diff --git a/kornia/augmentation/_2d/intensity/dissolving.py b/kornia/augmentation/_2d/intensity/dissolving.py
index 13e9df00b65..270f5278734 100644
--- a/kornia/augmentation/_2d/intensity/dissolving.py
+++ b/kornia/augmentation/_2d/intensity/dissolving.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Any, Dict, Optional, Tuple
 
 from kornia.augmentation import random_generator as rg
 from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
@@ -44,7 +44,7 @@ def __init__(
         version: str = "2.1",
         p: float = 0.5,
         keepdim: bool = False,
-        **kwargs
+        **kwargs,
     ) -> None:
         super().__init__(p=p, same_on_batch=True, keepdim=keepdim)
         self.step_range = step_range
diff --git a/kornia/core/__init__.py b/kornia/core/__init__.py
index 0aba5a8c0c1..2e1ebc2f7c8 100644
--- a/kornia/core/__init__.py
+++ b/kornia/core/__init__.py
@@ -1,3 +1,4 @@
+from . import external
 from ._backend import (
     Device,
     Dtype,
@@ -33,8 +34,6 @@
 )
 from .module import ImageModule
 from .tensor_wrapper import TensorWrapper  # type: ignore
-from . import external
-
 
 __all__ = [
     "external",
diff --git a/kornia/filters/dissolving.py b/kornia/filters/dissolving.py
index 0eed8f072dd..f4fb3734dd1 100644
--- a/kornia/filters/dissolving.py
+++ b/kornia/filters/dissolving.py
@@ -1,7 +1,5 @@
-from typing import Dict, Optional
-
 import torch
-import torch.nn as nn
+
 from kornia.core import Module, Tensor
 from kornia.core.external import diffusers
 
@@ -18,15 +16,14 @@ def __init__(self, model: Module, num_ddim_steps: int = 50) -> None:
 
     def predict_start_from_noise(self, noise_pred: Tensor, timestep: int, latent: Tensor) -> Tensor:
         return (
-            torch.sqrt(1. / self.scheduler.alphas_cumprod[timestep]) * latent - 
-            torch.sqrt(1. / self.scheduler.alphas_cumprod[timestep] - 1) * noise_pred
+            torch.sqrt(1.0 / self.scheduler.alphas_cumprod[timestep]) * latent
+            - torch.sqrt(1.0 / self.scheduler.alphas_cumprod[timestep] - 1) * noise_pred
         )
 
     @torch.no_grad()
     def init_prompt(self, prompt: str) -> None:
         uncond_input = self.model.tokenizer(
-            [""], padding="max_length", max_length=self.model.tokenizer.model_max_length,
-            return_tensors="pt"
+            [""], padding="max_length", max_length=self.model.tokenizer.model_max_length, return_tensors="pt"
         )
         uncond_embeddings = self.model.text_encoder(uncond_input.input_ids.to(self.model.device))[0]
         text_input = self.model.tokenizer(
@@ -45,14 +42,14 @@ def init_prompt(self, prompt: str) -> None:
     def encode_tensor_to_latent(self, image: Tensor) -> Tensor:
         with torch.no_grad():
             image = (image / 0.5 - 1).to(self.model.device)
-            latents = self.model.vae.encode(image)['latent_dist'].sample()
+            latents = self.model.vae.encode(image)["latent_dist"].sample()
             latents = latents * 0.18215
         return latents
 
     @torch.no_grad()
     def decode_tensor_to_latent(self, latents: Tensor) -> Tensor:
         latents = 1 / 0.18215 * latents.detach()
-        image = self.model.vae.decode(latents)['sample']
+        image = self.model.vae.decode(latents)["sample"]
         image = (image / 2 + 0.5).clamp(0, 1)
         return image
 
@@ -104,6 +101,7 @@ class StableDiffusionDissolving(Module):
         version: the version of the stable diffusion model.
         **kwargs: additional arguments for `.from_pretrained`.
     """
+
     def __init__(self, version: str = "2.1", **kwargs):
         super().__init__()
         StableDiffusionPipeline = diffusers.StableDiffusionPipeline
@@ -111,19 +109,26 @@ def __init__(self, version: str = "2.1", **kwargs):
 
         # Load the scheduler and model pipeline from diffusers library
         scheduler = DDIMScheduler(
-            beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", clip_sample=False, set_alpha_to_one=False,
+            beta_start=0.00085,
+            beta_end=0.012,
+            beta_schedule="scaled_linear",
+            clip_sample=False,
+            set_alpha_to_one=False,
             steps_offset=1,
         )
 
         if version == "1.4":
             self._sdm_model = StableDiffusionPipeline.from_pretrained(
-                "CompVis/stable-diffusion-v1-4", scheduler=scheduler, **kwargs)
+                "CompVis/stable-diffusion-v1-4", scheduler=scheduler, **kwargs
+            )
         elif version == "1.5":
             self._sdm_model = StableDiffusionPipeline.from_pretrained(
-                "runwayml/stable-diffusion-v1-5", scheduler=scheduler, **kwargs)
+                "runwayml/stable-diffusion-v1-5", scheduler=scheduler, **kwargs
+            )
         elif version == "2.1":
             self._sdm_model = StableDiffusionPipeline.from_pretrained(
-                "stabilityai/stable-diffusion-2-1", scheduler=scheduler, **kwargs)
+                "stabilityai/stable-diffusion-2-1", scheduler=scheduler, **kwargs
+            )
         else:
             raise NotImplementedError