diffusers/pr_13966/en/api/schedulers/dpm_sde.md

# DPMSolverSDEScheduler

The `DPMSolverSDEScheduler` is inspired by the stochastic sampler from the [Elucidating the Design Space of Diffusion-Based Generative Models](https://huggingface.co/papers/2206.00364) paper, and the scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/).

## DPMSolverSDEScheduler[[diffusers.DPMSolverSDEScheduler]]

## SchedulerOutput[[diffusers.schedulers.scheduling_utils.SchedulerOutput]]

- **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.