Add a reference to the name 'Sampler' (#1172)

* Add a reference to the name 'Sampler'

- Facilitate people that are familiar with the name samplers to understand that we call that schedulers
- Better SEO if people are googling for samplers to find our library as well

* Update README.md with a reference to 'Sampler'

README.md

@@ -428,7 +428,7 @@ If you just want to play around with some web demos, you can try out the followi
 <p>
     
 **Schedulers**: Algorithm class for both **inference** and **training**.
-The class provides functionality to compute previous image according to alpha, beta schedule as well as predict noise for training.
+The class provides functionality to compute previous image according to alpha, beta schedule as well as predict noise for training. Also known as **Samplers**.
 *Examples*: [DDPM](https://arxiv.org/abs/2006.11239), [DDIM](https://arxiv.org/abs/2010.02502), [PNDM](https://arxiv.org/abs/2202.09778), [DEIS](https://arxiv.org/abs/2204.13902)
 
 <p align="center">

docs/source/api/schedulers.mdx

@@ -16,7 +16,7 @@ Diffusers contains multiple pre-built schedule functions for the diffusion proce
 
 ## What is a scheduler?
 
-The schedule functions, denoted *Schedulers* in the library take in the output of a trained model, a sample which the diffusion process is iterating on, and a timestep to return a denoised sample.
+The schedule functions, denoted *Schedulers* in the library take in the output of a trained model, a sample which the diffusion process is iterating on, and a timestep to return a denoised sample. That's why schedulers may also be called *Samplers* in other diffusion models implementations.
 
 - Schedulers define the methodology for iteratively adding noise to an image or for updating a sample based on model outputs.
     - adding noise in different manners represent the algorithmic processes to train a diffusion model by adding noise to images.