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enable several pipeline integration tests on XPU (#11526)

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* enable kandinsky2_2 integration test cases on XPU

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

* fix style

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

* enable latent_diffusion, dance_diffusion, musicldm, shap_e integration
uts on xpu

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>
Co-authored-by: Aryan <aryan@huggingface.co>
This commit is contained in:
Yao Matrix
2025-05-12 19:21:37 +08:00
committed by GitHub
parent e48f6aeeb4
commit c3726153fd
7 changed files with 57 additions and 32 deletions
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15
tests/pipelines/dance_diffusion/test_dance_diffusion.py
View File

@@ -20,7 +20,14 @@ import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNet1DModel
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, skip_mps, torch_device
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
nightly,
require_torch_accelerator,
skip_mps,
torch_device,
)
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
@@ -116,19 +123,19 @@ class DanceDiffusionPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@nightly
@require_torch_gpu
@require_torch_accelerator
class PipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def test_dance_diffusion(self):
device = torch_device

10
tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py
View File

@@ -28,13 +28,15 @@ from diffusers import (
VQModel,
)
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
nightly,
numpy_cosine_similarity_distance,
require_torch_gpu,
require_torch_accelerator,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin
@@ -226,19 +228,19 @@ class KandinskyV22ControlnetPipelineFastTests(PipelineTesterMixin, unittest.Test
@nightly
@require_torch_gpu
@require_torch_accelerator
class KandinskyV22ControlnetPipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def test_kandinsky_controlnet(self):
expected_image = load_numpy(

12
tests/pipelines/kandinsky2_2/test_kandinsky_controlnet_img2img.py
View File

@@ -29,13 +29,15 @@ from diffusers import (
VQModel,
)
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
floats_tensor,
load_image,
load_numpy,
nightly,
numpy_cosine_similarity_distance,
require_torch_gpu,
require_torch_accelerator,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin
@@ -233,19 +235,19 @@ class KandinskyV22ControlnetImg2ImgPipelineFastTests(PipelineTesterMixin, unitte
@nightly
@require_torch_gpu
@require_torch_accelerator
class KandinskyV22ControlnetImg2ImgPipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def test_kandinsky_controlnet_img2img(self):
expected_image = load_numpy(
@@ -309,4 +311,4 @@ class KandinskyV22ControlnetImg2ImgPipelineIntegrationTests(unittest.TestCase):
assert image.shape == (512, 512, 3)
max_diff = numpy_cosine_similarity_distance(expected_image.flatten(), image.flatten())
assert max_diff < 1e-4
assert max_diff < 5e-4

15
tests/pipelines/latent_diffusion/test_latent_diffusion.py
View File

@@ -22,10 +22,11 @@ from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNet2DConditionModel
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
load_numpy,
nightly,
require_torch_gpu,
require_torch_accelerator,
torch_device,
)
@@ -136,17 +137,17 @@ class LDMTextToImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@nightly
@require_torch_gpu
@require_torch_accelerator
class LDMTextToImagePipelineSlowTests(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, dtype=torch.float32, seed=0):
generator = torch.manual_seed(seed)
@@ -177,17 +178,17 @@ class LDMTextToImagePipelineSlowTests(unittest.TestCase):
@nightly
@require_torch_gpu
@require_torch_accelerator
class LDMTextToImagePipelineNightlyTests(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, dtype=torch.float32, seed=0):
generator = torch.manual_seed(seed)

14
tests/pipelines/musicldm/test_musicldm.py
View File

@@ -39,7 +39,13 @@ from diffusers import (
UNet2DConditionModel,
)
from diffusers.utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, nightly, require_torch_gpu, torch_device
from diffusers.utils.testing_utils import (
backend_empty_cache,
enable_full_determinism,
nightly,
require_torch_accelerator,
torch_device,
)
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
@@ -408,17 +414,17 @@ class MusicLDMPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@nightly
@require_torch_gpu
@require_torch_accelerator
class MusicLDMPipelineNightlyTests(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, generator_device="cpu", dtype=torch.float32, seed=0):
generator = torch.Generator(device=generator_device).manual_seed(seed)

14
tests/pipelines/shap_e/test_shap_e.py
View File

@@ -21,7 +21,13 @@ from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokeni
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils.testing_utils import load_numpy, nightly, require_torch_gpu, torch_device
from diffusers.utils.testing_utils import (
backend_empty_cache,
load_numpy,
nightly,
require_torch_accelerator,
torch_device,
)
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
@@ -222,19 +228,19 @@ class ShapEPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@nightly
@require_torch_gpu
@require_torch_accelerator
class ShapEPipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def test_shap_e(self):
expected_image = load_numpy(

9
tests/pipelines/shap_e/test_shap_e_img2img.py
View File

@@ -23,11 +23,12 @@ from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImg2ImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils.testing_utils import (
backend_empty_cache,
floats_tensor,
load_image,
load_numpy,
nightly,
require_torch_gpu,
require_torch_accelerator,
torch_device,
)
@@ -250,19 +251,19 @@ class ShapEImg2ImgPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
@nightly
@require_torch_gpu
@require_torch_accelerator
class ShapEImg2ImgPipelineIntegrationTests(unittest.TestCase):
def setUp(self):
# clean up the VRAM before each test
super().setUp()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def tearDown(self):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def test_shap_e_img2img(self):
input_image = load_image(