enable bria integration test on xpu, passed (#12214)

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

tests/pipelines/bria/test_pipeline_bria.py

@@ -28,10 +28,10 @@ from diffusers import (
 )
 from diffusers.pipelines.bria import BriaPipeline
 from diffusers.utils.testing_utils import (
+    backend_empty_cache,
     enable_full_determinism,
     numpy_cosine_similarity_distance,
-    require_accelerator,
-    require_torch_gpu,
+    require_torch_accelerator,
     slow,
     torch_device,
 )
@@ -149,7 +149,7 @@ class BriaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
             assert (output_height, output_width) == (expected_height, expected_width)
 
     @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU")
-    @require_accelerator
+    @require_torch_accelerator
     def test_save_load_float16(self, expected_max_diff=1e-2):
         components = self.get_dummy_components()
         for name, module in components.items():
@@ -237,7 +237,7 @@ class BriaPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
 
 
 @slow
-@require_torch_gpu
+@require_torch_accelerator
 class BriaPipelineSlowTests(unittest.TestCase):
     pipeline_class = BriaPipeline
     repo_id = "briaai/BRIA-3.2"
@@ -245,12 +245,12 @@ class BriaPipelineSlowTests(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):
         generator = torch.Generator(device="cpu").manual_seed(seed)