**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)
diff --git a/docs/source/api/schedulers.mdx b/docs/source/api/schedulers.mdx index 12575a5ecae2..7ed527bedf3f 100644 --- a/docs/source/api/schedulers.mdx +++ b/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.