value.resample(tensor, shape, spline_order=3)

Resize an image tensor to the specified shape.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• spline_order (int) – Spline point count. 0=Constant, 1=Linear, 2=Cosine, 3=Bicubic

Returns:

Tensor

noisemaker.value.ridge(tensor)

Create a “ridge” at midpoint values. 1 - abs(n * 2 - 1)

Parameters:tensor (Tensor) – An image tensor. Returns:Tensor

noisemaker.value.row_index(shape)

Generate an X index for the given tensor.

[
  [ 0, 1, 2, ... width-1 ],
  [ 0, 1, 2, ... width-1 ],
  ... (x height)
]

Parameters:shape (list[int]) – Returns:Tensor of shape (height, width)

noisemaker.value.set_seed(seed)

noisemaker.value.simple_multires(freq, shape, octaves=1, spline_order=<InterpolationType.bicubic: 3>, distrib=<ValueDistribution.uniform: 1>, corners=False, ridges=False, mask=None, mask_inverse=False, mask_static=False, time=0.0, speed=1.0)

Generate multi-octave value noise. Unlike generators.multires, this function is single-channel and does not apply effects.

noisemaker.value.singularity(tensor, shape, diagram_type=<VoronoiDiagramType.range: 11>, **kwargs)

Return the range diagram for a single voronoi point, approximately centered.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• diagram_type (VoronoiDiagramType|int) –
• dist_metric (DistanceMetric|int) –

Additional kwargs will be sent to the voronoi metric.

noisemaker.value.value_map(tensor, shape, keepdims=False, signed_range=False, with_normalize=True)

Create a grayscale value map from the given image Tensor, based on apparent luminance.

Return value ranges between 0 and 1.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• keepdims (bool) – If True, don’t collapse the channel dimension.
• signed_range (bool) – If True, use an extended value range between -1 and 1.

Returns:

Tensor of shape (height, width), or (height, width, channels) if keepdims was True.

noisemaker.value.value_shape(shape)

noisemaker.value.values(freq, shape, distrib=<ValueDistribution.uniform: 1>, corners=False, mask=None, mask_inverse=False, mask_static=False, spline_order=<InterpolationType.bicubic: 3>, time=0.0, speed=1.0)

noisemaker.value.voronoi(tensor, shape, diagram_type=<VoronoiDiagramType.range: 11>, nth=0, dist_metric=<DistanceMetric.euclidean: 1>, sdf_sides=3, alpha=1.0, with_refract=0.0, inverse=False, xy=None, ridges_hint=False, refract_y_from_offset=True, time=0.0, speed=1.0, point_freq=3, point_generations=1, point_distrib=<PointDistribution.random: 1000000>, point_drift=0.0, point_corners=False, downsample=True)

Create a voronoi diagram, blending with input image Tensor color values.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• diagram_type (VoronoiDiagramType|int) – Diagram type (0=Off, 1=Range, 2=Color Range, 3=Indexed, 4=Color Map, 5=Blended, 6=Flow)
• nth (float) – Plot Nth nearest neighbor, or -Nth farthest
• dist_metric (DistanceMetric|int) – Voronoi distance metric
• regions (bool) – Assign colors to control points (memory intensive)
• alpha (float) – Blend with original tensor (0.0 = Original, 1.0 = Voronoi)
• with_refract (float) – Domain warp input tensor against resulting voronoi
• inverse (bool) – Invert range brightness values (does not affect hue)
• Tensor, int) xy ((Tensor,) – Bring your own x, y, and point count (You shouldn’t normally need this)
• ridges_hint (float) – Adjust output colors to match ridged multifractal output (You shouldn’t normally need this)
• downsample (bool) – Use a downsampled distance field, probably to conserve memory

Returns:

Tensor

noisemaker.effects

Low-level effects library for Noisemaker

noisemaker.effects.aberration(tensor, shape, displacement=0.005, time=0.0, speed=1.0)

Chromatic aberration

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• displacement (float) –

noisemaker.effects.adjust_brightness(tensor, shape, amount=0.125, time=0.0, speed=1.0)

noisemaker.effects.adjust_contrast(tensor, shape, amount=1.25, time=0.0, speed=1.0)

noisemaker.effects.adjust_hue(tensor, shape, amount=0.25, time=0.0, speed=1.0)

noisemaker.effects.adjust_saturation(tensor, shape, amount=0.75, time=0.0, speed=1.0)

noisemaker.effects.blend_layers(control, shape, feather=1.0, *layers)

noisemaker.effects.bloom(tensor, shape, alpha=0.5, time=0.0, speed=1.0)

Bloom effect

Input image must currently be square (sorry).

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• alpha (float) –

noisemaker.effects.blur(tensor, shape, amount=10.0, spline_order=<InterpolationType.bicubic: 3>, time=0.0, speed=1.0)

noisemaker.effects.center_mask(center, edges, shape, dist_metric=<DistanceMetric.chebyshev: 3>, power=2)

Blend two image tensors from the center to the edges.

Parameters:

• center (Tensor) –
• edges (Tensor) –
• shape (list[int]) –
• power (int) –

Returns:

Tensor

noisemaker.effects.clouds(tensor, shape, time=0.0, speed=1.0)

Top-down cloud cover effect

noisemaker.effects.color_map(tensor, shape, clut=None, horizontal=False, displacement=0.5, time=0.0, speed=1.0)

Apply a color map to an image tensor.

The color map can be a photo or whatever else.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• clut (Tensor|str) – An image tensor or filename (png/jpg only) to use as a color palette
• horizontal (bool) – Scan horizontally
• displacement (float) – Gather distance for clut

noisemaker.effects.conv_feedback(tensor, shape, iterations=50, alpha=0.5, time=0.0, speed=1.0)

Conv2d feedback loop

Parameters:tensor (Tensor) – Returns:Tensor

noisemaker.effects.crt(tensor, shape, time=0.0, speed=1.0)

Apply vintage CRT scanlines.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –

noisemaker.effects.degauss(tensor, shape, displacement=0.0625, time=0.0, speed=1.0)

noisemaker.effects.density_map(tensor, shape, time=0.0, speed=1.0)

Create a binned pixel value density map.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –

noisemaker.effects.derivative(tensor, shape, dist_metric=<DistanceMetric.euclidean: 1>, with_normalize=True, alpha=1.0, time=0.0, speed=1.0)

Extract a derivative from the given noise.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• dist_metric (DistanceMetric|int) – Derivative distance metric
• with_normalize (bool) –

Returns:

Tensor

noisemaker.effects.dla(tensor, shape, padding=2, seed_density=0.01, density=0.125, xy=None, alpha=1.0, time=0.0, speed=1.0)

Diffusion-limited aggregation. Slow.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• padding (int) –
• seed_density (float) –
• density (float) –
• xy (None|Tensor) – Pre-seeded point cloud (optional)

noisemaker.effects.erosion_worms(tensor, shape, density=50, iterations=50, contraction=1.0, alpha=0.25, inverse=False, xy_blend=False, time=0.0, speed=1.0)

WIP hydraulic erosion effect.

noisemaker.effects.expand_tile(tensor, input_shape, output_shape, with_offset=True)

noisemaker.effects.false_color(tensor, shape, horizontal=False, displacement=0.5, time=0.0, speed=1.0)

noisemaker.effects.fibers(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.frame(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.glitch(tensor, shape, time=0.0, speed=1.0)

Apply a glitch effect.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –

Returns:

Tensor

noisemaker.effects.glowing_edges(tensor, shape, sobel_metric=2, alpha=1.0, time=0.0, speed=1.0)

noisemaker.effects.glyph_map(tensor, shape, mask=None, colorize=True, zoom=1, alpha=1.0, spline_order=<InterpolationType.constant: 0>, time=0.0, speed=1.0)

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• mask (ValueMask|None) –

noisemaker.effects.grain(tensor, shape, alpha=0.25, time=0.0, speed=1.0)

noisemaker.effects.grime(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.inner_tile(tensor, shape, freq)

noisemaker.effects.jpeg_decimate(tensor, shape, iterations=25, time=0.0, speed=1.0)

Destroy an image with the power of JPEG

Parameters:tensor (Tensor) – Returns:Tensor

noisemaker.effects.kaleido(tensor, shape, sides=6, sdf_sides=5, xy=None, blend_edges=True, time=0.0, speed=1.0, point_freq=1, point_generations=1, point_distrib=<PointDistribution.random: 1000000>, point_drift=0.0, point_corners=False)

Adapted from https://github.com/patriciogonzalezvivo/thebookofshaders/blob/master/15/texture-kaleidoscope.frag

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• sides (int) – Number of sides
• dist_metric (DistanceMetric) –
• xy – Optional (x, y) coordinates for points
• blend_edges (bool) – Blend with original edge indices

noisemaker.effects.lens_distortion(tensor, shape, displacement=1.0, time=0.0, speed=1.0)

noisemaker.effects.lens_warp(tensor, shape, displacement=0.0625, time=0.0, speed=1.0)

noisemaker.effects.light_leak(tensor, shape, alpha=0.25, time=0.0, speed=1.0)

noisemaker.effects.lowpoly(tensor, shape, distrib=<PointDistribution.random: 1000000>, freq=10, time=0.0, speed=1.0, dist_metric=<DistanceMetric.euclidean: 1>)

Low-poly art style effect

noisemaker.effects.nebula(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.normal_map(tensor, shape, time=0.0, speed=1.0)

Generate a tangent-space normal map.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –

Returns:

Tensor

noisemaker.effects.normalize(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.offset_index(y_index, height, x_index, width)

Offset X and Y displacement channels from each other, to help with diagonal banding.

Returns a combined Tensor with shape [height, width, 2]

Parameters:

• y_index (Tensor) – Tensor with shape [height, width, 1], containing Y indices
• height (int) –
• x_index (Tensor) – Tensor with shape [height, width, 1], containing X indices
• width (int) –

Returns:

Tensor

noisemaker.effects.on_screen_display(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.outline(tensor, shape, sobel_metric=1, invert=False, time=0.0, speed=1.0)

Superimpose sobel operator results (cartoon edges)

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• sobel_metric (DistanceMetric|int) – Sobel distance metric

noisemaker.effects.palette(tensor, shape, name=None, alpha=1.0, time=0.0, speed=1.0)

Another approach to image coloration https://iquilezles.org/www/articles/palettes/palettes.htm

noisemaker.effects.pixel_sort(tensor, shape, angled=False, darkest=False, time=0.0, speed=1.0)

Pixel sort effect

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• angled (bool) – If True, sort along a random angle.
• darkest (bool) – If True, order by darkest instead of brightest

Return Tensor:

noisemaker.effects.post_process(tensor, shape, freq, ridges_hint=False, spline_order=<InterpolationType.bicubic: 3>, reflect_range=0.0, refract_range=0.0, reindex_range=0.0, refract_y_from_offset=False, refract_signed_range=False, clut=None, clut_horizontal=False, clut_range=0.5, with_worms=None, worms_density=4.0, worms_duration=4.0, worms_stride=1.0, worms_stride_deviation=0.05, worms_alpha=0.5, worms_kink=1.0, worms_drunkenness=0.0, with_sobel=None, with_normal_map=False, deriv=None, deriv_alpha=1.0, with_outline=False, with_glowing_edges=False, with_wormhole=False, wormhole_kink=2.5, wormhole_stride=0.1, wormhole_alpha=1.0, with_voronoi=0, voronoi_nth=0, voronoi_metric=<DistanceMetric.euclidean: 1>, voronoi_alpha=1.0, voronoi_refract=0.0, voronoi_inverse=False, voronoi_sdf_sides=5, voronoi_refract_y_from_offset=True, posterize_levels=0, with_erosion_worms=False, erosion_worms_density=50, erosion_worms_iterations=50, erosion_worms_contraction=1.0, erosion_worms_alpha=1.0, erosion_worms_inverse=False, erosion_worms_xy_blend=None, warp_range=0.0, warp_octaves=3, warp_interp=None, warp_freq=None, warp_map=None, warp_signed_range=True, ripple_range=0.0, ripple_freq=None, ripple_kink=1.0, vortex_range=0.0, with_aberration=None, with_dla=0.0, dla_padding=2, point_freq=5, point_distrib=1000000, point_corners=False, point_generations=1, point_drift=0.0, with_bloom=None, with_reverb=None, reverb_iterations=1, reverb_ridges=True, with_light_leak=None, with_vignette=None, vignette_brightness=0.0, with_vaseline=0.0, post_hue_rotation=None, post_saturation=None, post_brightness=None, post_contrast=None, with_ridge=False, with_jpeg_decimate=None, with_conv_feedback=None, conv_feedback_alpha=0.5, with_density_map=False, with_glyph_map=None, glyph_map_colorize=True, glyph_map_zoom=1.0, glyph_map_alpha=1.0, with_composite=None, composite_zoom=4.0, with_sort=False, sort_angled=False, sort_darkest=False, with_convolve=None, with_shadow=None, with_sketch=False, with_lowpoly=False, lowpoly_distrib=1000000, lowpoly_freq=10, lowpoly_metric=<DistanceMetric.euclidean: 1>, angle=None, with_simple_frame=False, with_kaleido=None, kaleido_sdf_sides=0, kaleido_blend_edges=True, with_wobble=None, with_palette=None, with_glitch=False, with_vhs=False, with_crt=False, with_scan_error=False, with_snow=False, with_grain=False, with_nebula=False, with_false_color=False, with_frame=False, with_scratches=False, with_fibers=False, with_stray_hair=False, with_grime=False, with_watermark=False, with_ticker=False, with_texture=False, with_pre_spatter=False, with_spatter=False, with_clouds=False, with_lens_warp=None, with_tint=None, with_degauss=False, color_space=<ColorSpace.hsv: 21>, time=0.0, speed=1.0, **_)

Apply post-processing effects.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• freq (list[int]) –
• spline_order (int) – Ortho spline point count (0=Constant, 1=Linear, 2=Cosine, 3=Bicubic)
• reflect_range (float) – Derivative distortion gradient.
• refract_range (float) – Self-distortion gradient.
• refract_y_from_offset (float) – Derive Y offset values from offsetting the image.
• reindex_range (float) – Self-reindexing gradient.
• clut (str) – PNG or JPG color lookup table filename.
• clut_horizontal (float) – Preserve clut Y axis.
• clut_range (float) – Gather range for clut.
• with_worms (WormBehavior|None) – Do worms.
• worms_density (float) – Worm density multiplier (larger == slower)
• worms_duration (float) – Iteration multiplier (larger == slower)
• worms_stride (float) – Mean travel distance per iteration
• worms_stride_deviation (float) – Per-worm travel distance deviation
• worms_alpha (float) – Fade worms (0..1)
• worms_kink (float) – Worm twistiness
• sobel (DistanceMetric|int) – Sobel operator distance metric
• outline (DistanceMetric|int) – Outlines distance metric (multiply)
• with_normal_map (bool) – Create a tangent-space normal map
• with_wormhole (bool) – Wormhole effect. What is this?
• wormhole_kink (float) – Wormhole kinkiness, if you’re into that.
• wormhole_stride (float) – Wormhole thickness range
• wormhole_alpha (float) – Wormhole alpha blending
• with_voronoi (VoronoiDiagramType|int) – Voronoi diagram type (0=Off, 1=Range, 2=Color Range, 3=Indexed, 4=Color Map, 5=Blended, 6=Flow)
• voronoi_nth (int) – Voronoi Nth nearest
• voronoi_metric (DistanceMetric|int) – Voronoi distance metric
• voronoi_alpha (float) – Blend with original tensor (0.0 = Original, 1.0 = Voronoi)
• voronoi_refract (float) – Domain warp input tensor against Voronoi
• voronoi_refract_y_from_offset (bool) – Derive Y offsets from offsetting image
• voronoi_inverse (bool) – Inverse values for Voronoi ‘range’ types
• voronoi_sdf_sides (bool) – Number of sides for Voronoi when using DistanceMetric.sdf
• ridges_hint (bool) – Ridged multifractal hint for Voronoi
• deriv (DistanceMetric|int) – Derivative distance metric
• deriv_alpha (float) – Derivative alpha blending amount
• posterize_levels (float) – Posterize levels
• with_erosion_worms (bool) – Erosion worms
• erosion_worms_density (float) – Default: 50
• erosion_worms_iterations (float) – Default: 50
• erosion_worms_contraction (float) – Inverse of stride. Default: 1.0, smaller=longer steps
• erosion_worms_alpha (float) –
• erosion_worms_inverse (bool) –
• erosion_worms_xy_blend (None|float) –
• vortex_range (float) – Vortex tiling amount
• warp_range (float) – Orthogonal distortion gradient.
• warp_octaves (int) – Multi-res iteration count for warp
• warp_interp (int|None) – Override spline order for warp (None = use spline_order)
• warp_freq (int|None) – Override frequency for warp (None = use freq)
• warp_map (str|None) – File with brightness values for warp (None = generate noise)
• ripple_range (float) – Ripple range
• ripple_freq (float) – Ripple frequency
• ripple_kink (float) – Ripple twistiness
• with_aberration (float|None) – Chromatic aberration distance
• with_bloom (float|None) – Bloom alpha
• with_dla (bool) – Diffusion-limited aggregation alpha
• dla_padding (int) – DLA pixel padding
• point_freq (int) – Voronoi and DLA point frequency (freq * freq = count)
• point_distrib (PointDistribution|ValueMask|int) – Voronoi and DLA point cloud distribution
• point_corners (bool) – Pin Voronoi and DLA points to corners (False = pin to center)
• point_generations (int) – Penrose-ish generations. Keep it low, and keep freq low, or you will run OOM easily.
• point_drift (float) – Fudge point locations (1.0 = nearest neighbor)
• with_reverb (None|int) – Reverb octave count
• reverb_iterations (int) – Re-reverberation N times
• reverb_ridges (bool) – Ridged reverb layers (False to disable)
• with_light_leak (None|float) – Light leak effect alpha
• with_vignette (None|float) – Vignette effect alpha
• vignette_brightness (None|float) – Vignette effect brightness
• post_hue_rotation (None|float) – Rotate hue (-.5 - .5)
• post_saturation (None|float) – Adjust saturation (0 - 1)
• post_brightness (None|float) – Adjust brightness
• post_contrast (None|float) – Adjust contrast
• with_ridge (bool) – Crease at midpoint values
• with_shadow (None|float) – Sobel-based shading alpha
• with_jpeg_decimate (None|int) – Conv2D feedback + JPEG encode/decode iteration count
• with_conv_feedback (None|int) – Conv2D feedback iterations
• conv_feedback_alpha (float) – Conv2D feedback alpha
• with_density_map (bool) – Map values to color histogram
• with_glyph_map (ValueMask|None) – Map values to glyph brightness. Square masks only for now
• glyph_map_colorize (bool) – Colorize glyphs from on average input colors
• glyph_map_zoom (float) – Scale glyph output
• glyph_map_alpha (float) – Fade glyph output
• with_composite (None|ValueMask) – Composite video effect
• composite_zoom (float) – Composite subpixel scaling
• with_sort (bool) – Pixel sort
• sort_angled (bool) – Pixel sort along a random angle
• sort_darkest (bool) – Pixel sort order by darkest instead of brightest
• convolve (None|list[str|ValueMask]) – List of ValueMasks to apply as convolution kernels
• with_sketch (bool) – Pencil sketch effect
• with_lowpoly (bool) – Low-poly art effect
• lowpoly_distrib (PointDistribution) – Point distribution for low-poly art effect
• lowpoly_freq (int) – Point frequency for low-poly art effect
• lowpoly_metric (DistanceMetric) – Low-poly effect distance metric
• angle (None|float) – Rotation angle
• with_simple_frame (None|bool) –
• with_kaleido (None|int) – Number of kaleido reflection sides
• kaleido_sdf_sides (None|DistanceMetric) – Number of kaleido shape sides
• kaleido_blend_edges (bool) – Blend Kaleido with original edge indices
• with_wobble (None|float) – Move entire image around
• with_palette (None|str) – Apply named cosine palette
• with_glitch (bool) – Glitch effect (Bit shit)
• with_vhs (bool) – VHS effect (Shitty tracking)
• with_crt (bool) – Vintage TV effect
• with_scan_error (bool) – Horizontal scan error
• with_snow (float) – Analog broadcast snow
• with_grain (float) – Per-pixel brightness jitter
• with_frame (bool) – Shitty instant camera effect
• with_nebula (bool) – Add clouds
• with_false_color (bool) – Swap colors with basic noise
• with_watermark (bool) – Stylized digital watermark effect
• with_ticker (bool) – With spooky ticker effect
• with_scratches (bool) – Scratched film effect
• with_fibers (bool) – Old-timey paper fibers
• with_texture (bool) – Bumpy canvas
• with_pre_spatter (bool) – Spatter mask (early pass)
• with_spatter (bool) – Spatter mask
• with_clouds (bool) – Cloud cover
• with_lens_warp (None|float) – Lens warp effect
• with_tint (None|float) – Color tint effect alpha amount
• with_degauss (None|float) – CRT degauss effect
• color_space (ColorSpace) –

Returns:

Tensor

noisemaker.effects.posterize(tensor, shape, levels=9, time=0.0, speed=1.0)

Reduce the number of color levels per channel.

Parameters:

• tensor (Tensor) –
• levels (int) –

Returns:

Tensor

noisemaker.effects.reindex(tensor, shape, displacement=0.5, time=0.0, speed=1.0)

Re-color the given tensor, by sampling along one axis at a specified frequency.

Parameters:

• tensor (Tensor) – An image tensor.
• shape (list[int]) –
• displacement (float) –

Returns:

Tensor

noisemaker.effects.reverb(tensor, shape, octaves=2, iterations=1, ridges=True, time=0.0, speed=1.0)

Multi-octave “reverberation” of input image tensor

Parameters:

• tensor (Tensor) –
• shape (float[int]) –
• octaves (int) –
• iterations (int) – Re-reverberate N times. Gratuitous!
• ridges (bool) – abs(tensor * 2 - 1) – False to not do that.

noisemaker.effects.ridge(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.ripple(tensor, shape, freq=2, displacement=1.0, kink=1.0, reference=None, spline_order=<InterpolationType.bicubic: 3>, time=0.0, speed=1.0)

Apply displacement from pixel radian values.

Parameters:

• tensor (Tensor) – An image tensor.
• shape (list[int]) –
• freq (list[int]) – Displacement frequency
• displacement (float) –
• kink (float) –
• reference (Tensor) – An optional displacement map.
• spline_order (int) – Ortho offset spline point count. 0=Constant, 1=Linear, 2=Cosine, 3=Bicubic

Returns:

Tensor

noisemaker.effects.rotate(tensor, shape, angle=None, time=0.0, speed=1.0)

Rotate the image. This breaks seamless edges.

noisemaker.effects.rotate2D(tensor, shape, angle)

noisemaker.effects.scanline_error(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.scratches(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.shadow(tensor, shape, alpha=1.0, reference=None, time=0.0, speed=1.0)

Convolution-based self-shadowing effect.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• alpha (float) –
• reference (None|Tensor) – Alternate reference values with shape (height, width)

noisemaker.effects.simple_frame(tensor, shape, brightness=0.0, time=0.0, speed=1.0)

noisemaker.effects.sine(tensor, shape, amount=1.0, time=0.0, speed=1.0, rgb=False)

noisemaker.effects.sketch(tensor, shape, time=0.0, speed=1.0)

Pencil sketch effect

Parameters:

• tensor (Tensor) –
• shape (list[int]) –

Return Tensor:

noisemaker.effects.snow(tensor, shape, alpha=0.25, time=0.0, speed=1.0)

noisemaker.effects.sobel(tensor, shape, dist_metric=1, rgb=False)

Colorized sobel edges.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• dist_metric (DistanceMetric|int) – Sobel distance metric
• rgb (bool) –

noisemaker.effects.sobel_operator(tensor, shape, dist_metric=<DistanceMetric.euclidean: 1>, time=0.0, speed=1.0)

Apply a sobel operator.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• dist_metric (DistanceMetric|int) – Sobel distance metric

Returns:

Tensor

noisemaker.effects.spatter(tensor, shape, color=True, time=0.0, speed=1.0)

noisemaker.effects.spooky_ticker(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.square_crop_and_resize(tensor, shape, length=1024)

Crop and resize an image Tensor into a square with desired side length.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• length (int) – Desired side length

Return Tensor:

noisemaker.effects.stray_hair(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.texture(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.tint(tensor, shape, time=0.0, speed=1.0, alpha=0.5)

noisemaker.effects.value_refract(tensor, shape, freq=4, distrib=<ValueDistribution.center_circle: 20>, displacement=0.125, time=0.0, speed=1.0)

noisemaker.effects.vaseline(tensor, shape, alpha=1.0, time=0.0, speed=1.0)

noisemaker.effects.vhs(tensor, shape, time=0.0, speed=1.0)

Apply a bad VHS tracking effect.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –

Returns:

Tensor

noisemaker.effects.vignette(tensor, shape, brightness=0.0, alpha=1.0, time=0.0, speed=1.0)

noisemaker.effects.vortex(tensor, shape, displacement=64.0, time=0.0, speed=1.0)

Vortex tiling effect

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• displacement (float) –

noisemaker.effects.warp(tensor, shape, freq=2, octaves=5, displacement=1, spline_order=<InterpolationType.bicubic: 3>, warp_map=None, signed_range=True, time=0.0, speed=1.0)

Multi-octave warp effect

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• freq (list[int]) –
• octaves (int) –
• displacement (float) –
• spline_order (int) –
• warp_map (str|None) –
• signed_range (bool) –
• time (float) –
• speed (float) –

noisemaker.effects.watermark(tensor, shape, time=0.0, speed=1.0)

noisemaker.effects.wobble(tensor, shape, time=0.0, speed=1.0)

Move the entire image around

noisemaker.effects.wormhole(tensor, shape, kink=1.0, input_stride=1.0, alpha=1.0, time=0.0, speed=1.0)

Apply per-pixel field flow. Non-iterative.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• kink (float) – Path twistiness
• input_stride (float) – Maximum pixel offset

Returns:

Tensor

noisemaker.effects.worms(tensor, shape, behavior=1, density=4.0, duration=4.0, stride=1.0, stride_deviation=0.05, alpha=0.5, kink=1.0, drunkenness=0.0, quantize=False, colors=None, time=0.0, speed=1.0)

Make a furry patch of worms which follow field flow rules.

Parameters:

• tensor (Tensor) –
• shape (list[int]) –
• behavior (int|WormBehavior) –
• density (float) – Worm density multiplier (larger == slower)
• duration (float) – Iteration multiplier (larger == slower)
• stride (float) – Mean travel distance per iteration
• stride_deviation (float) – Per-worm travel distance deviation
• alpha (float) – Fade worms (0..1)
• kink (float) – Make your worms twist.
• drunkenness (float) – Randomly fudge angle at each step (1.0 = 360 degrees)
• quantize (bool) – Quantize rotations to 45 degree increments
• colors (Tensor) – Optional starting colors, if not from tensor.

Returns:

Tensor

noisemaker.composer

Extremely high-level interface for composable noise presets. See detailed docs.

noisemaker.composer.Effect(effect_name, **kwargs)

Return a partial effects function. Invoke the wrapped function with params “tensor”, “shape”, “time”, and “speed.”

class noisemaker.composer.Preset(preset_name, presets, settings=None)

Bases: object

is_effect()

is_generator()

render(seed, tensor=None, shape=[1024, 1024, 3], time=0.0, speed=1.0, filename='art.png', with_alpha=False, with_supersample=False, with_ai=False, with_upscale=False, stability_model=None)

Render the preset to an image file.

class noisemaker.composer.SettingsDict(*args, **kwargs)

Bases: collections.UserDict

dict, but it makes sure the caller eats everything on their plate.

raise_if_unaccessed(unused_okay=None)

was_accessed(key)

exception noisemaker.composer.UnusedKeys

Bases: Exception

Exception raised when a preset has keys that aren’t being used.

noisemaker.composer.coin_flip()

noisemaker.composer.enum_range(a, b)

Return a list of enum members within the specified inclusive numeric value range.

noisemaker.composer.random_member(*collections)

Return a random member from a collection, enum list, or enum. Ensures deterministic ordering.

noisemaker.composer.reload_presets(presets)

Re-evaluate presets after changing the interpreter’s random seed.

noisemaker.composer.stash(key, value=None)

Hold on to a variable for reference within the same lambda. Returns the stashed value.

noisemaker.constants

Constants used in Noisemaker

class noisemaker.constants.ColorSpace

Bases: enum.Enum

color_members = <bound method ColorSpace.color_members of <enum 'ColorSpace'>>

grayscale = 1

hsv = 21

is_color = <bound method ColorSpace.is_color of <enum 'ColorSpace'>>

oklab = 31

rgb = 11

class noisemaker.constants.DistanceMetric

Bases: enum.Enum

Specify the distance metric used in various operations, such as Voronoi cells, derivatives, and sobel operators.

absolute_members = <bound method DistanceMetric.absolute_members of <enum 'DistanceMetric'>>

all = <bound method DistanceMetric.all of <enum 'DistanceMetric'>>

chebyshev = 3

euclidean = 1

hexagram = 102

is_absolute = <bound method DistanceMetric.is_absolute of <enum 'DistanceMetric'>>

is_signed = <bound method DistanceMetric.is_signed of <enum 'DistanceMetric'>>

manhattan = 2

none = 0

octagram = 4

sdf = 201

signed_members = <bound method DistanceMetric.signed_members of <enum 'DistanceMetric'>>

triangular = 101

class noisemaker.constants.InterpolationType

Bases: enum.Enum

Specify the spline point count for interpolation operations.

bicubic = 3

constant = 0

cosine = 2

linear = 1

class noisemaker.constants.OctaveBlending

Bases: enum.Enum

Specify the mode for flattening octaves.

alpha = 20

falloff = 0

reduce_max = 10

class noisemaker.constants.PointDistribution

Bases: enum.Enum

Point cloud distribution, used by Voronoi and DLA

chess = 1000003

circular = 1000100

circular_members = <bound method PointDistribution.circular_members of <enum 'PointDistribution'>>

concentric = 1000101

grid_members = <bound method PointDistribution.grid_members of <enum 'PointDistribution'>>

h_hex = 1000010

is_circular = <bound method PointDistribution.is_circular of <enum 'PointDistribution'>>

is_grid = <bound method PointDistribution.is_grid of <enum 'PointDistribution'>>

random = 1000000

rotating = 1000102

spiral = 1000050

square = 1000001

v_hex = 1000011

waffle = 1000002

class noisemaker.constants.ValueDistribution

Bases: enum.Enum

Specify the value distribution function for basic noise.

image = basic(freq, [height, width, channels], distrib=ValueDistribution.uniform)

center_circle = 20

center_decagon = 30

center_diamond = 21

center_dodecagon = 32

center_hendecagon = 31

center_heptagon = 27

center_hexagon = 26

center_nonagon = 29

center_octagon = 28

center_pentagon = 25

center_square = 24

center_triangle = 23

column_index = 10

exp = 2

is_center_distance = <bound method ValueDistribution.is_center_distance of <enum 'ValueDistribution'>>

is_native_size = <bound method ValueDistribution.is_native_size of <enum 'ValueDistribution'>>

is_noise = <bound method ValueDistribution.is_noise of <enum 'ValueDistribution'>>

is_scan = <bound method ValueDistribution.is_scan of <enum 'ValueDistribution'>>

mids = 6

ones = 5

row_index = 11

scan_down = 41

scan_left = 42

scan_right = 43

scan_up = 40

uniform = 1

zeros = 7

class noisemaker.constants.ValueMask

Bases: enum.Enum

ace = 911

alphanum_0 = 20

alphanum_1 = 21

alphanum_2 = 22

alphanum_3 = 23

alphanum_4 = 24

alphanum_5 = 25

alphanum_6 = 26

alphanum_7 = 27

alphanum_8 = 28

alphanum_9 = 29

alphanum_a = 30

alphanum_b = 31

alphanum_binary = 1013

alphanum_c = 32

alphanum_d = 33

alphanum_e = 34

alphanum_f = 35

alphanum_hex = 1015

alphanum_numeric = 1014

arecibo = 1034

arecibo_bignum = 1031

arecibo_dna = 1033

arecibo_nucleotide = 1032

arecibo_num = 1030

bank_ocr = 1070

bank_ocr_0 = 250

bank_ocr_1 = 251

bank_ocr_2 = 252

bank_ocr_3 = 253

bank_ocr_4 = 254

bank_ocr_5 = 255

bank_ocr_6 = 256

bank_ocr_7 = 257

bank_ocr_8 = 258

bank_ocr_9 = 259

bar_code = 1060

bar_code_short = 1061

chess = 3

conv2d_blur = 800

conv2d_box_blur = 810

conv2d_deriv_x = 801

conv2d_deriv_y = 802

conv2d_edges = 803

conv2d_emboss = 804

conv2d_invert = 805

conv2d_members = <bound method ValueMask.conv2d_members of <enum 'ValueMask'>>

conv2d_rand = 806

conv2d_sharpen = 807

conv2d_sobel_x = 808

conv2d_sobel_y = 809

dropout = 1100

emoji = 1051

emoji_00 = 200

emoji_01 = 201

emoji_02 = 202

emoji_03 = 203

emoji_04 = 204

emoji_05 = 205

emoji_06 = 206

emoji_07 = 207

emoji_08 = 208

emoji_09 = 209

emoji_10 = 210

emoji_11 = 211

emoji_12 = 212

emoji_13 = 213

emoji_14 = 214

emoji_15 = 215

emoji_16 = 216

emoji_17 = 217

emoji_18 = 218

emoji_19 = 219

emoji_20 = 220

emoji_21 = 221

emoji_22 = 222

emoji_23 = 223

emoji_24 = 224

emoji_25 = 225

emoji_26 = 226

emoji_27 = 227

fake_qr = 1080

fat_lcd = 1024

fat_lcd_0 = 70

fat_lcd_1 = 71

fat_lcd_2 = 72

fat_lcd_3 = 73

fat_lcd_4 = 74

fat_lcd_5 = 75

fat_lcd_6 = 76

fat_lcd_7 = 77

fat_lcd_8 = 78

fat_lcd_9 = 79

fat_lcd_a = 80

fat_lcd_b = 81

fat_lcd_binary = 1025

fat_lcd_c = 82

fat_lcd_d = 83

fat_lcd_e = 84

fat_lcd_f = 85

fat_lcd_g = 86

fat_lcd_h = 87

fat_lcd_hex = 1027

fat_lcd_i = 88

fat_lcd_j = 89

fat_lcd_k = 90

fat_lcd_l = 91

fat_lcd_m = 92

fat_lcd_n = 93

fat_lcd_numeric = 1026

fat_lcd_o = 94

fat_lcd_p = 95

fat_lcd_q = 96

fat_lcd_r = 97

fat_lcd_s = 98

fat_lcd_t = 99

fat_lcd_u = 100

fat_lcd_v = 101

fat_lcd_w = 102

fat_lcd_x = 103

fat_lcd_y = 104

fat_lcd_z = 105

glyph_members = <bound method ValueMask.glyph_members of <enum 'ValueMask'>>

grid = 4

grid_members = <bound method ValueMask.grid_members of <enum 'ValueMask'>>

h_bar = 5

h_hex = 10

h_tri = 12

halftone = 1021

halftone_0 = 50

halftone_1 = 51

halftone_2 = 52

halftone_3 = 53

halftone_4 = 54

halftone_5 = 55

halftone_6 = 56

halftone_7 = 57

halftone_8 = 58

halftone_9 = 59

iching = 1009

ideogram = 1008

invaders = 1003

invaders_large = 1004

invaders_square = 1005

is_conv2d = <bound method ValueMask.is_conv2d of <enum 'ValueMask'>>

is_glyph = <bound method ValueMask.is_glyph of <enum 'ValueMask'>>

is_grid = <bound method ValueMask.is_grid of <enum 'ValueMask'>>

is_procedural = <bound method ValueMask.is_procedural of <enum 'ValueMask'>>

is_rgb = <bound method ValueMask.is_rgb of <enum 'ValueMask'>>

lcd = 1022

lcd_0 = 60

lcd_1 = 61

lcd_2 = 62

lcd_3 = 63

lcd_4 = 64

lcd_5 = 65

lcd_6 = 66

lcd_7 = 67

lcd_8 = 68

lcd_9 = 69

lcd_binary = 1023

letters = 1007

matrix = 1006

mcpaint = 1050

mcpaint_00 = 130

mcpaint_01 = 131

mcpaint_02 = 132

mcpaint_03 = 133

mcpaint_04 = 134

mcpaint_05 = 135

mcpaint_06 = 136

mcpaint_07 = 137

mcpaint_08 = 138

mcpaint_09 = 139

mcpaint_10 = 140

mcpaint_11 = 141

mcpaint_12 = 142

mcpaint_13 = 143

mcpaint_14 = 144

mcpaint_15 = 145

mcpaint_16 = 146

mcpaint_17 = 147

mcpaint_18 = 148

mcpaint_19 = 149

mcpaint_20 = 150

mcpaint_21 = 151

mcpaint_22 = 152

mcpaint_23 = 153

mcpaint_24 = 154

mcpaint_25 = 155

mcpaint_26 = 156

mcpaint_27 = 157

mcpaint_28 = 158

mcpaint_29 = 159

mcpaint_30 = 160

mcpaint_31 = 161

mcpaint_32 = 162

mcpaint_33 = 163

mcpaint_34 = 164

mcpaint_35 = 165

mcpaint_36 = 166

mcpaint_37 = 167

mcpaint_38 = 168

mcpaint_39 = 169

mcpaint_40 = 170

nb = 912

nonprocedural_members = <bound method ValueMask.nonprocedural_members of <enum 'ValueMask'>>

procedural_members = <bound method ValueMask.procedural_members of <enum 'ValueMask'>>

rainbow = 910

rbggbr = 901

rgb = 900

rgb_members = <bound method ValueMask.rgb_members of <enum 'ValueMask'>>

rgbgr = 903

rggb = 902

roygbiv = 904

script = 1010

sparse = 1000

sparser = 1001

sparsest = 1002

square = 1

trans = 913

tromino = 1012

tromino_i = 40

tromino_l = 41

tromino_o = 42

tromino_s = 43

truchet_curves = 1041

truchet_curves_00 = 112

truchet_curves_01 = 113

truchet_lines = 1040

truchet_lines_00 = 110

truchet_lines_01 = 111

truchet_tile = 1042

truchet_tile_00 = 120

truchet_tile_01 = 121

truchet_tile_02 = 122

truchet_tile_03 = 123

truetype = 1020

v_bar = 6

v_hex = 11

v_tri = 13

waffle = 2

white_bear = 1011

class noisemaker.constants.VoronoiDiagramType

Bases: enum.Enum

Specify the artistic rendering function used for Voronoi diagrams.

color_flow = 42

Density-mapped flow diagram

color_range = 12

Normalized neighbor distances blended with input Tensor

color_regions = 22

Color-mapped regions

flow = 41

Edgeless voronoi. Natural logarithm of reduced distance sums.

flow_members = <bound method VoronoiDiagramType.flow_members of <enum 'VoronoiDiagramType'>>

is_flow_member = <bound method VoronoiDiagramType.is_flow_member of <enum 'VoronoiDiagramType'>>

none = 0

No Voronoi

range = 11

Normalized neighbor distances

range_regions = 31

Colorized neighbor distances blended with color-mapped regions

regions = 21

Indexed regions

class noisemaker.constants.WormBehavior

Bases: enum.Enum

Specify the type of heading bias for worms to follow.

image = worms(image, behavior=WormBehavior.unruly)

all = <bound method WormBehavior.all of <enum 'WormBehavior'>>

chaotic = 4

crosshatch = 2

meandering = 10

none = 0

obedient = 1

random = 5

unruly = 3

noisemaker.util

Utility functions for Noisemaker.

noisemaker.util.check_call(command, quiet=False)

noisemaker.util.dumps(kwargs)

noisemaker.util.get_noisemaker_dir()

noisemaker.util.load(filename, channels=None)

Load a .png or .jpg by filename.

Parameters:filename (str) – Returns:Tensor

noisemaker.util.log_subprocess_error(command, e)

noisemaker.util.magick(glob, name)

Shell out to ImageMagick’s “convert” (im6) or “magick” (im7) commands for GIF composition, depending on what’s available.

Parameters:

• glob (str) – Frame filename glob pattern
• name (str) – Filename

noisemaker.util.save(tensor, name='noise.png')

Save an image Tensor to a file.

Parameters:

• tensor (Tensor) – Image tensor
• name (str) – Filename, ending with .png or .jpg

Returns:

None

noisemaker.util.shape_from_file(filename)

Get image dimensions from a file, using PIL, to avoid adding to the TensorFlow graph.

noisemaker.util.shape_from_params(width, height, color_space, with_alpha)

noisemaker.util.watermark(text, filename)

Annotate an image.

Parameters:

• text –
• filename –

noisemaker.presets

noisemaker.presets.PRESETS()

Composable presets for Noisemaker. See composer.py and https://noisemaker.readthedocs.io/en/latest/composer.html