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 >( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = None use_karras_sigmas: Optional = False prediction_type: str = 'epsilon' timestep_spacing: str = 'linspace' steps_offset: int = 0 )
Parameters
- num_train_timesteps (
int
, defaults to 1000) — The number of diffusion steps to train the model. - beta_start (
float
, defaults to 0.0001) — The startingbeta
value of inference. - beta_end (
float
, defaults to 0.02) — The finalbeta
value. - beta_schedule (
str
, defaults to"linear"
) — The beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose fromlinear
orscaled_linear
. - trained_betas (
np.ndarray
, optional) — Pass an array of betas directly to the constructor to bypassbeta_start
andbeta_end
. - use_karras_sigmas (
bool
, optional, defaults toFalse
) — Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. IfTrue
, the sigmas are determined according to a sequence of noise levels {σi}. - prediction_type (
str
, defaults toepsilon
, optional) — Prediction type of the scheduler function; can beepsilon
(predicts the noise of the diffusion process),sample
(directly predicts the noisy sample) or
v_prediction` (see section 2.4 of Imagen Video paper). - timestep_spacing (
str
, defaults to"linspace"
) — The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and Sample Steps are Flawed for more information. - steps_offset (
int
, defaults to 0) — An offset added to the inference steps, as required by some model families.
A linear multistep scheduler for discrete beta schedules.
This model inherits from SchedulerMixin and ConfigMixin. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.
get_lms_coefficient
< source >( order t current_order )
Compute the linear multistep coefficient.
scale_model_input
< source >( sample: FloatTensor timestep: Union ) → torch.FloatTensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
set_begin_index
< source >( begin_index: int = 0 )
Sets the begin index for the scheduler. This function should be run from pipeline before the inference.
set_timesteps
< source >( num_inference_steps: int device: Union = None )
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
< source >( model_output: FloatTensor timestep: Union sample: FloatTensor order: int = 4 return_dict: bool = True ) → SchedulerOutput or tuple
Parameters
- model_output (
torch.FloatTensor
) — The direct output from learned diffusion model. - timestep (
float
ortorch.FloatTensor
) — The current discrete timestep in the diffusion chain. - sample (
torch.FloatTensor
) — A current instance of a sample created by the diffusion process. - order (
int
, defaults to 4) — The order of the linear multistep method. - return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a SchedulerOutput or tuple.
Returns
SchedulerOutput or tuple
If return_dict is True
, SchedulerOutput is returned, otherwise a
tuple is returned where the first element is the sample tensor.
Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise).
LMSDiscreteSchedulerOutput
class diffusers.schedulers.scheduling_lms_discrete.LMSDiscreteSchedulerOutput
< source >( prev_sample: FloatTensor pred_original_sample: Optional = None )
Parameters
- prev_sample (
torch.FloatTensor
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.FloatTensor
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.