diff --git a/sgm/inference/helpers.py b/sgm/inference/helpers.py
index 6f06218..b576669 100644
--- a/sgm/inference/helpers.py
+++ b/sgm/inference/helpers.py
@@ -35,9 +35,9 @@ class WatermarkEmbedder:
         if squeeze:
             image = image[None, ...]
         n = image.shape[0]
-        image_np = rearrange((255 * image).detach().cpu(), "n b c h w -> (n b) h w c").numpy()[
-            :, :, :, ::-1
-        ]
+        image_np = rearrange(
+            (255 * image).detach().cpu(), "n b c h w -> (n b) h w c"
+        ).numpy()[:, :, :, ::-1]
         # torch (b, c, h, w) in [0, 1] -> numpy (b, h, w, c) [0, 255]
         for k in range(image_np.shape[0]):
             image_np[k] = self.encoder.encode(image_np[k], "dwtDct")
@@ -185,7 +185,9 @@ def do_sample(
                 randn = torch.randn(shape).to(device)
 
                 def denoiser(input, sigma, c):
-                    return model.denoiser(model.model, input, sigma, c, **additional_model_inputs)
+                    return model.denoiser(
+                        model.model, input, sigma, c, **additional_model_inputs
+                    )
 
                 with ModelOnDevice(model.denoiser, device):
                     with ModelOnDevice(model.model, device):
@@ -212,10 +214,14 @@ def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
     for key in keys:
         if key == "txt":
             batch["txt"] = (
-                np.repeat([value_dict["prompt"]], repeats=math.prod(N)).reshape(N).tolist()
+                np.repeat([value_dict["prompt"]], repeats=math.prod(N))
+                .reshape(N)
+                .tolist()
             )
             batch_uc["txt"] = (
-                np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N)).reshape(N).tolist()
+                np.repeat([value_dict["negative_prompt"]], repeats=math.prod(N))
+                .reshape(N)
+                .tolist()
             )
         elif key == "original_size_as_tuple":
             batch["original_size_as_tuple"] = (
@@ -225,7 +231,9 @@ def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
             )
         elif key == "crop_coords_top_left":
             batch["crop_coords_top_left"] = (
-                torch.tensor([value_dict["crop_coords_top"], value_dict["crop_coords_left"]])
+                torch.tensor(
+                    [value_dict["crop_coords_top"], value_dict["crop_coords_left"]]
+                )
                 .to(device)
                 .repeat(*N, 1)
             )
@@ -234,7 +242,9 @@ def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
                 torch.tensor([value_dict["aesthetic_score"]]).to(device).repeat(*N, 1)
             )
             batch_uc["aesthetic_score"] = (
-                torch.tensor([value_dict["negative_aesthetic_score"]]).to(device).repeat(*N, 1)
+                torch.tensor([value_dict["negative_aesthetic_score"]])
+                .to(device)
+                .repeat(*N, 1)
             )
 
         elif key == "target_size_as_tuple":
@@ -255,7 +265,9 @@ def get_batch(keys, value_dict, N: Union[List, ListConfig], device="cuda"):
 def get_input_image_tensor(image: Image.Image, device="cuda"):
     w, h = image.size
     print(f"loaded input image of size ({w}, {h})")
-    width, height = map(lambda x: x - x % 64, (w, h))  # resize to integer multiple of 64
+    width, height = map(
+        lambda x: x - x % 64, (w, h)
+    )  # resize to integer multiple of 64
     image = image.resize((width, height))
     image_array = np.array(image.convert("RGB"))
     image_array = image_array[None].transpose(0, 3, 1, 2)
@@ -342,7 +354,9 @@ def do_img2img(
 
 class ModelOnDevice(object):
     def __init__(
-        self, model: Union[torch.nn.Module, torch.Tensor], device: Union[torch.device, str]
+        self,
+        model: Union[torch.nn.Module, torch.Tensor],
+        device: Union[torch.device, str],
     ):
         self.model = model
         self.device = torch.device(device)