diff --git a/tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py b/tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py
index 84085f9d7d39..b2d6f0fc0590 100644
--- a/tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py
+++ b/tests/pipelines/kandinsky2_2/test_kandinsky_controlnet.py
@@ -289,6 +289,5 @@ def test_kandinsky_controlnet(self):
         image = output.images[0]
 
         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 < 2e-4
diff --git a/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py b/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py
index 342561d4f5e9..ab0221dc815e 100644
--- a/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py
+++ b/tests/pipelines/ledits_pp/test_ledits_pp_stable_diffusion.py
@@ -29,6 +29,7 @@
     UNet2DConditionModel,
 )
 from diffusers.utils.testing_utils import (
+    Expectations,
     backend_empty_cache,
     enable_full_determinism,
     floats_tensor,
@@ -244,7 +245,35 @@ def test_ledits_pp_editing(self):
 
         output_slice = reconstruction[150:153, 140:143, -1]
         output_slice = output_slice.flatten()
-        expected_slice = np.array(
-            [0.9453125, 0.93310547, 0.84521484, 0.94628906, 0.9111328, 0.80859375, 0.93847656, 0.9042969, 0.8144531]
+        expected_slices = Expectations(
+            {
+                ("xpu", 3): np.array(
+                    [
+                        0.9511719,
+                        0.94140625,
+                        0.87597656,
+                        0.9472656,
+                        0.9296875,
+                        0.8378906,
+                        0.94433594,
+                        0.91503906,
+                        0.8491211,
+                    ]
+                ),
+                ("cuda", 7): np.array(
+                    [
+                        0.9453125,
+                        0.93310547,
+                        0.84521484,
+                        0.94628906,
+                        0.9111328,
+                        0.80859375,
+                        0.93847656,
+                        0.9042969,
+                        0.8144531,
+                    ]
+                ),
+            }
         )
+        expected_slice = expected_slices.get_expectation()
         assert np.abs(output_slice - expected_slice).max() < 1e-2
diff --git a/tests/pipelines/test_pipelines_common.py b/tests/pipelines/test_pipelines_common.py
index 207cff2a3cdc..4a3a9b1796a1 100644
--- a/tests/pipelines/test_pipelines_common.py
+++ b/tests/pipelines/test_pipelines_common.py
@@ -49,6 +49,7 @@
 from diffusers.utils.testing_utils import (
     CaptureLogger,
     backend_empty_cache,
+    numpy_cosine_similarity_distance,
     require_accelerate_version_greater,
     require_accelerator,
     require_hf_hub_version_greater,
@@ -1394,9 +1395,8 @@ def test_float16_inference(self, expected_max_diff=5e-2):
             fp16_inputs["generator"] = self.get_generator(0)
 
         output_fp16 = pipe_fp16(**fp16_inputs)[0]
-
-        max_diff = np.abs(to_np(output) - to_np(output_fp16)).max()
-        self.assertLess(max_diff, expected_max_diff, "The outputs of the fp16 and fp32 pipelines are too different.")
+        max_diff = numpy_cosine_similarity_distance(output.flatten(), output_fp16.flatten())
+        assert max_diff < 2e-4
 
     @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU")
     @require_accelerator
diff --git a/tests/quantization/gguf/test_gguf.py b/tests/quantization/gguf/test_gguf.py
index ae3900459de2..5d1fa4c22e2a 100644
--- a/tests/quantization/gguf/test_gguf.py
+++ b/tests/quantization/gguf/test_gguf.py
@@ -286,33 +286,33 @@ def test_pipeline_inference(self):
             {
                 ("xpu", 3): np.array(
                     [
-                        0.19335938,
-                        0.3125,
-                        0.3203125,
-                        0.1328125,
-                        0.3046875,
-                        0.296875,
-                        0.11914062,
-                        0.2890625,
-                        0.2890625,
-                        0.16796875,
-                        0.30273438,
-                        0.33203125,
-                        0.14648438,
-                        0.31640625,
-                        0.33007812,
+                        0.16210938,
+                        0.2734375,
+                        0.27734375,
+                        0.109375,
+                        0.27148438,
+                        0.2578125,
+                        0.1015625,
+                        0.2578125,
+                        0.2578125,
+                        0.14453125,
+                        0.26953125,
+                        0.29492188,
                         0.12890625,
-                        0.3046875,
-                        0.30859375,
-                        0.17773438,
-                        0.33789062,
-                        0.33203125,
-                        0.16796875,
-                        0.34570312,
-                        0.32421875,
+                        0.28710938,
+                        0.30078125,
+                        0.11132812,
+                        0.27734375,
+                        0.27929688,
                         0.15625,
-                        0.33203125,
-                        0.31445312,
+                        0.31054688,
+                        0.296875,
+                        0.15234375,
+                        0.3203125,
+                        0.29492188,
+                        0.140625,
+                        0.3046875,
+                        0.28515625,
                     ]
                 ),
                 ("cuda", 7): np.array(