diff --git a/lama_cleaner/model/ldm.py b/lama_cleaner/model/ldm.py
index a45a7e9..27f6d5a 100644
--- a/lama_cleaner/model/ldm.py
+++ b/lama_cleaner/model/ldm.py
@@ -11,7 +11,12 @@ from lama_cleaner.schema import Config, LDMSampler
 
 torch.manual_seed(42)
 import torch.nn as nn
-from lama_cleaner.helper import download_model, norm_img, get_cache_path_by_url, load_jit_model
+from lama_cleaner.helper import (
+    download_model,
+    norm_img,
+    get_cache_path_by_url,
+    load_jit_model,
+)
 from lama_cleaner.model.utils import (
     make_beta_schedule,
     timestep_embedding,
@@ -92,7 +97,7 @@ class DDPM(nn.Module):
         self.linear_start = linear_start
         self.linear_end = linear_end
         assert (
-                alphas_cumprod.shape[0] == self.num_timesteps
+            alphas_cumprod.shape[0] == self.num_timesteps
         ), "alphas have to be defined for each timestep"
 
         to_torch = lambda x: torch.tensor(x, dtype=torch.float32).to(self.device)
@@ -118,7 +123,7 @@ class DDPM(nn.Module):
 
         # calculations for posterior q(x_{t-1} | x_t, x_0)
         posterior_variance = (1 - self.v_posterior) * betas * (
-                1.0 - alphas_cumprod_prev
+            1.0 - alphas_cumprod_prev
         ) / (1.0 - alphas_cumprod) + self.v_posterior * betas
         # above: equal to 1. / (1. / (1. - alpha_cumprod_tm1) + alpha_t / beta_t)
         self.register_buffer("posterior_variance", to_torch(posterior_variance))
@@ -139,17 +144,17 @@ class DDPM(nn.Module):
         )
 
         if self.parameterization == "eps":
-            lvlb_weights = self.betas ** 2 / (
-                    2
-                    * self.posterior_variance
-                    * to_torch(alphas)
-                    * (1 - self.alphas_cumprod)
+            lvlb_weights = self.betas**2 / (
+                2
+                * self.posterior_variance
+                * to_torch(alphas)
+                * (1 - self.alphas_cumprod)
             )
         elif self.parameterization == "x0":
             lvlb_weights = (
-                    0.5
-                    * np.sqrt(torch.Tensor(alphas_cumprod))
-                    / (2.0 * 1 - torch.Tensor(alphas_cumprod))
+                0.5
+                * np.sqrt(torch.Tensor(alphas_cumprod))
+                / (2.0 * 1 - torch.Tensor(alphas_cumprod))
             )
         else:
             raise NotImplementedError("mu not supported")
@@ -222,7 +227,7 @@ class LatentDiffusion(DDPM):
 class LDM(InpaintModel):
     pad_mod = 32
 
-    def __init__(self, device, fp16: bool = True):
+    def __init__(self, device, fp16: bool = True, **kwargs):
         self.fp16 = fp16
         super().__init__(device)
         self.device = device
diff --git a/lama_cleaner/model/zits.py b/lama_cleaner/model/zits.py
index db59c63..597839c 100644
--- a/lama_cleaner/model/zits.py
+++ b/lama_cleaner/model/zits.py
@@ -206,7 +206,7 @@ class ZITS(InpaintModel):
     pad_mod = 32
     pad_to_square = True
 
-    def __init__(self, device):
+    def __init__(self, device, **kwargs):
         """
 
         Args: