Æmotion StudioandClaude Fable 5.1
f666d4e908
Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
80 lines
3.5 KiB
Python
80 lines
3.5 KiB
Python
"""
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The gaussian generator: white noise, the control.
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The properties worth pinning are the absences. It must have no spatial
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structure, none of the pattern knobs may reach it, and it must still keep the
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contract the other generators keep: a field per channel, channel 0 unchanged by
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how many were asked for, and one field evolving under temporal coherence.
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"""
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import pytest
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import torch
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from snk.core.constants import CHANNEL_BASIS
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from snk.core.shader_noise import effective_channel_rank, generate
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CPU = torch.device("cpu")
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PARAMS = {
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"scale": 1.0, "octaves": 3.0, "warp_strength": 0.5, "phase_shift": 0.5,
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"shape_type": "none", "color_scheme": "none", "time": 0.0, "base_seed": 8888,
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}
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def _neighbour_correlation(noise):
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field = noise[0]
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pairs = torch.stack([field[:, :-1, :].flatten(), field[:, 1:, :].flatten()])
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return torch.corrcoef(pairs)[0, 1].item()
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def test_the_field_is_white():
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noise = generate((1, 24, 22, 38), PARAMS, "gaussian", 8888, CPU)
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assert abs(_neighbour_correlation(noise)) < 0.05
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assert noise.abs().max() <= 1.0
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@pytest.mark.parametrize("knob", [dict(scale=0.3), dict(scale=3.0), dict(octaves=8.0),
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dict(warp_strength=5.0), dict(phase_shift=2.0),
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dict(color_scheme="viridis")])
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def test_no_pattern_knob_reaches_it(knob):
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"""The tooltip promises these do nothing, so a stray use of one fails here."""
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stock = generate((1, 4, 16, 16), PARAMS, "gaussian", 8888, CPU)
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assert torch.equal(generate((1, 4, 16, 16), dict(PARAMS, **knob), "gaussian", 8888, CPU), stock)
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def test_a_shape_mask_still_applies():
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stock = generate((1, 4, 16, 16), PARAMS, "gaussian", 8888, CPU)
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masked = generate((1, 4, 16, 16), dict(PARAMS, shape_type="radial"), "gaussian", 8888, CPU)
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assert not torch.equal(masked, stock)
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@pytest.mark.parametrize("shape", [(1, 4, 64, 64), (1, 24, 22, 38), (1, 24, 5, 16, 16),
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(1, 128, 3, 8, 8), (1, 128, 3, 32, 32)])
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def test_every_channel_is_its_own_field(shape):
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rank = effective_channel_rank(generate(shape, PARAMS, "gaussian", 8888, CPU))
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assert rank > min(shape[1], CHANNEL_BASIS) * 0.85
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def test_channel_zero_does_not_depend_on_how_many_channels_were_asked_for():
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wide = generate((1, 24, 5, 22, 38), PARAMS, "gaussian", 8888, CPU)
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single = generate((1, 1, 5, 22, 38), PARAMS, "gaussian", 8888, CPU)
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assert torch.equal(wide[:, :1], single)
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def test_holding_the_seed_and_advancing_time_turns_one_field_into_another():
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"""
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A 5-frame clip steps time by a quarter, so adjacent frames sit cos(pi/8)
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apart on the great circle: 0.92. Without coherence each frame is its own seed.
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"""
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def frame_correlation(coherent):
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noise = generate((1, 24, 5, 22, 38), dict(PARAMS, use_temporal_coherence=coherent),
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"gaussian", 8888, CPU, temporal_coherence=coherent)
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pairs = torch.stack([noise[0, :, 0].flatten(), noise[0, :, 1].flatten()])
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return torch.corrcoef(pairs)[0, 1].item()
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assert frame_correlation(True) > 0.8
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assert abs(frame_correlation(False)) < 0.1
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# Frame 0 is the first draw exactly, whatever time does to the frames after it.
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clip = generate((1, 4, 5, 16, 16), dict(PARAMS, use_temporal_coherence=True),
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"gaussian", 8888, CPU, temporal_coherence=True)
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still = generate((1, 4, 16, 16), dict(PARAMS, use_temporal_coherence=True), "gaussian", 8888, CPU)
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assert torch.equal(clip[:, :, 0], still)
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