diffusers/pr_14313/en/api/schedulers/lms_discrete.md

# LMSDiscreteScheduler

`LMSDiscreteScheduler` is a linear multistep scheduler for discrete beta schedules. The scheduler is ported from and created by [Katherine Crowson](https://github.com/crowsonkb/), and the original implementation can be found at [crowsonkb/k-diffusion](https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L181).

## LMSDiscreteScheduler[[diffusers.LMSDiscreteScheduler]]

## LMSDiscreteSchedulerOutput[[diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput]]

- **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.
- **pred_original_sample** (`torch.Tensor` of shape `(batch_size, num_channels, height, width)` for images) --
  The predicted denoised sample `(x_{0})` based on the model output from the current timestep.
  `pred_original_sample` can be used to preview progress or for guidance.

Output class for the scheduler's `step` function output.