DPMSolverMultistepScheduler

class

diffusers.

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py#L74)

(

num_train_timesteps

: int = 1000

beta_start

: float = 0.0001

beta_end

: float = 0.02

beta_schedule

: str = 'linear'

trained_betas

: typing.Union[numpy.ndarray, typing.List[float], NoneType] = None

solver_order

: int = 2

prediction_type

: str = 'epsilon'

thresholding

: bool = False

dynamic_thresholding_ratio

: float = 0.995

sample_max_value

: float = 1.0

algorithm_type

: str = 'dpmsolver++'

solver_type

: str = 'midpoint'

lower_order_final

: bool = True

euler_at_final

: bool = False

use_karras_sigmas

: typing.Optional[bool] = False

use_lu_lambdas

: typing.Optional[bool] = False

lambda_min_clipped

: float = -inf

variance_type

: typing.Optional[str] = None

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 starting beta value of inference.
beta_end ( float , defaults to 0.02) — The final beta 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 from linear , scaled_linear , or squaredcos_cap_v2 .
trained_betas ( np.ndarray , optional ) — Pass an array of betas directly to the constructor to bypass beta_start and beta_end .
solver_order ( int , defaults to 2) — The DPMSolver order which can be 1 or 2 or 3 . It is recommended to use solver_order=2 for guided sampling, and solver_order=3 for unconditional sampling.
prediction_type ( str , defaults to epsilon , optional ) — Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process), sample (directly predicts the noisy sample ) or v_prediction` (see section 2.4 of Imagen Video paper).
thresholding ( bool , defaults to False ) — Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such as Stable Diffusion.
dynamic_thresholding_ratio ( float , defaults to 0.995) — The ratio for the dynamic thresholding method. Valid only when thresholding=True .
sample_max_value ( float , defaults to 1.0) — The threshold value for dynamic thresholding. Valid only when thresholding=True and algorithm_type="dpmsolver++" .
algorithm_type ( str , defaults to dpmsolver++ ) — Algorithm type for the solver; can be dpmsolver , dpmsolver++ , sde-dpmsolver or sde-dpmsolver++ . The dpmsolver type implements the algorithms in the DPMSolver paper, and the dpmsolver++ type implements the algorithms in the DPMSolver++ paper. It is recommended to use dpmsolver++ or sde-dpmsolver++ with solver_order=2 for guided sampling like in Stable Diffusion.
solver_type ( str , defaults to midpoint ) — Solver type for the second-order solver; can be midpoint or heun . The solver type slightly affects the sample quality, especially for a small number of steps. It is recommended to use midpoint solvers.
lower_order_final ( bool , defaults to True ) — Whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. This can stabilize the sampling of DPMSolver for steps < 15, especially for steps <= 10.
euler_at_final ( bool , defaults to False ) — Whether to use Euler’s method in the final step. It is a trade-off between numerical stability and detail richness. This can stabilize the sampling of the SDE variant of DPMSolver for small number of inference steps, but sometimes may result in blurring.
use_karras_sigmas ( bool , optional , defaults to False ) — Whether to use Karras sigmas for step sizes in the noise schedule during the sampling process. If True , the sigmas are determined according to a sequence of noise levels {σi}.
use_lu_lambdas ( bool , optional , defaults to False ) — Whether to use the uniform-logSNR for step sizes proposed by Lu’s DPM-Solver in the noise schedule during the sampling process. If True , the sigmas and time steps are determined according to a sequence of lambda(t) .
lambda_min_clipped ( float , defaults to -inf ) — Clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for the cosine ( squaredcos_cap_v2 ) noise schedule.
variance_type ( str , optional ) — Set to “learned” or “learned_range” for diffusion models that predict variance. If set, the model’s output contains the predicted Gaussian variance.
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. You can use a combination of offset=1 and set_alpha_to_one=False to make the last step use step 0 for the previous alpha product like in Stable Diffusion.

DPMSolverMultistepScheduler is a fast dedicated high-order solver for diffusion ODEs.

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.

convert_model_output

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py#L386)

(

model_output

: FloatTensor

*args

sample

: FloatTensor = None

**kwargs

)

→

export const metadata = 'undefined';

torch.FloatTensor

Parameters

model_output ( torch.FloatTensor ) — The direct output from the learned diffusion model.
sample ( torch.FloatTensor ) — A current instance of a sample created by the diffusion process.

Returns

export const metadata = 'undefined';

torch.FloatTensor

export const metadata = 'undefined';

The converted model output.

Convert the model output to the corresponding type the DPMSolver/DPMSolver++ algorithm needs. DPM-Solver is designed to discretize an integral of the noise prediction model, and DPM-Solver++ is designed to discretize an integral of the data prediction model.

The algorithm and model type are decoupled. You can use either DPMSolver or DPMSolver++ for both noise prediction and data prediction models.

dpm_solver_first_order_update

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py#L485)

(

model_output

: FloatTensor

*args

sample

: FloatTensor = None

noise

: typing.Optional[torch.FloatTensor] = None

**kwargs

)

→

export const metadata = 'undefined';

torch.FloatTensor

Parameters

model_output ( torch.FloatTensor ) — The direct output from the learned diffusion model.
sample ( torch.FloatTensor ) — A current instance of a sample created by the diffusion process.

Returns

export const metadata = 'undefined';

torch.FloatTensor

export const metadata = 'undefined';

The sample tensor at the previous timestep.

One step for the first-order DPMSolver (equivalent to DDIM).

multistep_dpm_solver_second_order_update

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py#L554)

(

model_output_list

: typing.List[torch.FloatTensor]

*args

sample

: FloatTensor = None

noise

: typing.Optional[torch.FloatTensor] = None

**kwargs

)

→

export const metadata = 'undefined';

torch.FloatTensor

Parameters

model_output_list ( List[torch.FloatTensor] ) — The direct outputs from learned diffusion model at current and latter timesteps.
sample ( torch.FloatTensor ) — A current instance of a sample created by the diffusion process.

Returns

export const metadata = 'undefined';

torch.FloatTensor

export const metadata = 'undefined';

The sample tensor at the previous timestep.

One step for the second-order multistep DPMSolver.

multistep_dpm_solver_third_order_update

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py#L677)

(

model_output_list

: typing.List[torch.FloatTensor]

*args

sample

: FloatTensor = None

**kwargs

)

→

export const metadata = 'undefined';

torch.FloatTensor

Parameters

model_output_list ( List[torch.FloatTensor] ) — The direct outputs from learned diffusion model at current and latter timesteps.
sample ( torch.FloatTensor ) — A current instance of a sample created by diffusion process.

Returns

export const metadata = 'undefined';

torch.FloatTensor

export const metadata = 'undefined';

The sample tensor at the previous timestep.

One step for the third-order multistep DPMSolver.

scale_model_input

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py#L857)

(

sample

: FloatTensor

*args

**kwargs

)

→

export const metadata = 'undefined';

torch.FloatTensor

Parameters

sample ( torch.FloatTensor ) — The input sample.

Returns

export const metadata = 'undefined';

torch.FloatTensor

export const metadata = 'undefined';

A scaled input sample.

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.

set_timesteps

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py#L226)

(

num_inference_steps

: int = None

device

: typing.Union[str, torch.device] = None

)

Parameters

num_inference_steps ( int ) — The number of diffusion steps used when generating samples with a pre-trained model.
device ( str or torch.device , optional ) — The device to which the timesteps should be moved to. If None , the timesteps are not moved.

Sets the discrete timesteps used for the diffusion chain (to be run before inference).

step

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_dpmsolver_multistep.py#L781)

(

model_output

: FloatTensor

timestep

: int

sample

: FloatTensor

generator

= None

return_dict

: bool = True

)

→

export const metadata = 'undefined';

SchedulerOutput 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 ) — Whether or not to return a SchedulerOutput or tuple .

Returns

export const metadata = 'undefined';

SchedulerOutput or tuple

export const metadata = 'undefined';

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 sample with the multistep DPMSolver.

SchedulerOutput

class

diffusers.schedulers.scheduling_utils.

SchedulerOutput

[<

source

](https://github.com/huggingface/diffusers/blob/v0.23.0/src/diffusers/schedulers/scheduling_utils.py#L50)

(

prev_sample

: FloatTensor

)

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.

Base class for the output of a scheduler’s step function.

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