make style

examples/research_projects/intel_opts/textual_inversion/textual_inversion_bf16.py

@@ -336,10 +336,7 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            (
-                h,
-                w,
-            ) = (
+            (h, w,) = (
                 img.shape[0],
                 img.shape[1],
             )

examples/textual_inversion/textual_inversion.py

@@ -432,10 +432,7 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            (
-                h,
-                w,
-            ) = (
+            (h, w,) = (
                 img.shape[0],
                 img.shape[1],
             )

examples/textual_inversion/textual_inversion_flax.py

@@ -306,10 +306,7 @@ class TextualInversionDataset(Dataset):
 
         if self.center_crop:
             crop = min(img.shape[0], img.shape[1])
-            (
-                h,
-                w,
-            ) = (
+            (h, w,) = (
                 img.shape[0],
                 img.shape[1],
             )

src/diffusers/models/attention.py

@@ -94,10 +94,8 @@ class AttentionBlock(nn.Module):
         if use_memory_efficient_attention_xformers:
             if not is_xformers_available():
                 raise ModuleNotFoundError(
-                    (
                     "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
-                        " xformers"
-                    ),
+                    " xformers",
                     name="xformers",
                 )
             elif not torch.cuda.is_available():

src/diffusers/models/cross_attention.py

@@ -111,10 +111,8 @@ class CrossAttention(nn.Module):
                 )
             elif not is_xformers_available():
                 raise ModuleNotFoundError(
-                    (
                     "Refer to https://github.com/facebookresearch/xformers for more information on how to install"
-                        " xformers"
-                    ),
+                    " xformers",
                     name="xformers",
                 )
             elif not torch.cuda.is_available():

src/diffusers/schedulers/scheduling_euler_ancestral_discrete.py

@@ -189,11 +189,9 @@ class EulerAncestralDiscreteScheduler(SchedulerMixin, ConfigMixin):
             or isinstance(timestep, torch.LongTensor)
         ):
             raise ValueError(
-                (
                 "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                 " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
-                    " one of the `scheduler.timesteps` as a timestep."
-                ),
+                " one of the `scheduler.timesteps` as a timestep.",
             )
 
         if not self.is_scale_input_called:

src/diffusers/schedulers/scheduling_euler_discrete.py

@@ -198,11 +198,9 @@ class EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):
             or isinstance(timestep, torch.LongTensor)
         ):
             raise ValueError(
-                (
                 "Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"
                 " `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"
-                    " one of the `scheduler.timesteps` as a timestep."
-                ),
+                " one of the `scheduler.timesteps` as a timestep.",
             )
 
         if not self.is_scale_input_called:

tests/test_scheduler.py

@@ -537,10 +537,8 @@ class SchedulerCommonTest(unittest.TestCase):
                 )
                 self.assertTrue(
                     hasattr(scheduler, "scale_model_input"),
-                    (
                     f"{scheduler_class} does not implement a required class method `scale_model_input(sample,"
-                        " timestep)`"
-                    ),
+                    " timestep)`",
                 )
             self.assertTrue(
                 hasattr(scheduler, "step"),