enable kandinsky2_2 integration test cases on XPU

Signed-off-by: Yao Matrix <[email protected]>

Author: yao-matrix

tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py

@@ -28,13 +28,16 @@
     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_accelerator,
     require_torch_gpu,
+    torch_device,
 )
 
 from ..test_pipelines_common import PipelineTesterMixin
@@ -226,19 +229,19 @@ def test_inference_batch_single_identical(self):
 
 
 @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(

tests/pipelines/kandinsky2_2/test_kandinsky_controlnet_img2img.py

@@ -29,13 +29,16 @@
     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_accelerator,
     require_torch_gpu,
+    torch_device,
 )
 
 from ..test_pipelines_common import PipelineTesterMixin
@@ -233,19 +236,19 @@ def test_float16_inference(self):
 
 
 @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 +312,4 @@ def test_kandinsky_controlnet_img2img(self):
         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