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enable unidiffuser test cases on xpu (#11444)

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* enable unidiffuser cases on XPU

Signed-off-by: Yao Matrix <matrix.yao@intel.com>

* fix a typo

Signed-off-by: Yao Matrix <matrix.yao@intel.com>

* fix style

Signed-off-by: Yao Matrix <matrix.yao@intel.com>

---------

Signed-off-by: Yao Matrix <matrix.yao@intel.com>
This commit is contained in:
Yao Matrix
2025-04-30 16:28:00 +08:00
committed by GitHub
parent fbe2fe5578
commit 35fada4169
2 changed files with 13 additions and 13 deletions
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10
tests/pipelines/test_pipelines_common.py
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@@ -1485,8 +1485,8 @@ class PipelineTesterMixin:
model_devices = [component.device.type for component in components.values() if hasattr(component, "device")]
self.assertTrue(all(device == torch_device for device in model_devices))
output_cuda = pipe(**self.get_dummy_inputs(torch_device))[0]
self.assertTrue(np.isnan(to_np(output_cuda)).sum() == 0)
output_device = pipe(**self.get_dummy_inputs(torch_device))[0]
self.assertTrue(np.isnan(to_np(output_device)).sum() == 0)
def test_to_dtype(self):
components = self.get_dummy_components()
@@ -1677,11 +1677,11 @@ class PipelineTesterMixin:
pipe.set_progress_bar_config(disable=None)
pipe.enable_model_cpu_offload(device=torch_device)
pipe.enable_model_cpu_offload()
inputs = self.get_dummy_inputs(generator_device)
output_with_offload = pipe(**inputs)[0]
pipe.enable_model_cpu_offload(device=torch_device)
pipe.enable_model_cpu_offload()
inputs = self.get_dummy_inputs(generator_device)
output_with_offload_twice = pipe(**inputs)[0]
@@ -2226,7 +2226,7 @@ class PipelineTesterMixin:
def enable_group_offload_on_component(pipe, group_offloading_kwargs):
# We intentionally don't test VAE's here. This is because some tests enable tiling on the VAE. If
# tiling is enabled and a forward pass is run, when cuda streams are used, the execution order of
# tiling is enabled and a forward pass is run, when accelerator streams are used, the execution order of
# the layers is not traced correctly. This causes errors. For apply group offloading to VAE, a
# warmup forward pass (even with dummy small inputs) is recommended.
for component_name in [

16
tests/pipelines/unidiffuser/test_unidiffuser.py
View File

@@ -22,13 +22,13 @@ from diffusers import (
UniDiffuserTextDecoder,
)
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
floats_tensor,
load_image,
nightly,
require_torch_2,
require_torch_accelerator,
require_torch_gpu,
run_test_in_subprocess,
torch_device,
)
@@ -577,24 +577,24 @@ class UniDiffuserPipelineFastTests(
assert text[0][: len(expected_text_prefix)] == expected_text_prefix
@unittest.skip(
"Test not supported becauseit has a bunch of direct configs at init and also, this pipeline isn't used that much now."
"Test not supported because it has a bunch of direct configs at init and also, this pipeline isn't used that much now."
)
def test_encode_prompt_works_in_isolation():
pass
@nightly
@require_torch_gpu
@require_torch_accelerator
class UniDiffuserPipelineSlowTests(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, device, seed=0, generate_latents=False):
generator = torch.manual_seed(seed)
@@ -705,17 +705,17 @@ class UniDiffuserPipelineSlowTests(unittest.TestCase):
@nightly
@require_torch_gpu
@require_torch_accelerator
class UniDiffuserPipelineNightlyTests(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, device, seed=0, generate_latents=False):
generator = torch.manual_seed(seed)