diff --git a/ldm/models/diffusion/dpm_solver/dpm_solver.py b/ldm/models/diffusion/dpm_solver/dpm_solver.py
index 79390c0..c7c2e09 100644
--- a/ldm/models/diffusion/dpm_solver/dpm_solver.py
+++ b/ldm/models/diffusion/dpm_solver/dpm_solver.py
@@ -131,7 +131,7 @@ class NoiseScheduleVP:
                 self.log_alpha_array.to(t.device),
             ).reshape((-1))
         elif self.schedule == "linear":
-            return -0.25 * t ** 2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
+            return -0.25 * t**2 * (self.beta_1 - self.beta_0) - 0.5 * t * self.beta_0
         elif self.schedule == "cosine":
             log_alpha_fn = lambda s: torch.log(
                 torch.cos((s + self.cosine_s) / (1.0 + self.cosine_s) * math.pi / 2.0)
@@ -169,7 +169,7 @@ class NoiseScheduleVP:
                 * (self.beta_1 - self.beta_0)
                 * torch.logaddexp(-2.0 * lamb, torch.zeros((1,)).to(lamb))
             )
-            Delta = self.beta_0 ** 2 + tmp
+            Delta = self.beta_0**2 + tmp
             return tmp / (torch.sqrt(Delta) + self.beta_0) / (self.beta_1 - self.beta_0)
         elif self.schedule == "discrete":
             log_alpha = -0.5 * torch.logaddexp(
@@ -522,15 +522,21 @@ class DPM_Solver:
         if order == 3:
             K = steps // 3 + 1
             if steps % 3 == 0:
-                orders = [3, ] * (
+                orders = [
+                    3,
+                ] * (
                     K - 2
                 ) + [2, 1]
             elif steps % 3 == 1:
-                orders = [3, ] * (
+                orders = [
+                    3,
+                ] * (
                     K - 1
                 ) + [1]
             else:
-                orders = [3, ] * (
+                orders = [
+                    3,
+                ] * (
                     K - 1
                 ) + [2]
         elif order == 2:
@@ -541,7 +547,9 @@ class DPM_Solver:
                 ] * K
             else:
                 K = steps // 2 + 1
-                orders = [2, ] * (
+                orders = [
+                    2,
+                ] * (
                     K - 1
                 ) + [1]
         elif order == 1:
@@ -1000,7 +1008,7 @@ class DPM_Solver:
                 - expand_dims(alpha_t * (torch.exp(-h) - 1.0), dims) * model_prev_0
                 + expand_dims(alpha_t * ((torch.exp(-h) - 1.0) / h + 1.0), dims) * D1
                 - expand_dims(
-                    alpha_t * ((torch.exp(-h) - 1.0 + h) / h ** 2 - 0.5), dims
+                    alpha_t * ((torch.exp(-h) - 1.0 + h) / h**2 - 0.5), dims
                 )
                 * D2
             )
@@ -1009,7 +1017,7 @@ class DPM_Solver:
                 expand_dims(torch.exp(log_alpha_t - log_alpha_prev_0), dims) * x
                 - expand_dims(sigma_t * (torch.exp(h) - 1.0), dims) * model_prev_0
                 - expand_dims(sigma_t * ((torch.exp(h) - 1.0) / h - 1.0), dims) * D1
-                - expand_dims(sigma_t * ((torch.exp(h) - 1.0 - h) / h ** 2 - 0.5), dims)
+                - expand_dims(sigma_t * ((torch.exp(h) - 1.0 - h) / h**2 - 0.5), dims)
                 * D2
             )
         return x_t