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

* Fix typos

* Add a typo check action

* Fix a bug

* Changed to manual typo check currently

Ref: https://github.com/huggingface/diffusers/pull/483#pullrequestreview-1104468010

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* Removed a confusing message

* Renamed "nin_shortcut" to "in_shortcut"

* Add memo about NIN

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

.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/typos@v1.12.4

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

@@ -86,11 +86,11 @@ 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:
 
 ```
@@ -98,7 +98,7 @@ 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)

scripts/convert_ddpm_original_checkpoint_to_diffusers.py

@@ -22,7 +22,7 @@ def renew_resnet_paths(old_list, n_shave_prefix_segments=0):
         new_item = old_item
         new_item = new_item.replace("block.", "resnets.")
         new_item = new_item.replace("conv_shorcut", "conv1")
-        new_item = new_item.replace("nin_shortcut", "conv_shortcut")
+        new_item = new_item.replace("in_shortcut", "conv_shortcut")
         new_item = new_item.replace("temb_proj", "time_emb_proj")
 
         new_item = shave_segments(new_item, n_shave_prefix_segments=n_shave_prefix_segments)

scripts/generate_logits.py

@@ -124,4 +124,4 @@ for mod in models:
         assert torch.allclose(
             logits[0, 0, 0, :30], results["_".join("_".join(mod.modelId.split("/")).split("-"))], atol=1e-3
         )
-        print(f"{mod.modelId} has passed succesfully!!!")
+        print(f"{mod.modelId} has passed successfully!!!")

src/diffusers/configuration_utils.py

@@ -45,9 +45,9 @@ class ConfigMixin:
 
     Class attributes:
         - **config_name** (`str`) -- A filename under which the config should stored when calling
-          [`~ConfigMixin.save_config`] (should be overriden by parent class).
+          [`~ConfigMixin.save_config`] (should be overridden by parent class).
         - **ignore_for_config** (`List[str]`) -- A list of attributes that should not be saved in the config (should be
-          overriden by parent class).
+          overridden by parent class).
     """
     config_name = None
     ignore_for_config = []
@@ -125,7 +125,7 @@ class ConfigMixin:
                 A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
             output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
+                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
             local_files_only(`bool`, *optional*, defaults to `False`):
                 Whether or not to only look at local files (i.e., do not try to download the model).
             use_auth_token (`str` or *bool*, *optional*):

src/diffusers/modeling_utils.py

@@ -218,7 +218,7 @@ class ModelMixin(torch.nn.Module):
                 A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
             output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
+                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
             local_files_only(`bool`, *optional*, defaults to `False`):
                 Whether or not to only look at local files (i.e., do not try to download the model).
             use_auth_token (`str` or *bool*, *optional*):

src/diffusers/models/resnet.py

@@ -264,7 +264,7 @@ class ResnetBlock2D(nn.Module):
         time_embedding_norm="default",
         kernel=None,
         output_scale_factor=1.0,
-        use_nin_shortcut=None,
+        use_in_shortcut=None,
         up=False,
         down=False,
     ):
@@ -321,10 +321,10 @@ class ResnetBlock2D(nn.Module):
             else:
                 self.downsample = Downsample2D(in_channels, use_conv=False, padding=1, name="op")
 
-        self.use_nin_shortcut = self.in_channels != self.out_channels if use_nin_shortcut is None else use_nin_shortcut
+        self.use_in_shortcut = self.in_channels != self.out_channels if use_in_shortcut is None else use_in_shortcut
 
         self.conv_shortcut = None
-        if self.use_nin_shortcut:
+        if self.use_in_shortcut:
             self.conv_shortcut = torch.nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
 
     def forward(self, x, temb):

src/diffusers/models/unet_blocks.py

@@ -820,7 +820,7 @@ class AttnSkipDownBlock2D(nn.Module):
                 non_linearity=resnet_act_fn,
                 output_scale_factor=output_scale_factor,
                 pre_norm=resnet_pre_norm,
-                use_nin_shortcut=True,
+                use_in_shortcut=True,
                 down=True,
                 kernel="fir",
             )
@@ -900,7 +900,7 @@ class SkipDownBlock2D(nn.Module):
                 non_linearity=resnet_act_fn,
                 output_scale_factor=output_scale_factor,
                 pre_norm=resnet_pre_norm,
-                use_nin_shortcut=True,
+                use_in_shortcut=True,
                 down=True,
                 kernel="fir",
             )
@@ -1355,7 +1355,7 @@ class AttnSkipUpBlock2D(nn.Module):
                 non_linearity=resnet_act_fn,
                 output_scale_factor=output_scale_factor,
                 pre_norm=resnet_pre_norm,
-                use_nin_shortcut=True,
+                use_in_shortcut=True,
                 up=True,
                 kernel="fir",
             )
@@ -1452,7 +1452,7 @@ class SkipUpBlock2D(nn.Module):
                 non_linearity=resnet_act_fn,
                 output_scale_factor=output_scale_factor,
                 pre_norm=resnet_pre_norm,
-                use_nin_shortcut=True,
+                use_in_shortcut=True,
                 up=True,
                 kernel="fir",
             )

src/diffusers/pipeline_utils.py

@@ -86,7 +86,7 @@ class DiffusionPipeline(ConfigMixin):
     Class attributes:
 
         - **config_name** ([`str`]) -- name of the config file that will store the class and module names of all
-          compenents of the diffusion pipeline.
+          components of the diffusion pipeline.
     """
     config_name = "model_index.json"
 
@@ -95,7 +95,7 @@ class DiffusionPipeline(ConfigMixin):
         from diffusers import pipelines
 
         for name, module in kwargs.items():
-            # retrive library
+            # retrieve library
             library = module.__module__.split(".")[0]
 
             # check if the module is a pipeline module
@@ -109,7 +109,7 @@ class DiffusionPipeline(ConfigMixin):
             if library not in LOADABLE_CLASSES or is_pipeline_module:
                 library = pipeline_dir
 
-            # retrive class_name
+            # retrieve class_name
             class_name = module.__class__.__name__
 
             register_dict = {name: (library, class_name)}
@@ -217,7 +217,7 @@ class DiffusionPipeline(ConfigMixin):
                 A dictionary of proxy servers to use by protocol or endpoint, e.g., `{'http': 'foo.bar:3128',
                 'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
             output_loading_info(`bool`, *optional*, defaults to `False`):
-                Whether ot not to also return a dictionary containing missing keys, unexpected keys and error messages.
+                Whether or not to also return a dictionary containing missing keys, unexpected keys and error messages.
             local_files_only(`bool`, *optional*, defaults to `False`):
                 Whether or not to only look at local files (i.e., do not try to download the model).
             use_auth_token (`str` or *bool*, *optional*):
@@ -234,7 +234,7 @@ class DiffusionPipeline(ConfigMixin):
 
             kwargs (remaining dictionary of keyword arguments, *optional*):
                 Can be used to overwrite load - and saveable variables - *i.e.* the pipeline components - of the
-                speficic pipeline class. The overritten components are then directly passed to the pipelines `__init__`
+                specific pipeline class. The overritten components are then directly passed to the pipelines `__init__`
                 method. See example below for more information.
 
         <Tip>

src/diffusers/pipelines/README.md

@@ -70,7 +70,7 @@ not be used for training. If you want to store the gradients during the forward
 
 ## Contribution
 
-We are more than happy about any contribution to the offically supported pipelines 🤗. We aspire 
+We are more than happy about any contribution to the officially supported pipelines 🤗. We aspire 
 all of our pipelines to be  **self-contained**, **easy-to-tweak**, **beginner-friendly** and for **one-purpose-only**.
 
 - **Self-contained**: A pipeline shall be as self-contained as possible. More specifically, this means that all functionality should be either directly defined in the pipeline file iteslf, should be inherited from (and only from) the [`DiffusionPipeline` class](https://github.com/huggingface/diffusers/blob/5cbed8e0d157f65d3ddc2420dfd09f2df630e978/src/diffusers/pipeline_utils.py#L56) or be directly attached to the model and scheduler components of the pipeline. 

src/diffusers/pipelines/ddim/pipeline_ddim.py

@@ -64,7 +64,7 @@ class DDIMPipeline(DiffusionPipeline):
                 expense of slower inference.
             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 [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
 

src/diffusers/pipelines/ddpm/pipeline_ddpm.py

@@ -57,7 +57,7 @@ class DDPMPipeline(DiffusionPipeline):
                 deterministic.
             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 [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
 

src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py

@@ -85,7 +85,7 @@ class LDMTextToImagePipeline(DiffusionPipeline):
                 deterministic.
             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*):
                 Whether or not to return a [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
 

src/diffusers/pipelines/latent_diffusion_uncond/pipeline_latent_diffusion_uncond.py

@@ -50,7 +50,7 @@ class LDMPipeline(DiffusionPipeline):
                 expense of slower inference.
             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 [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
 

src/diffusers/pipelines/pndm/pipeline_pndm.py

@@ -63,7 +63,7 @@ class PNDMPipeline(DiffusionPipeline):
                 generator](https://pytorch.org/docs/stable/generated/torch.Generator.html) to make generation
                 deterministic.
             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`.
+                between [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
                 [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
 

src/diffusers/pipelines/score_sde_ve/pipeline_score_sde_ve.py

@@ -43,7 +43,7 @@ class ScoreSdeVePipeline(DiffusionPipeline):
                 deterministic.
             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 [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
 

src/diffusers/pipelines/stable_diffusion/README.md

@@ -12,7 +12,7 @@ The summary of the model is the following:
 
 - Stable Diffusion has the same architecture as [Latent Diffusion](https://arxiv.org/abs/2112.10752) but uses a frozen CLIP Text Encoder instead of training the text encoder jointly with the diffusion model.
 - An in-detail explanation of the Stable Diffusion model can be found under [Stable Diffusion with 🧨 Diffusers](https://huggingface.co/blog/stable_diffusion).
-- If you don't want to rely on the Hugging Face Hub and having to pass a authentification token, you can 
+- If you don't want to rely on the Hugging Face Hub and having to pass a authentication token, you can 
 download the weights with `git lfs install; git clone https://huggingface.co/CompVis/stable-diffusion-v1-4` and instead pass the local path to the cloned folder to `from_pretrained` as shown below.
 - Stable Diffusion can work with a variety of different samplers as is shown below.
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py

@@ -136,7 +136,7 @@ class StableDiffusionPipeline(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.
@@ -224,7 +224,7 @@ class StableDiffusionPipeline(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]
 

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py

@@ -146,7 +146,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
                 deterministic.
             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.
@@ -249,7 +249,7 @@ class StableDiffusionImg2ImgPipeline(DiffusionPipeline):
             # expand the latents if we are doing classifier free guidance
             latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
 
-            # 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):
                 sigma = self.scheduler.sigmas[t_index]
                 # the model input needs to be scaled to match the continuous ODE formulation in K-LMS

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_inpaint.py

@@ -169,7 +169,7 @@ class StableDiffusionInpaintPipeline(DiffusionPipeline):
                 deterministic.
             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.

src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_onnx.py

@@ -107,7 +107,7 @@ class StableDiffusionOnnxPipeline(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]
 

src/diffusers/pipelines/stochastic_karras_ve/pipeline_stochastic_karras_ve.py

@@ -55,7 +55,7 @@ class KarrasVePipeline(DiffusionPipeline):
                 expense of slower inference.
             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 [`~pipeline_utils.ImagePipelineOutput`] instead of a plain tuple.
 

src/diffusers/schedulers/README.md

@@ -1,7 +1,7 @@
 # Schedulers
 
 - Schedulers are the algorithms to use diffusion models in inference as well as for training. They include the noise schedules and define algorithm-specific diffusion steps.
-- Schedulers can be used interchangable between diffusion models in inference to find the preferred trade-off between speed and generation quality.
+- Schedulers can be used interchangeable between diffusion models in inference to find the preferred trade-off between speed and generation quality.
 - Schedulers are available in numpy, but can easily be transformed into PyTorch.
 
 ## API

src/diffusers/schedulers/scheduling_karras_ve.py

@@ -34,7 +34,7 @@ class KarrasVeOutput(BaseOutput):
             Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the
             denoising loop.
         derivative (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):
-            Derivate of predicted original image sample (x_0).
+            Derivative of predicted original image sample (x_0).
     """
 
     prev_sample: torch.FloatTensor

src/diffusers/schedulers/scheduling_sde_vp.py

@@ -14,7 +14,7 @@
 
 # DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
 
-# TODO(Patrick, Anton, Suraj) - make scheduler framework indepedent and clean-up a bit
+# TODO(Patrick, Anton, Suraj) - make scheduler framework independent and clean-up a bit
 
 import numpy as np
 import torch

tests/test_modeling_common.py

@@ -145,7 +145,7 @@ class ModelTesterMixin:
             new_model.to(torch_device)
             new_model.eval()
 
-        # check if all paramters shape are the same
+        # check if all parameters shape are the same
         for param_name in model.state_dict().keys():
             param_1 = model.state_dict()[param_name]
             param_2 = new_model.state_dict()[param_name]

utils/check_inits.py

@@ -288,7 +288,7 @@ def check_submodules():
     if len(module_not_registered) > 0:
         list_of_modules = "\n".join(f"- {module}" for module in module_not_registered)
         raise ValueError(
-            "The following submodules are not properly registed in the main init of Transformers:\n"
+            "The following submodules are not properly registered in the main init of Transformers:\n"
             f"{list_of_modules}\n"
             "Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value."
         )

utils/check_table.py

@@ -53,7 +53,7 @@ def _find_text_in_file(filename, start_prompt, end_prompt):
     return "".join(lines[start_index:end_index]), start_index, end_index, lines
 
 
-# Add here suffixes that are used to identify models, seperated by |
+# Add here suffixes that are used to identify models, separated by |
 ALLOWED_MODEL_SUFFIXES = "Model|Encoder|Decoder|ForConditionalGeneration"
 # Regexes that match TF/Flax/PT model names.
 _re_tf_models = re.compile(r"TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)")
@@ -88,11 +88,11 @@ def _center_text(text, width):
 def get_model_table_from_auto_modules():
     """Generates an up-to-date model table from the content of the auto modules."""
     # Dictionary model names to config.
-    config_maping_names = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
+    config_mapping_names = diffusers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
     model_name_to_config = {
-        name: config_maping_names[code]
+        name: config_mapping_names[code]
         for code, name in diffusers_module.MODEL_NAMES_MAPPING.items()
-        if code in config_maping_names
+        if code in config_mapping_names
     }
     model_name_to_prefix = {name: config.replace("ConfigMixin", "") for name, config in model_name_to_config.items()}
 

utils/check_tf_ops.py

@@ -41,7 +41,7 @@ INTERNAL_OPS = [
 ]
 
 
-def onnx_compliancy(saved_model_path, strict, opset):
+def onnx_compliance(saved_model_path, strict, opset):
     saved_model = SavedModel()
     onnx_ops = []
 
@@ -98,4 +98,4 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     if args.framework == "onnx":
-        onnx_compliancy(args.saved_model_path, args.strict, args.opset)
+        onnx_compliance(args.saved_model_path, args.strict, args.opset)

utils/custom_init_isort.py

@@ -178,7 +178,7 @@ def sort_imports(file, check_only=True):
         code, start_prompt="_import_structure = {", end_prompt="if TYPE_CHECKING:"
     )
 
-    # We ignore block 0 (everything untils start_prompt) and the last block (everything after end_prompt).
+    # We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
     for block_idx in range(1, len(main_blocks) - 1):
         # Check if the block contains some `_import_structure`s thingy to sort.
         block = main_blocks[block_idx]
@@ -202,7 +202,7 @@ def sort_imports(file, check_only=True):
         internal_blocks = split_code_in_indented_blocks(internal_block_code, indent_level=indent)
         # We have two categories of import key: list or _import_structu[key].append/extend
         pattern = _re_direct_key if "_import_structure" in block_lines[0] else _re_indirect_key
-        # Grab the keys, but there is a trap: some lines are empty or jsut comments.
+        # Grab the keys, but there is a trap: some lines are empty or just comments.
         keys = [(pattern.search(b).groups()[0] if pattern.search(b) is not None else None) for b in internal_blocks]
         # We only sort the lines with a key.
         keys_to_sort = [(i, key) for i, key in enumerate(keys) if key is not None]