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102 行
3.4 KiB
Python
102 行
3.4 KiB
Python
from unittest.mock import MagicMock
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import pytest
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import torch
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@pytest.mark.parametrize(
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("noise_type", "width", "height", "expected_shape"),
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[
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("SD", 64, 64, (1, 4, 8, 8)),
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("FLUX", 64, 64, (1, 16, 8, 8)),
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("FLUX.2", 64, 64, (1, 32, 8, 8)),
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("SD3", 64, 64, (1, 16, 8, 8)),
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("CogView4", 64, 64, (1, 16, 8, 8)),
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("Z-Image", 64, 64, (1, 16, 8, 8)),
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("Anima", 64, 64, (1, 16, 1, 8, 8)),
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],
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)
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def test_noise_invocation_generates_expected_shapes(noise_type: str, width: int, height: int, expected_shape):
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from invokeai.app.invocations.noise import NoiseInvocation
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mock_context = MagicMock()
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mock_context.tensors.save.return_value = "noise-name"
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invocation = NoiseInvocation(noise_type=noise_type, width=width, height=height, seed=123)
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output = invocation.invoke(mock_context)
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saved_tensor = mock_context.tensors.save.call_args.kwargs["tensor"]
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assert saved_tensor.shape == expected_shape
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assert output.noise.seed == 123
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assert output.width == width
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assert output.height == height
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def test_noise_invocation_defaults_to_sd_shape():
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from invokeai.app.invocations.noise import NoiseInvocation
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mock_context = MagicMock()
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mock_context.tensors.save.return_value = "noise-name"
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invocation = NoiseInvocation(width=64, height=64, seed=1)
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invocation.invoke(mock_context)
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saved_tensor = mock_context.tensors.save.call_args.kwargs["tensor"]
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assert saved_tensor.shape == (1, 4, 8, 8)
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@pytest.mark.parametrize(
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("noise_type", "width", "height", "message"),
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[
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("SD", 66, 64, "multiple of 8"),
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("FLUX", 72, 64, "multiple of 16"),
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("FLUX.2", 64, 72, "multiple of 16"),
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("SD3", 72, 64, "multiple of 16"),
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("Z-Image", 64, 72, "multiple of 16"),
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("CogView4", 64, 80, "multiple of 32"),
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("Anima", 66, 64, "multiple of 8"),
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],
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)
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def test_noise_invocation_rejects_invalid_dimensions(noise_type: str, width: int, height: int, message: str):
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from invokeai.app.invocations.noise import NoiseInvocation
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mock_context = MagicMock()
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with pytest.raises(ValueError, match=message):
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invocation = NoiseInvocation(noise_type=noise_type, width=width, height=height, seed=0)
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invocation.invoke(mock_context)
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def test_noise_invocation_is_deterministic_for_identical_inputs():
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from invokeai.app.invocations.noise import NoiseInvocation
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mock_context = MagicMock()
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mock_context.tensors.save.side_effect = ["noise-1", "noise-2"]
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invocation = NoiseInvocation(noise_type="FLUX", width=64, height=64, seed=7)
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invocation.invoke(mock_context)
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first = mock_context.tensors.save.call_args_list[0].kwargs["tensor"]
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invocation.invoke(mock_context)
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second = mock_context.tensors.save.call_args_list[1].kwargs["tensor"]
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assert torch.equal(first, second)
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@pytest.mark.parametrize(("noise_type", "expected_shape"), [("FLUX", (1, 16, 8, 8)), ("FLUX.2", (1, 32, 8, 8))])
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def test_generate_noise_tensor_honors_use_cpu_false_for_flux_variants(noise_type: str, expected_shape):
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from invokeai.app.invocations.latent_noise import generate_noise_tensor
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noise = generate_noise_tensor(
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noise_type=noise_type,
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width=64,
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height=64,
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seed=0,
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device=torch.device("cpu"),
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dtype=torch.float32,
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use_cpu=False,
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)
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assert noise.shape == expected_shape
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