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enable consistency test cases on XPU, all passed (#11446)

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Signed-off-by: Yao Matrix <matrix.yao@intel.com>
This commit is contained in:
Yao Matrix
2025-04-30 15:11:29 +08:00
committed by GitHub
parent c86511586f
commit fbe2fe5578
1 changed files with 15 additions and 5 deletions
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20
tests/pipelines/consistency_models/test_consistency_models.py
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@@ -11,10 +11,12 @@ from diffusers import (
UNet2DModel,
)
from diffusers.utils.testing_utils import (
Expectations,
backend_empty_cache,
enable_full_determinism,
nightly,
require_torch_2,
require_torch_gpu,
require_torch_accelerator,
torch_device,
)
from diffusers.utils.torch_utils import randn_tensor
@@ -168,17 +170,17 @@ class ConsistencyModelPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@nightly
@require_torch_gpu
@require_torch_accelerator
class ConsistencyModelPipelineSlowTests(unittest.TestCase):
def setUp(self):
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def get_inputs(self, seed=0, get_fixed_latents=False, device="cpu", dtype=torch.float32, shape=(1, 3, 64, 64)):
generator = torch.manual_seed(seed)
@@ -264,11 +266,19 @@ class ConsistencyModelPipelineSlowTests(unittest.TestCase):
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=True, enable_math=False, enable_mem_efficient=False):
image = pipe(**inputs).images
assert image.shape == (1, 64, 64, 3)
image_slice = image[0, -3:, -3:, -1]
expected_slice = np.array([0.1845, 0.1371, 0.1211, 0.2035, 0.1954, 0.1323, 0.1773, 0.1593, 0.1314])
expected_slices = Expectations(
{
("xpu", 3): np.array([0.0816, 0.0518, 0.0445, 0.0594, 0.0739, 0.0534, 0.0805, 0.0457, 0.0765]),
("cuda", 7): np.array([0.1845, 0.1371, 0.1211, 0.2035, 0.1954, 0.1323, 0.1773, 0.1593, 0.1314]),
("cuda", 8): np.array([0.0816, 0.0518, 0.0445, 0.0594, 0.0739, 0.0534, 0.0805, 0.0457, 0.0765]),
}
)
expected_slice = expected_slices.get_expectation()
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-3