DPMSolverMultistepInverse

DPMSolverMultistepInverse is the inverted 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.

The implementation is mostly based on the DDIM inversion definition of Null-text Inversion for Editing Real Images using Guided Diffusion Models and notebook implementation of the DiffEdit latent inversion from Xiang-cd/DiffEdit-stable-diffusion.

Tips

Dynamic thresholding from Imagen (https://boincai.com/papers/2205.11487) 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.

DPMSolverMultistepInverseScheduler

class diffusers.DPMSolverMultistepInverseScheduler

( num_train_timesteps: int = 1000beta_start: float = 0.0001beta_end: float = 0.02beta_schedule: str = 'linear'trained_betas: typing.Union[numpy.ndarray, typing.List[float], NoneType] = Nonesolver_order: int = 2prediction_type: str = 'epsilon'thresholding: bool = Falsedynamic_thresholding_ratio: float = 0.995sample_max_value: float = 1.0algorithm_type: str = 'dpmsolver++'solver_type: str = 'midpoint'lower_order_final: bool = Trueuse_karras_sigmas: typing.Optional[bool] = Falselambda_min_clipped: float = -infvariance_type: typing.Optional[str] = Nonetimestep_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.
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}.
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.

DPMSolverMultistepInverseScheduler is the reverse scheduler of DPMSolverMultistepScheduler.

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: FloatTensortimestep: intsample: FloatTensor ) → torch.FloatTensor

Parameters

model_output (torch.FloatTensor) — The direct output from the 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.

Returns

torch.FloatTensor

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: FloatTensortimestep: intprev_timestep: intsample: FloatTensornoise: typing.Optional[torch.FloatTensor] = None ) → torch.FloatTensor

Parameters

model_output (torch.FloatTensor) — The direct output from the learned diffusion model.
timestep (int) — The current discrete timestep in the diffusion chain.
prev_timestep (int) — The previous discrete timestep in the diffusion chain.
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 first-order DPMSolver (equivalent to DDIM).

multistep_dpm_solver_second_order_update

( model_output_list: typing.List[torch.FloatTensor]timestep_list: typing.List[int]prev_timestep: intsample: FloatTensornoise: typing.Optional[torch.FloatTensor] = None ) → torch.FloatTensor

Parameters

model_output_list (List[torch.FloatTensor]) — The direct outputs from learned diffusion model at current and latter timesteps.
timestep (int) — The current and latter discrete timestep in the diffusion chain.
prev_timestep (int) — The previous discrete timestep in the diffusion chain.
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

( model_output_list: typing.List[torch.FloatTensor]timestep_list: typing.List[int]prev_timestep: intsample: FloatTensor ) → torch.FloatTensor

Parameters

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

Returns

torch.FloatTensor

The sample tensor at the previous timestep.

One step for the third-order multistep DPMSolver.

scale_model_input

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

Parameters

sample (torch.FloatTensor) — The input sample.

Returns

torch.FloatTensor

A scaled input sample.

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

set_timesteps

( num_inference_steps: int = Nonedevice: 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

( model_output: FloatTensortimestep: intsample: FloatTensorgenerator = Nonereturn_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.
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: 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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