diffusers/pr_14409/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, and the original implementation can be found at crowsonkb/k-diffusion.

LMSDiscreteScheduler[[diffusers.LMSDiscreteScheduler]]

diffusers.LMSDiscreteScheduler[[diffusers.LMSDiscreteScheduler]]

diffusers.LMSDiscreteScheduler(*args, **kwargs)

Source

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

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

diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput(prev_sample: Tensor, pred_original_sample: typing.Optional[torch.Tensor] = None)

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.

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.