fix 3 xpu failures uts w/ latest pytorch (#12408)

fix xpu ut failures w/ latest pytorch
Signed-off-by: Yao, Matrix <matrix.yao@intel.com>

tests/pipelines/controlnet_hunyuandit/test_controlnet_hunyuandit.py

@@ -155,7 +155,7 @@ class HunyuanDiTControlNetPipelineFastTests(unittest.TestCase, PipelineTesterMix
 
         if torch_device == "xpu":
             expected_slice = np.array(
-                [0.6376953, 0.84375, 0.58691406, 0.48046875, 0.43652344, 0.5517578, 0.54248047, 0.5644531, 0.48217773]
+                [0.6948242, 0.89160156, 0.59375, 0.5078125, 0.57910156, 0.6035156, 0.58447266, 0.53564453, 0.52246094]
             )
         else:
             expected_slice = np.array(

tests/pipelines/flux/test_pipeline_flux.py

@@ -15,6 +15,7 @@ from diffusers import (
 )
 
 from ...testing_utils import (
+    Expectations,
     backend_empty_cache,
     nightly,
     numpy_cosine_similarity_distance,
@@ -276,10 +277,14 @@ class FluxPipelineSlowTests(unittest.TestCase):
         image = pipe(**inputs).images[0]
         image_slice = image[0, :10, :10]
         # fmt: off
-        expected_slice = np.array(
-            [0.3242, 0.3203, 0.3164, 0.3164, 0.3125, 0.3125, 0.3281, 0.3242, 0.3203, 0.3301, 0.3262, 0.3242, 0.3281, 0.3242, 0.3203, 0.3262, 0.3262, 0.3164, 0.3262, 0.3281, 0.3184, 0.3281, 0.3281, 0.3203, 0.3281, 0.3281, 0.3164, 0.3320, 0.3320, 0.3203],
-            dtype=np.float32,
+
+        expected_slices = Expectations(
+            {
+                ("cuda", None): np.array([0.3242, 0.3203, 0.3164, 0.3164, 0.3125, 0.3125, 0.3281, 0.3242, 0.3203, 0.3301, 0.3262, 0.3242, 0.3281, 0.3242, 0.3203, 0.3262, 0.3262, 0.3164, 0.3262, 0.3281, 0.3184, 0.3281, 0.3281, 0.3203, 0.3281, 0.3281, 0.3164, 0.3320, 0.3320, 0.3203], dtype=np.float32,),
+                ("xpu", 3): np.array([0.3301, 0.3281, 0.3359, 0.3203, 0.3203, 0.3281, 0.3281, 0.3301, 0.3340, 0.3281, 0.3320, 0.3359, 0.3281, 0.3301, 0.3320, 0.3242, 0.3301, 0.3281, 0.3242, 0.3320, 0.3320, 0.3281, 0.3320, 0.3320, 0.3262, 0.3320, 0.3301, 0.3301, 0.3359, 0.3320], dtype=np.float32,),
+            }
         )
+        expected_slice = expected_slices.get_expectation()
         # fmt: on
 
         max_diff = numpy_cosine_similarity_distance(expected_slice.flatten(), image_slice.flatten())

tests/quantization/gguf/test_gguf.py

@@ -360,33 +360,33 @@ class SD35LargeGGUFSingleFileTests(GGUFSingleFileTesterMixin, unittest.TestCase)
             {
                 ("xpu", 3): np.array(
                     [
-                        0.1953125,
-                        0.3125,
-                        0.31445312,
-                        0.13085938,
-                        0.30664062,
-                        0.29296875,
-                        0.11523438,
-                        0.2890625,
+                        0.16796875,
+                        0.27929688,
                         0.28320312,
-                        0.16601562,
-                        0.3046875,
-                        0.328125,
-                        0.140625,
-                        0.31640625,
+                        0.11328125,
+                        0.27539062,
+                        0.26171875,
+                        0.10742188,
+                        0.26367188,
+                        0.26171875,
+                        0.1484375,
+                        0.2734375,
+                        0.296875,
+                        0.13476562,
+                        0.2890625,
+                        0.30078125,
+                        0.1171875,
+                        0.28125,
+                        0.28125,
+                        0.16015625,
+                        0.31445312,
+                        0.30078125,
+                        0.15625,
                         0.32421875,
-                        0.12304688,
-                        0.3046875,
-                        0.3046875,
-                        0.17578125,
-                        0.3359375,
-                        0.3203125,
-                        0.16601562,
-                        0.34375,
-                        0.31640625,
-                        0.15429688,
-                        0.328125,
-                        0.31054688,
+                        0.296875,
+                        0.14453125,
+                        0.30859375,
+                        0.2890625,
                     ]
                 ),
                 ("cuda", 7): np.array(