ScoreSdeVeScheduler
ScoreSdeVeScheduler
is a variance exploding stochastic differential equation (SDE) scheduler. It was introduced in the ScoreBased Generative Modeling through Stochastic Differential Equations paper by Yang Song, Jascha SohlDickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole.
The abstract from the paper is:
Creating noise from data is easy; creating data from noise is generative modeling. We present a stochastic differential equation (SDE) that smoothly transforms a complex data distribution to a known prior distribution by slowly injecting noise, and a corresponding reversetime SDE that transforms the prior distribution back into the data distribution by slowly removing the noise. Crucially, the reversetime SDE depends only on the timedependent gradient field (\aka, score) of the perturbed data distribution. By leveraging advances in scorebased generative modeling, we can accurately estimate these scores with neural networks, and use numerical SDE solvers to generate samples. We show that this framework encapsulates previous approaches in scorebased generative modeling and diffusion probabilistic modeling, allowing for new sampling procedures and new modeling capabilities. In particular, we introduce a predictorcorrector framework to correct errors in the evolution of the discretized reversetime SDE. We also derive an equivalent neural ODE that samples from the same distribution as the SDE, but additionally enables exact likelihood computation, and improved sampling efficiency. In addition, we provide a new way to solve inverse problems with scorebased models, as demonstrated with experiments on classconditional generation, image inpainting, and colorization. Combined with multiple architectural improvements, we achieve recordbreaking performance for unconditional image generation on CIFAR10 with an Inception score of 9.89 and FID of 2.20, a competitive likelihood of 2.99 bits/dim, and demonstrate high fidelity generation of 1024 x 1024 images for the first time from a scorebased generative model.
ScoreSdeVeScheduler
class diffusers.ScoreSdeVeScheduler
< source >( num_train_timesteps: int = 2000 snr: float = 0.15 sigma_min: float = 0.01 sigma_max: float = 1348.0 sampling_eps: float = 1e05 correct_steps: int = 1 )
Parameters

num_train_timesteps (
int
, defaults to 1000) — The number of diffusion steps to train the model. 
snr (
float
, defaults to 0.15) — A coefficient weighting the step from themodel_output
sample (from the network) to the random noise. 
sigma_min (
float
, defaults to 0.01) — The initial noise scale for the sigma sequence in the sampling procedure. The minimum sigma should mirror the distribution of the data. 
sigma_max (
float
, defaults to 1348.0) — The maximum value used for the range of continuous timesteps passed into the model. 
sampling_eps (
float
, defaults to 1e5) — The end value of sampling where timesteps decrease progressively from 1 to epsilon. 
correct_steps (
int
, defaults to 1) — The number of correction steps performed on a produced sample.
ScoreSdeVeScheduler
is a variance exploding stochastic differential equation (SDE) scheduler.
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.
scale_model_input
< source >(
sample: FloatTensor
timestep: typing.Optional[int] = None
)
→
torch.FloatTensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
set_sigmas
< source >( num_inference_steps: int sigma_min: float = None sigma_max: float = None sampling_eps: float = None )
Parameters

num_inference_steps (
int
) — The number of diffusion steps used when generating samples with a pretrained model. 
sigma_min (
float
, optional) — The initial noise scale value (overrides value given during scheduler instantiation). 
sigma_max (
float
, optional) — The final noise scale value (overrides value given during scheduler instantiation). 
sampling_eps (
float
, optional) — The final timestep value (overrides value given during scheduler instantiation).
Sets the noise scales used for the diffusion chain (to be run before inference). The sigmas control the weight
of the drift
and diffusion
components of the sample update.
set_timesteps
< source >( num_inference_steps: int sampling_eps: float = None device: typing.Union[str, torch.device] = None )
Parameters

num_inference_steps (
int
) — The number of diffusion steps used when generating samples with a pretrained model. 
sampling_eps (
float
, optional) — The final timestep value (overrides value given during scheduler instantiation). 
device (
str
ortorch.device
, optional) — The device to which the timesteps should be moved to. IfNone
, the timesteps are not moved.
Sets the continuous timesteps used for the diffusion chain (to be run before inference).
step_correct
< source >(
model_output: FloatTensor
sample: FloatTensor
generator: typing.Optional[torch._C.Generator] = None
return_dict: bool = True
)
→
SdeVeOutput or tuple
Parameters

model_output (
torch.FloatTensor
) — The direct output from learned diffusion model. 
sample (
torch.FloatTensor
) — A current instance of a sample created by the diffusion process. 
generator (
torch.Generator
, optional) — A random number generator. 
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a SdeVeOutput ortuple
.
Returns
SdeVeOutput or tuple
If return_dict is True
, SdeVeOutput is returned, otherwise a tuple
is returned where the first element is the sample tensor.
Correct the predicted sample based on the model_output
of the network. This is often run repeatedly after
making the prediction for the previous timestep.
step_pred
< source >(
model_output: FloatTensor
timestep: int
sample: FloatTensor
generator: typing.Optional[torch._C.Generator] = None
return_dict: bool = True
)
→
SdeVeOutput or tuple
Parameters

model_output (
torch.FloatTensor
) — The direct output from learned diffusion model. 
timestep (
int
) — The current discrete timestep in the diffusion chain. 
sample (
torch.FloatTensor
) — A current instance of a sample created by the diffusion process. 
generator (
torch.Generator
, optional) — A random number generator. 
return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a SdeVeOutput ortuple
.
Returns
SdeVeOutput or tuple
If return_dict is True
, SdeVeOutput 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).
SdeVeOutput
class diffusers.schedulers.scheduling_sde_ve.SdeVeOutput
< source >( prev_sample: FloatTensor prev_sample_mean: FloatTensor )
Parameters

prev_sample (
torch.FloatTensor
of shape(batch_size, num_channels, height, width)
for images) — Computed sample(x_{t1})
of previous timestep.prev_sample
should be used as next model input in the denoising loop. 
prev_sample_mean (
torch.FloatTensor
of shape(batch_size, num_channels, height, width)
for images) — Mean averagedprev_sample
over previous timesteps.
Output class for the scheduler’s step
function output.