diff --git a/examples/community/pipeline_prompt2prompt.py b/examples/community/pipeline_prompt2prompt.py
index d0fa385627..580d3d38a0 100644
--- a/examples/community/pipeline_prompt2prompt.py
+++ b/examples/community/pipeline_prompt2prompt.py
@@ -501,7 +501,7 @@ class LocalBlend:
 
         alpha_layers = torch.zeros(len(prompts), 1, 1, 1, 1, self.max_num_words)
         for i, (prompt, words_) in enumerate(zip(prompts, words)):
-            if type(words_) is str:
+            if isinstance(words_, str):
                 words_ = [words_]
             for word in words_:
                 ind = get_word_inds(prompt, word, tokenizer)
@@ -565,7 +565,7 @@ class AttentionControlEdit(AttentionStore, abc.ABC):
         self.cross_replace_alpha = get_time_words_attention_alpha(
             prompts, num_steps, cross_replace_steps, self.tokenizer
         ).to(self.device)
-        if type(self_replace_steps) is float:
+        if isinstance(self_replace_steps, float):
             self_replace_steps = 0, self_replace_steps
         self.num_self_replace = int(num_steps * self_replace_steps[0]), int(num_steps * self_replace_steps[1])
         self.local_blend = local_blend  # 在外面定义后传进来
@@ -645,7 +645,7 @@ class AttentionReweight(AttentionControlEdit):
 def update_alpha_time_word(
     alpha, bounds: Union[float, Tuple[float, float]], prompt_ind: int, word_inds: Optional[torch.Tensor] = None
 ):
-    if type(bounds) is float:
+    if isinstance(bounds, float):
         bounds = 0, bounds
     start, end = int(bounds[0] * alpha.shape[0]), int(bounds[1] * alpha.shape[0])
     if word_inds is None:
@@ -659,7 +659,7 @@ def update_alpha_time_word(
 def get_time_words_attention_alpha(
     prompts, num_steps, cross_replace_steps: Union[float, Dict[str, Tuple[float, float]]], tokenizer, max_num_words=77
 ):
-    if type(cross_replace_steps) is not dict:
+    if not isinstance(cross_replace_steps, dict):
         cross_replace_steps = {"default_": cross_replace_steps}
     if "default_" not in cross_replace_steps:
         cross_replace_steps["default_"] = (0.0, 1.0)
@@ -679,9 +679,9 @@ def get_time_words_attention_alpha(
 ### util functions for LocalBlend and ReplacementEdit
 def get_word_inds(text: str, word_place: int, tokenizer):
     split_text = text.split(" ")
-    if type(word_place) is str:
+    if isinstance(word_place, str):
         word_place = [i for i, word in enumerate(split_text) if word_place == word]
-    elif type(word_place) is int:
+    elif isinstance(word_place, str):
         word_place = [word_place]
     out = []
     if len(word_place) > 0:
@@ -750,7 +750,7 @@ def get_replacement_mapper(prompts, tokenizer, max_len=77):
 def get_equalizer(
     text: str, word_select: Union[int, Tuple[int, ...]], values: Union[List[float], Tuple[float, ...]], tokenizer
 ):
-    if type(word_select) is int or type(word_select) is str:
+    if isinstance(word_select, (int, str)):
         word_select = (word_select,)
     equalizer = torch.ones(len(values), 77)
     values = torch.tensor(values, dtype=torch.float32)
diff --git a/examples/community/run_onnx_controlnet.py b/examples/community/run_onnx_controlnet.py
index b1a393ef69..6ccd7847c7 100644
--- a/examples/community/run_onnx_controlnet.py
+++ b/examples/community/run_onnx_controlnet.py
@@ -8,7 +8,6 @@ from typing import Any, Callable, Dict, List, Optional, Union
 import numpy as np
 import PIL.Image
 import torch
-from diffusers.utils.torch_utils import randn_tensor
 from PIL import Image
 from transformers import CLIPTokenizer
 
@@ -22,6 +21,7 @@ from diffusers.utils import (
     logging,
     replace_example_docstring,
 )
+from diffusers.utils.torch_utils import randn_tensor
 
 
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
diff --git a/examples/community/run_tensorrt_controlnet.py b/examples/community/run_tensorrt_controlnet.py
index 530994687e..fa60a66242 100644
--- a/examples/community/run_tensorrt_controlnet.py
+++ b/examples/community/run_tensorrt_controlnet.py
@@ -11,7 +11,6 @@ import PIL.Image
 import pycuda.driver as cuda
 import tensorrt as trt
 import torch
-from diffusers.utils.torch_utils import randn_tensor
 from PIL import Image
 from pycuda.tools import make_default_context
 from transformers import CLIPTokenizer
@@ -26,6 +25,7 @@ from diffusers.utils import (
     logging,
     replace_example_docstring,
 )
+from diffusers.utils.torch_utils import randn_tensor
 
 
 # Initialize CUDA
diff --git a/src/diffusers/models/attention_processor.py b/src/diffusers/models/attention_processor.py
index 36851085c4..34593a4e77 100644
--- a/src/diffusers/models/attention_processor.py
+++ b/src/diffusers/models/attention_processor.py
@@ -382,7 +382,7 @@ class Attention(nn.Module):
             }
 
             if hasattr(self.processor, "attention_op"):
-                kwargs["attention_op"] = self.prcoessor.attention_op
+                kwargs["attention_op"] = self.processor.attention_op
 
             lora_processor = lora_processor_cls(hidden_size, **kwargs)
             lora_processor.to_q_lora.load_state_dict(self.to_q.lora_layer.state_dict())
diff --git a/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py b/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
index 4133fcb48c..703db3204e 100644
--- a/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
+++ b/src/diffusers/pipelines/versatile_diffusion/modeling_text_unet.py
@@ -992,7 +992,7 @@ class UNetFlatConditionModel(ModelMixin, ConfigMixin):
         upsample_size = None
 
         if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):
-            logger.info("Forward upsample size to force interpolation output size.")
+            # Forward upsample size to force interpolation output size.
             forward_upsample_size = True
 
         # ensure attention_mask is a bias, and give it a singleton query_tokens dimension