Fix typos (#12705)

docs/source/en/modular_diffusers/custom_blocks.md

@@ -140,7 +140,7 @@ class Florence2ImageAnnotatorBlock(ModularPipelineBlocks):
                 type_hint=str,
                 required=True,
                 default="mask_image",
-                description="""Output type from annotation predictions. Availabe options are
+                description="""Output type from annotation predictions. Available options are
                 mask_image:
                     -black and white mask image for the given image based on the task type
                 mask_overlay:
@@ -256,7 +256,7 @@ class Florence2ImageAnnotatorBlock(ModularPipelineBlocks):
                 type_hint=str,
                 required=True,
                 default="mask_image",
-                description="""Output type from annotation predictions. Availabe options are
+                description="""Output type from annotation predictions. Available options are
                 mask_image:
                     -black and white mask image for the given image based on the task type
                 mask_overlay:

docs/source/en/modular_diffusers/loop_sequential_pipeline_blocks.md

@@ -53,7 +53,7 @@ The loop wrapper can pass additional arguments, like current iteration index, to
 
 A loop block is a [`~modular_pipelines.ModularPipelineBlocks`], but the `__call__` method behaves differently.
 
-- It recieves the iteration variable from the loop wrapper.
+- It receives the iteration variable from the loop wrapper.
 - It works directly with the [`~modular_pipelines.BlockState`] instead of the [`~modular_pipelines.PipelineState`].
 - It doesn't require retrieving or updating the [`~modular_pipelines.BlockState`].
 

examples/cogvideo/train_cogvideox_image_to_video_lora.py

@@ -149,13 +149,13 @@ def get_args():
         "--validation_prompt",
         type=str,
         default=None,
-        help="One or more prompt(s) that is used during validation to verify that the model is learning. Multiple validation prompts should be separated by the '--validation_prompt_seperator' string.",
+        help="One or more prompt(s) that is used during validation to verify that the model is learning. Multiple validation prompts should be separated by the '--validation_prompt_separator' string.",
     )
     parser.add_argument(
         "--validation_images",
         type=str,
         default=None,
-        help="One or more image path(s) that is used during validation to verify that the model is learning. Multiple validation paths should be separated by the '--validation_prompt_seperator' string. These should correspond to the order of the validation prompts.",
+        help="One or more image path(s) that is used during validation to verify that the model is learning. Multiple validation paths should be separated by the '--validation_prompt_separator' string. These should correspond to the order of the validation prompts.",
     )
     parser.add_argument(
         "--validation_prompt_separator",

examples/cogvideo/train_cogvideox_lora.py

@@ -140,7 +140,7 @@ def get_args():
         "--validation_prompt",
         type=str,
         default=None,
-        help="One or more prompt(s) that is used during validation to verify that the model is learning. Multiple validation prompts should be separated by the '--validation_prompt_seperator' string.",
+        help="One or more prompt(s) that is used during validation to verify that the model is learning. Multiple validation prompts should be separated by the '--validation_prompt_separator' string.",
     )
     parser.add_argument(
         "--validation_prompt_separator",

examples/research_projects/onnxruntime/text_to_image/README.md

@@ -4,7 +4,7 @@ The `train_text_to_image.py` script shows how to fine-tune stable diffusion mode
 
 ___Note___:
 
-___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparamters to get the best result on your dataset.___
+___This script is experimental. The script fine-tunes the whole model and often times the model overfits and runs into issues like catastrophic forgetting. It's recommended to try different hyperparameters to get the best result on your dataset.___
 
 
 ## Running locally with PyTorch

examples/research_projects/sdxl_flax/sdxl_single.py

@@ -18,7 +18,7 @@ cc.initialize_cache("/tmp/sdxl_cache")
 NUM_DEVICES = jax.device_count()
 
 # 1. Let's start by downloading the model and loading it into our pipeline class
-# Adhering to JAX's functional approach, the model's parameters are returned seperatetely and
+# Adhering to JAX's functional approach, the model's parameters are returned separately and
 # will have to be passed to the pipeline during inference
 pipeline, params = FlaxStableDiffusionXLPipeline.from_pretrained(
     "stabilityai/stable-diffusion-xl-base-1.0", revision="refs/pr/95", split_head_dim=True

src/diffusers/modular_pipelines/qwenimage/before_denoise.py

@@ -455,7 +455,7 @@ class QwenImageSetTimestepsStep(ModularPipelineBlocks):
 
     @property
     def description(self) -> str:
-        return "Step that sets the the scheduler's timesteps for text-to-image generation. Should be run after prepare latents step."
+        return "Step that sets the scheduler's timesteps for text-to-image generation. Should be run after prepare latents step."
 
     @property
     def expected_components(self) -> List[ComponentSpec]:
@@ -579,7 +579,7 @@ class QwenImageSetTimestepsWithStrengthStep(ModularPipelineBlocks):
 
     @property
     def description(self) -> str:
-        return "Step that sets the the scheduler's timesteps for image-to-image generation, and inpainting. Should be run after prepare latents step."
+        return "Step that sets the scheduler's timesteps for image-to-image generation, and inpainting. Should be run after prepare latents step."
 
     @property
     def expected_components(self) -> List[ComponentSpec]:

src/diffusers/pipelines/lucy/pipeline_lucy_edit.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 #
 # Modifications by Decart AI Team:
-# - Based on pipeline_wan.py, but with supports recieving a condition video appended to the channel dimension.
+# - Based on pipeline_wan.py, but with supports receiving a condition video appended to the channel dimension.
 
 import html
 from typing import Any, Callable, Dict, List, Optional, Tuple, Union