DPMSolverMultistepScheduler
DPMSolverMultistep
is a multistep scheduler from DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models by Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.
DPMSolver (and the improved version DPMSolver++) is a fast dedicated high-order solver for diffusion ODEs with convergence order guarantee. Empirically, DPMSolver sampling with only 20 steps can generate high-quality samples, and it can generate quite good samples even in 10 steps.
Tips
It is recommended to set solver_order
to 2 for guide sampling, and solver_order=3
for unconditional sampling.
Dynamic thresholding from Imagen is supported, and for pixel-space
diffusion models, you can set both algorithm_type="dpmsolver++"
and thresholding=True
to use the dynamic
thresholding. This thresholding method is unsuitable for latent-space diffusion models such as
Stable Diffusion.
The SDE variant of DPMSolver and DPM-Solver++ is also supported, but only for the first and second-order solvers. This is a fast SDE solver for the reverse diffusion SDE. It is recommended to use the second-order sde-dpmsolver++
.
DPMSolverMultistepScheduler
class diffusers.DPMSolverMultistepScheduler
< source >( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: Union = 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: Optional = False use_lu_lambdas: Optional = False final_sigmas_type: Optional = 'zero' lambda_min_clipped: float = -inf variance_type: Optional = 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 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
,scaled_linear
, orsquaredcos_cap_v2
. - trained_betas (
np.ndarray
, optional) — Pass an array of betas directly to the constructor to bypassbeta_start
andbeta_end
. - solver_order (
int
, defaults to 2) — The DPMSolver order which can be1
or2
or3
. It is recommended to usesolver_order=2
for guided sampling, andsolver_order=3
for unconditional sampling. - 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). - thresholding (
bool
, defaults toFalse
) — 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 whenthresholding=True
. - sample_max_value (
float
, defaults to 1.0) — The threshold value for dynamic thresholding. Valid only whenthresholding=True
andalgorithm_type="dpmsolver++"
. - algorithm_type (
str
, defaults todpmsolver++
) — Algorithm type for the solver; can bedpmsolver
,dpmsolver++
,sde-dpmsolver
orsde-dpmsolver++
. Thedpmsolver
type implements the algorithms in the DPMSolver paper, and thedpmsolver++
type implements the algorithms in the DPMSolver++ paper. It is recommended to usedpmsolver++
orsde-dpmsolver++
withsolver_order=2
for guided sampling like in Stable Diffusion. - solver_type (
str
, defaults tomidpoint
) — Solver type for the second-order solver; can bemidpoint
orheun
. The solver type slightly affects the sample quality, especially for a small number of steps. It is recommended to usemidpoint
solvers. - lower_order_final (
bool
, defaults toTrue
) — 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 toFalse
) — 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 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}. - use_lu_lambdas (
bool
, optional, defaults toFalse
) — Whether to use the uniform-logSNR for step sizes proposed by Lu’s DPM-Solver in the noise schedule during the sampling process. IfTrue
, the sigmas and time steps are determined according to a sequence oflambda(t)
. - final_sigmas_type (
str
, defaults to"zero"
) — The finalsigma
value for the noise schedule during the sampling process. If"sigma_min"
, the final sigma is the same as the last sigma in the training schedule. Ifzero
, the final sigma is set to 0. - lambda_min_clipped (
float
, defaults to-inf
) — Clipping threshold for the minimum value oflambda(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 ofoffset=1
andset_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 >( model_output: FloatTensor *args sample: FloatTensor = None **kwargs ) → torch.FloatTensor
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 >( model_output: FloatTensor *args sample: FloatTensor = None noise: Optional = None **kwargs ) → torch.FloatTensor
One step for the first-order DPMSolver (equivalent to DDIM).
multistep_dpm_solver_second_order_update
< source >( model_output_list: List *args sample: FloatTensor = None noise: Optional = None **kwargs ) → 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
torch.FloatTensor
The sample tensor at the previous timestep.
One step for the second-order multistep DPMSolver.
multistep_dpm_solver_third_order_update
< source >( model_output_list: List *args sample: FloatTensor = None **kwargs ) → torch.FloatTensor
One step for the third-order multistep DPMSolver.
scale_model_input
< source >( sample: FloatTensor *args **kwargs ) → 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 = None device: Union = None )
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
< source >( model_output: FloatTensor timestep: int sample: FloatTensor generator = None return_dict: bool = True ) → 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 ortuple
.
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 sample with the multistep DPMSolver.
SchedulerOutput
class diffusers.schedulers.scheduling_utils.SchedulerOutput
< source >( prev_sample: FloatTensor )
Base class for the output of a scheduler’s step
function.