Diffusers

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

class diffusers.LMSDiscreteScheduler

< source >

( *args**kwargs )

LMSDiscreteSchedulerOutput

class diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput

< source >

( prev_sample: Tensorpred_original_sample: typing.Optional[torch.Tensor] = None )

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.

Update on GitHub