Diffusers

You are viewing main version, which requires installation from source. If you'd like regular pip install, checkout the latest stable version (v0.39.0).

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

DPMSolverSDEScheduler

The DPMSolverSDEScheduler is inspired by the stochastic sampler from the Elucidating the Design Space of Diffusion-Based Generative Models paper, and the scheduler is ported from and created by Katherine Crowson.

DPMSolverSDEScheduler

class diffusers.DPMSolverSDEScheduler

< source >

( *args**kwargs )

SchedulerOutput

class diffusers.schedulers.scheduling_utils.SchedulerOutput

< source >

( prev_sample: Tensor )

Parameters

prev_sample (torch.Tensor of shape (batch_size, num_channels, height, width) for images) — Computed sample (x_{t-1}) of previous timestep. prev_sample should be used as next model input in the denoising loop.

Base class for the output of a scheduler’s step function.

Update on GitHub

←DPMSolverMultistepScheduler DPMSolverSinglestepScheduler→