make style

src/diffusers/pipelines/wuerstchen/pipeline_wuerstchen_combined.py

@@ -227,8 +227,9 @@ def __call__(
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.ImagePipelineOutput`] instead of a plain tuple.
             prior_callback (`Callable`, *optional*):
-                A function that will be called every `prior_callback_steps` steps during inference. The function will be
-                called with the following arguments: `prior_callback(step: int, timestep: int, latents: torch.FloatTensor)`.
+                A function that will be called every `prior_callback_steps` steps during inference. The function will
+                be called with the following arguments: `prior_callback(step: int, timestep: int, latents:
+                torch.FloatTensor)`.
             prior_callback_steps (`int`, *optional*, defaults to 1):
                 The frequency at which the `callback` function will be called. If not specified, the callback will be
                 called at every step.

src/diffusers/schedulers/scheduling_utils.py

@@ -15,7 +15,7 @@
 import os
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Dict, Optional, Union
+from typing import Optional, Union
 
 import torch
 

src/diffusers/schedulers/scheduling_utils_flax.py

@@ -16,7 +16,7 @@
 import os
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Dict, Optional, Tuple, Union
+from typing import Optional, Tuple, Union
 
 import flax
 import jax.numpy as jnp