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.
Dynamic thresholding from Imagen (https://huggingface.co/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.
( 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 use_karras_sigmas: typing.Optional[bool] = False lambda_min_clipped: float = -inf variance_type: typing.Optional[str] = None timestep_spacing: str = 'linspace' steps_offset: int = 0 )
Parameters
int
, defaults to 1000) —
The number of diffusion steps to train the model. float
, defaults to 0.0001) —
The starting beta
value of inference. float
, defaults to 0.02) —
The final beta
value. 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
. np.ndarray
, optional) —
Pass an array of betas directly to the constructor to bypass beta_start
and beta_end
. 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. 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). bool
, defaults to False
) —
Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion. float
, defaults to 0.995) —
The ratio for the dynamic thresholding method. Valid only when thresholding=True
. float
, defaults to 1.0) —
The threshold value for dynamic thresholding. Valid only when thresholding=True
and
algorithm_type="dpmsolver++"
. 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. 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. 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. 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}. 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. 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. 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. 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.
( 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.
( model_output: FloatTensor *args sample: FloatTensor = None noise: typing.Optional[torch.FloatTensor] = None **kwargs ) → torch.FloatTensor
One step for the first-order DPMSolver (equivalent to DDIM).
( model_output_list: typing.List[torch.FloatTensor] *args sample: FloatTensor = None noise: typing.Optional[torch.FloatTensor] = None **kwargs ) → torch.FloatTensor
Parameters
List[torch.FloatTensor]
) —
The direct outputs from learned diffusion model at current and latter timesteps. 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.
( model_output_list: typing.List[torch.FloatTensor] *args sample: FloatTensor = None **kwargs ) → torch.FloatTensor
One step for the third-order multistep DPMSolver.
( sample: FloatTensor *args **kwargs ) → torch.FloatTensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
( num_inference_steps: int = None device: typing.Union[str, torch.device] = None )
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
( model_output: FloatTensor timestep: int sample: FloatTensor generator = None return_dict: bool = True ) → SchedulerOutput or tuple
Parameters
torch.FloatTensor
) —
The direct output from learned diffusion model. int
) —
The current discrete timestep in the diffusion chain. torch.FloatTensor
) —
A current instance of a sample created by the diffusion process. torch.Generator
, optional) —
A random number generator. 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.
( prev_sample: FloatTensor )
Base class for the output of a scheduler’s step
function.