fix test_vanilla_funetuning failure on XPU and A100

tests/lora/test_lora_layers_sd.py

@@ -33,6 +33,7 @@
 )
 from diffusers.utils.import_utils import is_accelerate_available
 from diffusers.utils.testing_utils import (
+    Expectations,
     backend_empty_cache,
     load_image,
     nightly,
@@ -455,11 +456,54 @@ def test_vanilla_funetuning(self):
 
         images = pipe("A pokemon with blue eyes.", output_type="np", generator=generator, num_inference_steps=2).images
 
-        images = images[0, -3:, -3:, -1].flatten()
-
-        expected = np.array([0.7406, 0.699, 0.5963, 0.7493, 0.7045, 0.6096, 0.6886, 0.6388, 0.583])
+        image_slice = images[0, -3:, -3:, -1].flatten()
+
+        expected_slices = Expectations(
+            {
+                ("xpu", 3): np.array(
+                    [
+                        0.6544,
+                        0.6127,
+                        0.5397,
+                        0.6845,
+                        0.6047,
+                        0.5469,
+                        0.6349,
+                        0.5906,
+                        0.5382,
+                    ]
+                ),
+                ("cuda", 7): np.array(
+                    [
+                        0.7406,
+                        0.699,
+                        0.5963,
+                        0.7493,
+                        0.7045,
+                        0.6096,
+                        0.6886,
+                        0.6388,
+                        0.583,
+                    ]
+                ),
+                ("cuda", 8): np.array(
+                    [
+                        0.6542,
+                        0.61253,
+                        0.5396,
+                        0.6843,
+                        0.6044,
+                        0.5468,
+                        0.6349,
+                        0.5905,
+                        0.5381,
+                    ]
+                ),
+            }
+        )
+        expected_slice = expected_slices.get_expectation()
 
-        max_diff = numpy_cosine_similarity_distance(expected, images)
+        max_diff = numpy_cosine_similarity_distance(expected_slice, image_slice)
         assert max_diff < 1e-4
 
         pipe.unload_lora_weights()

tests/pipelines/test_pipelines_common.py

@@ -1347,7 +1347,7 @@ def test_components_function(self):
 
     @unittest.skipIf(torch_device not in ["cuda", "xpu"], reason="float16 requires CUDA or XPU")
     @require_accelerator
-    def test_float16_inference(self, expected_max_diff=6e-2):
+    def test_float16_inference(self, expected_max_diff=5e-2):
         components = self.get_dummy_components()
         pipe = self.pipeline_class(**components)
         for component in pipe.components.values():