[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

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

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",

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
 
@@ -104,26 +101,34 @@ 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
  DDIMScheduler = diffusers.DDIMScheduler
 
  # 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