use xfail

Author: sayakpaul

tests/lora/test_lora_layers_cogvideox.py

@@ -16,6 +16,7 @@
 import unittest
 
 import numpy as np
+import pytest
 import torch
 from transformers import AutoTokenizer, T5EncoderModel
 
@@ -29,7 +30,6 @@
 from diffusers.utils.testing_utils import (
     floats_tensor,
     is_peft_available,
-    is_torch_version,
     require_peft_backend,
     skip_mps,
     torch_device,
@@ -127,10 +127,7 @@ def get_dummy_inputs(self, with_generator=True):
         return noise, input_ids, pipeline_inputs
 
     @skip_mps
-    @unittest.skipIf(
-        torch.device(torch_device).type == "cpu" and is_torch_version(">=", "2.5"),
-        "Test not supported on PyTorch 2.5 and CPU.",
-    )
+    @pytest.mark.xfail("Test currently fails on CPU and PyTorch 2.5.1 but not on PyTorch 2.4.1.", strict=True)
     def test_lora_fuse_nan(self):
         for scheduler_cls in self.scheduler_classes:
             components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)

tests/lora/utils.py

@@ -19,6 +19,7 @@
 from itertools import product
 
 import numpy as np
+import pytest
 import torch
 
 from diffusers import (
@@ -32,7 +33,6 @@
 from diffusers.utils.testing_utils import (
     CaptureLogger,
     floats_tensor,
-    is_torch_version,
     require_peft_backend,
     require_peft_version_greater,
     require_transformers_version_greater,
@@ -1511,10 +1511,7 @@ def test_simple_inference_with_text_denoiser_multi_adapter_weighted(self):
             )
 
     @skip_mps
-    @unittest.skipIf(
-        torch.device(torch_device).type == "cpu" and is_torch_version(">=", "2.5"),
-        "Test not supported on PyTorch 2.5 and CPU.",
-    )
+    @pytest.mark.xfail("Test currently fails on CPU and PyTorch 2.5.1 but not on PyTorch 2.4.1.", strict=True)
     def test_lora_fuse_nan(self):
         for scheduler_cls in self.scheduler_classes:
             components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)