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enable consistency test cases on XPU, all passed #11446

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Merged
merged 4 commits into from
Apr 30, 2025
Merged

enable consistency test cases on XPU, all passed #11446

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f0dd735
enable consistency test cases on XPU, all passed
yao-matrix Apr 29, 2025
225d347
Merge branch 'main' into consistency-xpu
yao-matrix Apr 29, 2025
a52b23c
Merge branch 'main' into consistency-xpu
yao-matrix Apr 30, 2025
891b7a5
Merge branch 'main' into consistency-xpu
yao-matrix Apr 30, 2025
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20 changes: 15 additions & 5 deletions tests/pipelines/consistency_models/test_consistency_models.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,10 +11,12 @@
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
Expand Down Expand Up @@ -168,17 +170,17 @@ def test_consistency_model_pipeline_onestep_class_cond(self):


@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)
Expand Down Expand Up @@ -264,11 +266,19 @@ def test_consistency_model_cd_multistep_flash_attn(self):
# 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

Expand Down
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