Scheduler
Schedulers may also be called Samplers in other diffusion models implementation
To learn more about what schedulers are and how they work: huggingface.co/docs/diffusers/api/schedulers/overview
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