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77 lines
3.4 KiB
Python
77 lines
3.4 KiB
Python
import pytest
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import torch
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from invokeai.backend.flux.sampling_utils import clip_timestep_schedule, clip_timestep_schedule_fractional
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def float_lists_almost_equal(list1: list[float], list2: list[float], tol: float = 1e-6) -> bool:
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return all(abs(a - b) < tol for a, b in zip(list1, list2, strict=True))
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@pytest.mark.parametrize(
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["denoising_start", "denoising_end", "expected_timesteps", "raises"],
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[
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(0.0, 1.0, [1.0, 0.75, 0.5, 0.25, 0.0], False), # Default case.
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(-0.1, 1.0, [], True), # Negative denoising_start should raise.
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(0.0, 1.1, [], True), # denoising_end > 1 should raise.
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(0.5, 0.0, [], True), # denoising_start > denoising_end should raise.
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(0.0, 0.0, [1.0], False), # denoising_end == 0.
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(1.0, 1.0, [0.0], False), # denoising_start == 1.
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(0.2, 0.8, [1.0, 0.75, 0.5, 0.25], False), # Middle of the schedule.
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# If we denoise from 0.0 to x, then from x to 1.0, it is important that denoise_end = x and denoise_start = x
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# map to the same timestep. We test this first when x is equal to a timestep, then when it falls between two
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# timesteps.
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# x = 0.5
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(0.0, 0.5, [1.0, 0.75, 0.5], False),
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(0.5, 1.0, [0.5, 0.25, 0.0], False),
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# x = 0.3
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(0.0, 0.3, [1.0, 0.75], False),
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(0.3, 1.0, [0.75, 0.5, 0.25, 0.0], False),
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],
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)
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def test_clip_timestep_schedule(
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denoising_start: float, denoising_end: float, expected_timesteps: list[float], raises: bool
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):
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timesteps = torch.linspace(1, 0, 5).tolist()
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if raises:
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with pytest.raises(AssertionError):
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clip_timestep_schedule(timesteps, denoising_start, denoising_end)
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else:
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assert float_lists_almost_equal(
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clip_timestep_schedule(timesteps, denoising_start, denoising_end), expected_timesteps
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)
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@pytest.mark.parametrize(
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["denoising_start", "denoising_end", "expected_timesteps", "raises"],
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[
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(0.0, 1.0, [1.0, 0.75, 0.5, 0.25, 0.0], False), # Default case.
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(-0.1, 1.0, [], True), # Negative denoising_start should raise.
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(0.0, 1.1, [], True), # denoising_end > 1 should raise.
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(0.5, 0.0, [], True), # denoising_start > denoising_end should raise.
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(0.0, 0.0, [1.0], False), # denoising_end == 0.
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(1.0, 1.0, [0.0], False), # denoising_start == 1.
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(0.2, 0.8, [0.8, 0.75, 0.5, 0.25, 0.2], False), # Middle of the schedule.
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# If we denoise from 0.0 to x, then from x to 1.0, it is important that denoise_end = x and denoise_start = x
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# map to the same timestep. We test this first when x is equal to a timestep, then when it falls between two
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# timesteps.
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# x = 0.5
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(0.0, 0.5, [1.0, 0.75, 0.5], False),
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(0.5, 1.0, [0.5, 0.25, 0.0], False),
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# x = 0.3
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(0.0, 0.3, [1.0, 0.75, 0.7], False),
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(0.3, 1.0, [0.7, 0.5, 0.25, 0.0], False),
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],
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)
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def test_clip_timestep_schedule_fractional(
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denoising_start: float, denoising_end: float, expected_timesteps: list[float], raises: bool
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):
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timesteps = torch.linspace(1, 0, 5).tolist()
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if raises:
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with pytest.raises(AssertionError):
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clip_timestep_schedule_fractional(timesteps, denoising_start, denoising_end)
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else:
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assert float_lists_almost_equal(
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clip_timestep_schedule_fractional(timesteps, denoising_start, denoising_end), expected_timesteps
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)
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