Unverified Commit 4067d6c4 authored by kaixuanliu's avatar kaixuanliu Committed by GitHub
Browse files

adjust criteria for marigold-intrinsics example on XPU (#12290)



adjust criteria for XPU
Signed-off-by: default avatarLiu, Kaixuan <kaixuan.liu@intel.com>
Co-authored-by: default avatarAryan <aryan@huggingface.co>
parent 28106fca
......@@ -34,6 +34,7 @@ from diffusers import (
)
from ...testing_utils import (
Expectations,
backend_empty_cache,
enable_full_determinism,
floats_tensor,
......@@ -416,7 +417,7 @@ class MarigoldIntrinsicsPipelineIntegrationTests(unittest.TestCase):
expected_slice: np.ndarray = None,
model_id: str = "prs-eth/marigold-iid-appearance-v1-1",
image_url: str = "https://marigoldmonodepth.github.io/images/einstein.jpg",
atol: float = 1e-4,
atol: float = 1e-3,
**pipe_kwargs,
):
from_pretrained_kwargs = {}
......@@ -531,11 +532,41 @@ class MarigoldIntrinsicsPipelineIntegrationTests(unittest.TestCase):
)
def test_marigold_intrinsics_einstein_f16_accelerator_G0_S1_P768_E3_B1_M1(self):
expected_slices = Expectations(
{
("xpu", 3): np.array(
[
0.62655,
0.62477,
0.62161,
0.62452,
0.62454,
0.62454,
0.62255,
0.62647,
0.63379,
]
),
("cuda", 7): np.array(
[
0.61572,
0.1377,
0.61182,
0.61426,
0.61377,
0.61426,
0.61279,
0.61572,
0.62354,
]
),
}
)
self._test_marigold_intrinsics(
is_fp16=True,
device=torch_device,
generator_seed=0,
expected_slice=np.array([0.61572, 0.61377, 0.61182, 0.61426, 0.61377, 0.61426, 0.61279, 0.61572, 0.62354]),
expected_slice=expected_slices.get_expectation(),
num_inference_steps=1,
processing_resolution=768,
ensemble_size=3,
......@@ -545,11 +576,41 @@ class MarigoldIntrinsicsPipelineIntegrationTests(unittest.TestCase):
)
def test_marigold_intrinsics_einstein_f16_accelerator_G0_S1_P768_E4_B2_M1(self):
expected_slices = Expectations(
{
("xpu", 3): np.array(
[
0.62988,
0.62792,
0.62548,
0.62841,
0.62792,
0.62792,
0.62646,
0.62939,
0.63721,
]
),
("cuda", 7): np.array(
[
0.61914,
0.6167,
0.61475,
0.61719,
0.61719,
0.61768,
0.61572,
0.61914,
0.62695,
]
),
}
)
self._test_marigold_intrinsics(
is_fp16=True,
device=torch_device,
generator_seed=0,
expected_slice=np.array([0.61914, 0.6167, 0.61475, 0.61719, 0.61719, 0.61768, 0.61572, 0.61914, 0.62695]),
expected_slice=expected_slices.get_expectation(),
num_inference_steps=1,
processing_resolution=768,
ensemble_size=4,
......
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