Schedulers may also be called Samplers in other diffusion models implementation

To learn more about what schedulers are and how they work:

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.
    • for inference, the scheduler defines how to update a sample based on an output from a pretrained model.
  • Schedulers are often defined by a noise schedule and an update rule to solve the differential equation solution.

Available Schedulers

  • DDPMScheduler
  • DDIMScheduler
  • PNDMScheduler
  • DPMSolverMultistepScheduler
  • DPMSolverSinglestepScheduler
  • UniPCMultistepScheduler

To ensure the time cost of image generation and image quality, we only provide better schedulers