diffusers/pr_14381/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]]

#### diffusers.DPMSolverSDEScheduler[[diffusers.DPMSolverSDEScheduler]]

```python
diffusers.DPMSolverSDEScheduler(*args, **kwargs)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14381/src/diffusers/utils/dummy_torch_and_torchsde_objects.py#L20)

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

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

```python
diffusers.schedulers.scheduling_utils.SchedulerOutput(prev_sample: Tensor)
```

[Source](https://github.com/huggingface/diffusers/blob/vr_14381/src/diffusers/schedulers/scheduling_utils.py#L66)

**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.