src/diffusers/pipelines/omnigen/pipeline_omnigen.py

@@ -23,12 +23,14 @@
 from ...models.autoencoders import AutoencoderKL
 from ...models.transformers import OmniGenTransformer2DModel
 from ...schedulers import FlowMatchEulerDiscreteScheduler
-from ...utils import is_torch_xla_available, logging, replace_example_docstring
+from ...utils import is_torch_xla_available, is_torchvision_available, logging, replace_example_docstring
 from ...utils.torch_utils import randn_tensor
 from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
-from .processor_omnigen import OmniGenMultiModalProcessor
 
 
+if is_torchvision_available():
+    from .processor_omnigen import OmniGenMultiModalProcessor
+
 if is_torch_xla_available():
     XLA_AVAILABLE = True
 else:

src/diffusers/pipelines/omnigen/processor_omnigen.py

@@ -18,7 +18,12 @@
 import numpy as np
 import torch
 from PIL import Image
-from torchvision import transforms
+
+from ...utils import is_torchvision_available
+
+
+if is_torchvision_available():
+    from torchvision import transforms
 
 
 def crop_image(pil_image, max_image_size):

tests/models/test_modeling_common.py

@@ -2107,7 +2107,7 @@ def test_hotswapping_compiled_model_linear(self, rank0, rank1):
     @parameterized.expand([(11, 11), (7, 13), (13, 7)])  # important to test small to large and vice versa
     def test_hotswapping_compiled_model_conv2d(self, rank0, rank1):
         if "unet" not in self.model_class.__name__.lower():
-            return
+            pytest.skip("Test only applies to UNet.")
 
         # It's important to add this context to raise an error on recompilation
         target_modules = ["conv", "conv1", "conv2"]
@@ -2117,7 +2117,7 @@ def test_hotswapping_compiled_model_conv2d(self, rank0, rank1):
     @parameterized.expand([(11, 11), (7, 13), (13, 7)])  # important to test small to large and vice versa
     def test_hotswapping_compiled_model_both_linear_and_conv2d(self, rank0, rank1):
         if "unet" not in self.model_class.__name__.lower():
-            return
+            pytest.skip("Test only applies to UNet.")
 
         # It's important to add this context to raise an error on recompilation
         target_modules = ["to_q", "conv"]

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

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

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

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(