Docs fix some typos (#408)

* fix small typos

* capitalize Diffusers

docs/source/api/pipelines/overview.mdx

@@ -82,7 +82,7 @@ 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](.../diffusion_pipeline) or be directly attached to the model and scheduler components of the pipeline. 

docs/source/conceptual/contribution.mdx

@@ -35,7 +35,7 @@ You can contribute in so many ways! Just to name a few:
 
 *All are equally valuable to the community.*
 
-### Browse github issues for suggestions
+### Browse GitHub issues for suggestions
 
 If you need inspiration, you can look out for [issues](https://github.com/huggingface/diffusers/issues) you'd like to tackle to contribute to the library. There are a few filters that can be helpful:
 
@@ -56,7 +56,7 @@ The 🧨 Diffusers library is robust and reliable thanks to the users who notify
 the problems they encounter. So thank you for reporting an issue.
 
 First, we would really appreciate it if you could **make sure the bug was not
-already reported** (use the search bar on Github under Issues).
+already reported** (use the search bar on GitHub under Issues).
 
 ### Do you want to implement a new diffusion pipeline / diffusion model?
 
@@ -127,7 +127,7 @@ Follow these steps to start contributing ([supported Python versions](https://gi
    $ pip install -e ".[dev]"
    ```
 
-   (If diffusers was already installed in the virtual environment, remove
+   (If Diffusers was already installed in the virtual environment, remove
    it with `pip uninstall diffusers` before reinstalling it in editable
    mode with the `-e` flag.)
 
@@ -231,7 +231,7 @@ Follow these steps to start contributing ([supported Python versions](https://gi
      `RUN_SLOW=1 python -m pytest tests/test_my_new_model.py`.
    - If you are adding a new tokenizer, write tests, and make sure
      `RUN_SLOW=1 python -m pytest tests/test_tokenization_{your_model_name}.py` passes.
-   CircleCI does not run the slow tests, but github actions does every night!
+   CircleCI does not run the slow tests, but GitHub actions does every night!
 6. All public methods must have informative docstrings that work nicely with sphinx. See `[pipeline_latent_diffusion.py](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/latent_diffusion/pipeline_latent_diffusion.py)` for an example.
 7. Due to the rapidly growing repository, it is important to make sure that no files that would significantly weigh down the repository are added. This includes images, videos and other non-text files. We prefer to leverage a hf.co hosted `dataset` like
    the ones hosted on [`hf-internal-testing`](https://huggingface.co/hf-internal-testing) in which to place these files and reference or [huggingface/documentation-images](https://huggingface.co/datasets/huggingface/documentation-images).
@@ -274,7 +274,7 @@ $ python -m unittest discover -s examples -t examples -v
 
 To avoid pinging the upstream repository which adds reference notes to each upstream PR and sends unnecessary notifications to the developers involved in these PRs,
 when syncing the main branch of a forked repository, please, follow these steps:
-1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead merge directly into the forked main.
+1. When possible, avoid syncing with the upstream using a branch and PR on the forked repository. Instead, merge directly into the forked main.
 2. If a PR is absolutely necessary, use the following steps after checking out your branch:
 ```
 $ git checkout -b your-branch-for-syncing

docs/source/conceptual/philosophy.mdx

@@ -13,5 +13,5 @@ specific language governing permissions and limitations under the License.
 # Philosophy
 
 - Readability and clarity are 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 use 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. This is one of the guiding goals even if the initial pipelines are devoted to vision tasks.
+- Diffusers is **modality independent** and focuses on providing pretrained models and tools to build systems that generate **continuous outputs**, *e.g.* vision and audio. This is one of the guiding goals even if the initial pipelines are devoted to vision tasks.
 - 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 implementations and can include components of other libraries, such as text encoders. Examples of diffusion pipelines are [Glide](https://github.com/openai/glide-text2im), [Latent Diffusion](https://github.com/CompVis/latent-diffusion) and [Stable Diffusion](https://github.com/compvis/stable-diffusion).

docs/source/index.mdx

@@ -23,7 +23,7 @@ specific language governing permissions and limitations under the License.
 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 [**Using Diffusers**](./using-diffusers/conditional_image_generation)) or have a look at [**Pipelines**](#pipelines) to get an overview of all supported pipelines and their corresponding papers.
-- Various noise schedulers that can be used interchangeably for the prefered speed vs. quality trade-off in inference. For more information see [**Schedulers**](./api/schedulers).
+- Various noise schedulers that can be used interchangeably for the preferred speed vs. quality trade-off in inference. For more information see [**Schedulers**](./api/schedulers).
 - Multiple types of models, such as UNet, can be used as building blocks in an end-to-end diffusion system. See [**Models**](./api/models) for more details 
 - Training examples to show how to train the most popular diffusion model tasks. For more information see [**Training**](./training/overview).