Fix typos

examples/community/stable_diffusion_reference.py

@@ -31,7 +31,7 @@
                 torch_dtype=torch.float16
                 ).to('cuda:0')
 
-        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe_controlnet.scheduler.config)
+        >>> pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
 
         >>> result_img = pipe(ref_image=input_image,
                         prompt="1girl",

src/diffusers/loaders/lora.py

@@ -1192,7 +1192,7 @@ def set_lora_device(self, adapter_names: List[str], device: Union[torch.device,
 class StableDiffusionXLLoraLoaderMixin(LoraLoaderMixin):
     """This class overrides `LoraLoaderMixin` with LoRA loading/saving code that's specific to SDXL"""
 
-    # Overrride to properly handle the loading and unloading of the additional text encoder.
+    # Override to properly handle the loading and unloading of the additional text encoder.
     def load_lora_weights(
         self,
         pretrained_model_name_or_path_or_dict: Union[str, Dict[str, torch.Tensor]],

src/diffusers/loaders/textual_inversion.py

@@ -215,7 +215,7 @@ def _retrieve_tokens_and_embeddings(tokens, state_dicts, tokenizer):
                 embedding = state_dict["string_to_param"]["*"]
             else:
                 raise ValueError(
-                    f"Loaded state dictonary is incorrect: {state_dict}. \n\n"
+                    f"Loaded state dictionary is incorrect: {state_dict}. \n\n"
                     "Please verify that the loaded state dictionary of the textual embedding either only has a single key or includes the `string_to_param`"
                     " input key."
                 )

src/diffusers/pipelines/pipeline_utils.py

@@ -170,7 +170,7 @@ def is_safetensors_compatible(filenames, variant=None, passed_components=None) -
             sf_filenames.add(os.path.normpath(filename))
 
     for filename in pt_filenames:
-        #  filename = 'foo/bar/baz.bam' -> path = 'foo/bar', filename = 'baz', extention = '.bam'
+        #  filename = 'foo/bar/baz.bam' -> path = 'foo/bar', filename = 'baz', extension = '.bam'
         path, filename = os.path.split(filename)
         filename, extension = os.path.splitext(filename)
 
@@ -375,7 +375,7 @@ def _get_pipeline_class(
 
         if repo_id is not None and hub_revision is not None:
             # if we load the pipeline code from the Hub
-            # make sure to overwrite the `revison`
+            # make sure to overwrite the `revision`
             revision = hub_revision
 
         return get_class_from_dynamic_module(
@@ -451,7 +451,7 @@ def load_sub_model(
     )
 
     load_method_name = None
-    # retrive load method name
+    # retrieve load method name
     for class_name, class_candidate in class_candidates.items():
         if class_candidate is not None and issubclass(class_obj, class_candidate):
             load_method_name = importable_classes[class_name][1]
@@ -1897,7 +1897,7 @@ def download(cls, pretrained_model_name, **kwargs) -> Union[str, os.PathLike]:
             else:
                 # 2. we forced `local_files_only=True` when `model_info` failed
                 raise EnvironmentError(
-                    f"Cannot load model {pretrained_model_name}: model is not cached locally and an error occured"
+                    f"Cannot load model {pretrained_model_name}: model is not cached locally and an error occurred"
                     " while trying to fetch metadata from the Hub. Please check out the root cause in the stacktrace"
                     " above."
                 ) from model_info_call_error