Fix typos (#978)

examples/community/README.md

@@ -355,7 +355,7 @@ generator = th.Generator("cuda").manual_seed(0)
 
 seed = 0
 prompt = "a forest | a camel"
-weights = " 1 | 1"  # Equal weight to each prompt. Cna be negative
+weights = " 1 | 1"  # Equal weight to each prompt. Can be negative
 
 images = []
 for i in range(4):

examples/community/composable_stable_diffusion.py

@@ -133,7 +133,7 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline):
                 tensor will ge generated by sampling using the supplied random `generator`.
             output_type (`str`, *optional*, defaults to `"pil"`):
                 The output format of the generate image. Choose between
-                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `nd.array`.
+                [PIL](https://pillow.readthedocs.io/en/stable/): `PIL.Image.Image` or `np.array`.
             return_dict (`bool`, *optional*, defaults to `True`):
                 Whether or not to return a [`~pipelines.stable_diffusion.StableDiffusionPipelineOutput`] instead of a
                 plain tuple.
@@ -264,7 +264,7 @@ class ComposableStableDiffusionPipeline(DiffusionPipeline):
 
         self.scheduler.set_timesteps(num_inference_steps, **extra_set_kwargs)
 
-        # if we use LMSDiscreteScheduler, let's make sure latents are mulitplied by sigmas
+        # if we use LMSDiscreteScheduler, let's make sure latents are multiplied by sigmas
         if isinstance(self.scheduler, LMSDiscreteScheduler):
             latents = latents * self.scheduler.sigmas[0]
 

examples/community/lpw_stable_diffusion.py

@@ -40,7 +40,7 @@ re_attention = re.compile(
 
 def parse_prompt_attention(text):
     """
-    Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
+    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
     Accepted tokens are:
       (abc) - increases attention to abc by a multiplier of 1.1
       (abc:3.12) - increases attention to abc by a multiplier of 3.12
@@ -237,9 +237,9 @@ def get_weighted_text_embeddings(
     r"""
     Prompts can be assigned with local weights using brackets. For example,
     prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
-    and the embedding tokens corresponding to the words get multipled by a constant, 1.1.
+    and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
 
-    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the origional mean.
+    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
 
     Args:
         pipe (`DiffusionPipeline`):

examples/community/lpw_stable_diffusion_onnx.py

@@ -38,7 +38,7 @@ re_attention = re.compile(
 
 def parse_prompt_attention(text):
     """
-    Parses a string with attention tokens and returns a list of pairs: text and its assoicated weight.
+    Parses a string with attention tokens and returns a list of pairs: text and its associated weight.
     Accepted tokens are:
       (abc) - increases attention to abc by a multiplier of 1.1
       (abc:3.12) - increases attention to abc by a multiplier of 3.12
@@ -236,9 +236,9 @@ def get_weighted_text_embeddings(
     r"""
     Prompts can be assigned with local weights using brackets. For example,
     prompt 'A (very beautiful) masterpiece' highlights the words 'very beautiful',
-    and the embedding tokens corresponding to the words get multipled by a constant, 1.1.
+    and the embedding tokens corresponding to the words get multiplied by a constant, 1.1.
 
-    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the origional mean.
+    Also, to regularize of the embedding, the weighted embedding would be scaled to preserve the original mean.
 
     Args:
         pipe (`DiffusionPipeline`):

src/diffusers/pipeline_utils.py

@@ -584,7 +584,7 @@ class DiffusionPipeline(ConfigMixin):
     def components(self) -> Dict[str, Any]:
         r"""
 
-        The `self.compenents` property can be useful to run different pipelines with the same weights and
+        The `self.components` property can be useful to run different pipelines with the same weights and
         configurations to not have to re-allocate memory.
 
         Examples:
@@ -602,7 +602,7 @@ class DiffusionPipeline(ConfigMixin):
         ```
 
         Returns:
-            A dictionaly containing all the modules needed to initialize the pipleline.
+            A dictionaly containing all the modules needed to initialize the pipeline.
         """
         components = {k: getattr(self, k) for k in self.config.keys() if not k.startswith("_")}
         expected_modules = set(inspect.signature(self.__init__).parameters.keys()) - set(["self"])