diffusers/pr_14421/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 paper, and the scheduler is ported from and created by Katherine Crowson.

DPMSolverSDEScheduler[[diffusers.DPMSolverSDEScheduler]]

diffusers.DPMSolverSDEScheduler[[diffusers.DPMSolverSDEScheduler]]

diffusers.DPMSolverSDEScheduler(*args, **kwargs)

Source

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

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

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

Source

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.