diff --git a/src/diffusers/schedulers/scheduling_sasolver.py b/src/diffusers/schedulers/scheduling_sasolver.py
index e09db049ce99..25f559fcd299 100644
--- a/src/diffusers/schedulers/scheduling_sasolver.py
+++ b/src/diffusers/schedulers/scheduling_sasolver.py
@@ -328,9 +328,7 @@ def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
         batch_size, channels, *remaining_dims = sample.shape
 
         if dtype not in (torch.float32, torch.float64):
-            sample = (
-                sample.float()
-            )  # upcast for quantile calculation, and clamp not implemented for cpu half
+            sample = sample.float()  # upcast for quantile calculation, and clamp not implemented for cpu half
 
         # Flatten sample for doing quantile calculation along each image
         sample = sample.reshape(batch_size, channels * np.prod(remaining_dims))
@@ -342,9 +340,7 @@ def _threshold_sample(self, sample: torch.FloatTensor) -> torch.FloatTensor:
             s, min=1, max=self.config.sample_max_value
         )  # When clamped to min=1, equivalent to standard clipping to [-1, 1]
         s = s.unsqueeze(1)  # (batch_size, 1) because clamp will broadcast along dim=0
-        sample = (
-            torch.clamp(sample, -s, s) / s
-        )  # "we threshold xt0 to the range [-s, s] and then divide by s"
+        sample = torch.clamp(sample, -s, s) / s  # "we threshold xt0 to the range [-s, s] and then divide by s"
 
         sample = sample.reshape(batch_size, channels, *remaining_dims)
         sample = sample.to(dtype)
@@ -360,11 +356,7 @@ def _sigma_to_t(self, sigma, log_sigmas):
         dists = log_sigma - log_sigmas[:, np.newaxis]
 
         # get sigmas range
-        low_idx = (
-            np.cumsum((dists >= 0), axis=0)
-            .argmax(axis=0)
-            .clip(max=log_sigmas.shape[0] - 2)
-        )
+        low_idx = np.cumsum((dists >= 0), axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
         high_idx = low_idx + 1
 
         low = log_sigmas[low_idx]
@@ -387,9 +379,7 @@ def _sigma_to_alpha_sigma_t(self, sigma):
         return alpha_t, sigma_t
 
     # Copied from diffusers.schedulers.scheduling_euler_discrete.EulerDiscreteScheduler._convert_to_karras
-    def _convert_to_karras(
-        self, in_sigmas: torch.FloatTensor, num_inference_steps
-    ) -> torch.FloatTensor:
+    def _convert_to_karras(self, in_sigmas: torch.FloatTensor, num_inference_steps) -> torch.FloatTensor:
         """Constructs the noise schedule of Karras et al. (2022)."""
 
         # Hack to make sure that other schedulers which copy this function don't break
@@ -1200,9 +1190,7 @@ def add_noise(
         timesteps: torch.IntTensor,
     ) -> torch.FloatTensor:
         # Make sure alphas_cumprod and timestep have same device and dtype as original_samples
-        alphas_cumprod = self.alphas_cumprod.to(
-            device=original_samples.device, dtype=original_samples.dtype
-        )
+        alphas_cumprod = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype)
         timesteps = timesteps.to(original_samples.device)
 
         sqrt_alpha_prod = alphas_cumprod[timesteps] ** 0.5
@@ -1215,9 +1203,7 @@ def add_noise(
         while len(sqrt_one_minus_alpha_prod.shape) < len(original_samples.shape):
             sqrt_one_minus_alpha_prod = sqrt_one_minus_alpha_prod.unsqueeze(-1)
 
-        noisy_samples = (
-            sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
-        )
+        noisy_samples = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
         return noisy_samples
 
     def __len__(self):