Fix typos and add Typo check GitHub Action (huggingface#483)

* Fix typos

* Add a typo check action

* Fix a bug

* Changed to manual typo check currently

Ref: huggingface#483 (review)

Co-authored-by: Anton Lozhkov <[email protected]>

* Removed a confusing message

* Renamed "nin_shortcut" to "in_shortcut"

* Add memo about NIN

Co-authored-by: Anton Lozhkov <[email protected]>

Author: shirayu

.github/workflows/typos.yml

@@ -0,0 +1,14 @@
+name: Check typos
+
+on:
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - name: typos-action
+        uses: crate-ci/[email protected]

README.md

@@ -21,7 +21,7 @@ as a modular toolbox for inference and training of diffusion models.
 More precisely, 🤗 Diffusers offers:
 
 - State-of-the-art diffusion pipelines that can be run in inference with just a couple of lines of code (see [src/diffusers/pipelines](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines)). Check [this overview](https://github.com/huggingface/diffusers/tree/main/src/diffusers/pipelines/README.md#pipelines-summary) to see all supported pipelines and their corresponding official papers.
-- Various noise schedulers that can be used interchangeably for the prefered speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)).
+- Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference (see [src/diffusers/schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers)).
 - Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system (see [src/diffusers/models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models)).
 - Training examples to show how to train the most popular diffusion model tasks (see [examples](https://github.com/huggingface/diffusers/tree/main/examples), *e.g.* [unconditional-image-generation](https://github.com/huggingface/diffusers/tree/main/examples/unconditional_image_generation)).
 
@@ -297,7 +297,7 @@ with autocast("cuda"):
 image.save("ddpm_generated_image.png")
 ```
 - [Unconditional Latent Diffusion](https://huggingface.co/CompVis/ldm-celebahq-256)
-- [Unconditional Diffusion with continous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024)
+- [Unconditional Diffusion with continuous scheduler](https://huggingface.co/google/ncsnpp-ffhq-1024)
 
 **Other Notebooks**:
 * [image-to-image generation with Stable Diffusion](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/image_2_image_using_diffusers.ipynb) ![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg),
@@ -346,8 +346,8 @@ The class provides functionality to compute previous image according to alpha, b
 
 ## Philosophy
 
-- Readability and clarity is prefered over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.
-- Diffusers is **modality independent** and focuses on providing pretrained models and tools to build systems that generate **continous outputs**, *e.g.* vision and audio.
+- Readability and clarity is preferred over highly optimized code. A strong importance is put on providing readable, intuitive and elementary code design. *E.g.*, the provided [schedulers](https://github.com/huggingface/diffusers/tree/main/src/diffusers/schedulers) are separated from the provided [models](https://github.com/huggingface/diffusers/tree/main/src/diffusers/models) and provide well-commented code that can be read alongside the original paper.
+- Diffusers is **modality independent** and focuses on providing pretrained models and tools to build systems that generate **continuous outputs**, *e.g.* vision and audio.
 - Diffusion models and schedulers are provided as concise, elementary building blocks. In contrast, diffusion pipelines are a collection of end-to-end diffusion systems that can be used out-of-the-box, should stay as close as possible to their original implementation and can include components of another library, such as text-encoders. Examples for diffusion pipelines are [Glide](https://github.com/openai/glide-text2im) and [Latent Diffusion](https://github.com/CompVis/latent-diffusion).
 
 ## In the works

_typos.toml

@@ -0,0 +1,12 @@
+# Files for typos
+# Instruction:  https://github.com/marketplace/actions/typos-action#getting-started
+
+[default.extend-identifiers]
+
+[default.extend-words]
+NIN_="NIN" # NIN is used in scripts/convert_ncsnpp_original_checkpoint_to_diffusers.py
+nd="np" # nd may be np (numpy)
+
+
+[files]
+extend-exclude = ["_typos.toml"]

docs/source/api/schedulers.mdx

@@ -44,7 +44,7 @@ To this end, the design of schedulers is such that:
 The core API for any new scheduler must follow a limited structure.
 - Schedulers should provide one or more `def step(...)` functions that should be called to update the generated sample iteratively.
 - Schedulers should provide a `set_timesteps(...)` method that configures the parameters of a schedule function for a specific inference task.
-- Schedulers should be framework-agonstic, but provide a simple functionality to convert the scheduler into a specific framework, such as PyTorch
+- Schedulers should be framework-agnostic, but provide a simple functionality to convert the scheduler into a specific framework, such as PyTorch
 with a `set_format(...)` method.
 
 The base class [`SchedulerMixin`] implements low level utilities used by multiple schedulers.
@@ -53,7 +53,7 @@ The base class [`SchedulerMixin`] implements low level utilities used by multipl
 [[autodoc]] SchedulerMixin
 
 ### SchedulerOutput
-The class [`SchedulerOutput`] contains the ouputs from any schedulers `step(...)` call.
+The class [`SchedulerOutput`] contains the outputs from any schedulers `step(...)` call.
 
 [[autodoc]] schedulers.scheduling_utils.SchedulerOutput
 
@@ -71,7 +71,7 @@ Original paper can be found [here](https://arxiv.org/abs/2010.02502).
 
 [[autodoc]] DDPMScheduler
 
-#### Varience exploding, stochastic sampling from Karras et. al
+#### Variance exploding, stochastic sampling from Karras et. al
 
 Original paper can be found [here](https://arxiv.org/abs/2006.11239).
 

docs/source/quicktour.mdx

@@ -86,19 +86,19 @@ just like we did before only that now you need to pass your `AUTH_TOKEN`:
 >>> generator = DiffusionPipeline.from_pretrained("CompVis/stable-diffusion-v1-4", use_auth_token=AUTH_TOKEN)
 ```
 
-If you do not pass your authentification token you will see that the diffusion system will not be correctly 
-downloaded. Forcing the user to pass an authentification token ensures that it can be verified that the 
+If you do not pass your authentication token you will see that the diffusion system will not be correctly 
+downloaded. Forcing the user to pass an authentication token ensures that it can be verified that the 
 user has indeed read and accepted the license, which also means that an internet connection is required.
 
-**Note**: If you do not want to be forced to pass an authentification token, you can also simply download 
+**Note**: If you do not want to be forced to pass an authentication token, you can also simply download 
 the weights locally via:
 
 ```
 git lfs install
 git clone https://huggingface.co/CompVis/stable-diffusion-v1-4
 ```
 
-and then load locally saved weights into the pipeline. This way, you do not need to pass an authentification
+and then load locally saved weights into the pipeline. This way, you do not need to pass an authentication
 token. Assuming that `"./stable-diffusion-v1-4"` is the local path to the cloned stable-diffusion-v1-4 repo,
 you can also load the pipeline as follows:
 

docs/source/training/text_inversion.mdx

@@ -49,7 +49,7 @@ The `textual_inversion.py` script [here](https://github.com/huggingface/diffuser
 
 ### Installing the dependencies
 
-Before running the scipts, make sure to install the library's training dependencies:
+Before running the scripts, make sure to install the library's training dependencies:
 
 ```bash
 pip install diffusers[training] accelerate transformers
@@ -68,7 +68,7 @@ You need to accept the model license before downloading or using the weights. In
 
 You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
 
-Run the following command to autheticate your token
+Run the following command to authenticate your token
 
 ```bash
 huggingface-cli login

docs/source/training/unconditional_training.mdx

@@ -18,7 +18,7 @@ distribution.
 
 ## Installing the dependencies
 
-Before running the scipts, make sure to install the library's training dependencies:
+Before running the scripts, make sure to install the library's training dependencies:
 
 ```bash
 pip install diffusers[training] accelerate datasets
@@ -117,7 +117,7 @@ from datasets import load_dataset
 # example 1: local folder
 dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")
 
-# example 2: local files (suppoted formats are tar, gzip, zip, xz, rar, zstd)
+# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)
 dataset = load_dataset("imagefolder", data_files="path_to_zip_file")
 
 # example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)

examples/textual_inversion/README.md

@@ -14,7 +14,7 @@ Colab for inference
 ## Running locally 
 ### Installing the dependencies
 
-Before running the scipts, make sure to install the library's training dependencies:
+Before running the scripts, make sure to install the library's training dependencies:
 
 ```bash
 pip install diffusers[training] accelerate transformers
@@ -33,7 +33,7 @@ You need to accept the model license before downloading or using the weights. In
 
 You have to be a registered user in 🤗 Hugging Face Hub, and you'll also need to use an access token for the code to work. For more information on access tokens, please refer to [this section of the documentation](https://huggingface.co/docs/hub/security-tokens).
 
-Run the following command to autheticate your token
+Run the following command to authenticate your token
 
 ```bash
 huggingface-cli login

examples/textual_inversion/textual_inversion.py

@@ -422,7 +422,7 @@ def main():
         eps=args.adam_epsilon,
     )
 
-    # TODO (patil-suraj): laod scheduler using args
+    # TODO (patil-suraj): load scheduler using args
     noise_scheduler = DDPMScheduler(
         beta_start=0.00085, beta_end=0.012, beta_schedule="scaled_linear", num_train_timesteps=1000, tensor_format="pt"
     )

examples/unconditional_image_generation/README.md

@@ -4,7 +4,7 @@ Creating a training image set is [described in a different document](https://hug
 
 ### Installing the dependencies
 
-Before running the scipts, make sure to install the library's training dependencies:
+Before running the scripts, make sure to install the library's training dependencies:
 
 ```bash
 pip install diffusers[training] accelerate datasets
@@ -102,7 +102,7 @@ from datasets import load_dataset
 # example 1: local folder
 dataset = load_dataset("imagefolder", data_dir="path_to_your_folder")
 
-# example 2: local files (suppoted formats are tar, gzip, zip, xz, rar, zstd)
+# example 2: local files (supported formats are tar, gzip, zip, xz, rar, zstd)
 dataset = load_dataset("imagefolder", data_files="path_to_zip_file")
 
 # example 3: remote files (supported formats are tar, gzip, zip, xz, rar, zstd)