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DPMSolverMultistepScheduler

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DPMSolverMultistepScheduler

DPMSolverMultistepScheduler 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

< >

( 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_exponential_sigmas: typing.Optional[bool] = False use_beta_sigmas: typing.Optional[bool] = False use_lu_lambdas: typing.Optional[bool] = False use_flow_sigmas: typing.Optional[bool] = False flow_shift: typing.Optional[float] = 1.0 final_sigmas_type: typing.Optional[str] = 'zero' lambda_min_clipped: float = -inf variance_type: typing.Optional[str] = None timestep_spacing: str = 'linspace' steps_offset: int = 0 rescale_betas_zero_snr: bool = False )

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_exponential_sigmas (bool, optional, defaults to False) — Whether to use exponential sigmas for step sizes in the noise schedule during the sampling process.
  • use_beta_sigmas (bool, optional, defaults to False) — Whether to use beta sigmas for step sizes in the noise schedule during the sampling process. Refer to Beta Sampling is All You Need for more information.
  • 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).
  • final_sigmas_type (str, defaults to "zero") — The final sigma 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. If zero, the final sigma is set to 0.
  • 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, as required by some model families.
  • rescale_betas_zero_snr (bool, defaults to False) — Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and dark samples instead of limiting it to samples with medium brightness. Loosely related to --offset_noise.

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

< >

( model_output: Tensor *args sample: Tensor = None **kwargs ) → torch.Tensor

Parameters

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

Returns

torch.Tensor

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

< >

( model_output: Tensor *args sample: Tensor = None noise: typing.Optional[torch.Tensor] = None **kwargs ) → torch.Tensor

Parameters

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

Returns

torch.Tensor

The sample tensor at the previous timestep.

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

multistep_dpm_solver_second_order_update

< >

( model_output_list: typing.List[torch.Tensor] *args sample: Tensor = None noise: typing.Optional[torch.Tensor] = None **kwargs ) → torch.Tensor

Parameters

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

Returns

torch.Tensor

The sample tensor at the previous timestep.

One step for the second-order multistep DPMSolver.

multistep_dpm_solver_third_order_update

< >

( model_output_list: typing.List[torch.Tensor] *args sample: Tensor = None noise: typing.Optional[torch.Tensor] = None **kwargs ) → torch.Tensor

Parameters

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

Returns

torch.Tensor

The sample tensor at the previous timestep.

One step for the third-order multistep DPMSolver.

scale_model_input

< >

( sample: Tensor *args **kwargs ) → torch.Tensor

Parameters

  • sample (torch.Tensor) — The input sample.

Returns

torch.Tensor

A scaled input sample.

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

set_begin_index

< >

( begin_index: int = 0 )

Parameters

  • begin_index (int) — The begin index for the scheduler.

Sets the begin index for the scheduler. This function should be run from pipeline before the inference.

set_timesteps

< >

( num_inference_steps: int = None device: typing.Union[str, torch.device] = None timesteps: typing.Optional[typing.List[int]] = 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.
  • timesteps (List[int], optional) — Custom timesteps used to support arbitrary timesteps schedule. If None, timesteps will be generated based on the timestep_spacing attribute. If timesteps is passed, num_inference_steps and sigmas must be None, and timestep_spacing attribute will be ignored.

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

step

< >

( model_output: Tensor timestep: typing.Union[int, torch.Tensor] sample: Tensor generator = None variance_noise: typing.Optional[torch.Tensor] = None return_dict: bool = True ) → SchedulerOutput or tuple

Parameters

  • model_output (torch.Tensor) — The direct output from learned diffusion model.
  • timestep (int) — The current discrete timestep in the diffusion chain.
  • sample (torch.Tensor) — A current instance of a sample created by the diffusion process.
  • generator (torch.Generator, optional) — A random number generator.
  • variance_noise (torch.Tensor) — Alternative to generating noise with generator by directly providing the noise for the variance itself. Useful for methods such as LEdits++.
  • return_dict (bool) — 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 sample with the multistep DPMSolver.

SchedulerOutput

class diffusers.schedulers.scheduling_utils.SchedulerOutput

< >

( prev_sample: Tensor )

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.

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

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