This scheduler is the inverted scheduler of 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 the ad-hoc notebook implementation for DiffEdit latent inversion here.
( 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 )
Parameters
int
) — number of diffusion steps used to train the model.
float
) — the starting beta
value of inference.
float
) — the final beta
value.
str
) —
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) —
option to pass an array of betas directly to the constructor to bypass beta_start
, beta_end
etc.
int
, default 2
) —
the order of DPM-Solver; can be 1
or 2
or 3
. We recommend to use solver_order=2
for guided
sampling, and solver_order=3
for unconditional sampling.
str
, default epsilon
, optional) —
prediction type of the scheduler function, one of epsilon
(predicting the noise of the diffusion
process), sample
(directly predicting the noisy sample) or
v_prediction` (see section 2.4
https://imagen.research.google/video/paper.pdf)
bool
, default False
) —
whether to use the “dynamic thresholding” method (introduced by Imagen, https://arxiv.org/abs/2205.11487).
For pixel-space diffusion models, you can set both algorithm_type=dpmsolver++
and thresholding=True
to
use the dynamic thresholding. Note that the thresholding method is unsuitable for latent-space diffusion
models (such as stable-diffusion).
float
, default 0.995
) —
the ratio for the dynamic thresholding method. Default is 0.995
, the same as Imagen
(https://arxiv.org/abs/2205.11487).
float
, default 1.0
) —
the threshold value for dynamic thresholding. Valid only when thresholding=True
and
algorithm_type="dpmsolver++
.
str
, default dpmsolver++
) —
the algorithm type for the solver. Either dpmsolver
or dpmsolver++
or sde-dpmsolver
or
sde-dpmsolver++
. The dpmsolver
type implements the algorithms in https://arxiv.org/abs/2206.00927, and
the dpmsolver++
type implements the algorithms in https://arxiv.org/abs/2211.01095. We recommend to use
dpmsolver++
or sde-dpmsolver++
with solver_order=2
for guided sampling (e.g. stable-diffusion).
str
, default midpoint
) —
the solver type for the second-order solver. Either midpoint
or heun
. The solver type slightly affects
the sample quality, especially for small number of steps. We empirically find that midpoint
solvers are
slightly better, so we recommend to use the midpoint
type.
bool
, default True
) —
whether to use lower-order solvers in the final steps. Only valid for < 15 inference steps. We empirically
find this trick can stabilize the sampling of DPM-Solver for steps < 15, especially for steps <= 10.
bool
, optional, defaults to False
) —
This parameter controls whether to use Karras sigmas (Karras et al. (2022) scheme) for step sizes in the
noise schedule during the sampling process. If True, the sigmas will be determined according to a sequence
of noise levels {σi} as defined in Equation (5) of the paper https://arxiv.org/pdf/2206.00364.pdf.
float
, default -inf
) —
the clipping threshold for the minimum value of lambda(t) for numerical stability. This is critical for
cosine (squaredcos_cap_v2) noise schedule.
str
, optional) —
Set to “learned” or “learned_range” for diffusion models that predict variance. For example, OpenAI’s
guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the
Gaussian distribution in the model’s output. DPM-Solver only needs the “mean” output because it is based on
diffusion ODEs. whether the model’s output contains the predicted Gaussian variance. For example, OpenAI’s
guided-diffusion (https://github.com/openai/guided-diffusion) predicts both mean and variance of the
Gaussian distribution in the model’s output. DPM-Solver only needs the “mean” output because it is based on
diffusion ODEs.
DPMSolverMultistepInverseScheduler is the reverse scheduler of DPMSolverMultistepScheduler.
We also support the “dynamic thresholding” method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space
diffusion models, you can set both algorithm_type="dpmsolver++"
and thresholding=True
to use the dynamic
thresholding. Note that the thresholding method is unsuitable for latent-space diffusion models (such as
stable-diffusion).
~ConfigMixin takes care of storing all config attributes that are passed in the scheduler’s __init__
function, such as num_train_timesteps
. They can be accessed via scheduler.config.num_train_timesteps
.
SchedulerMixin provides general loading and saving functionality via the SchedulerMixin.save_pretrained() and
from_pretrained() functions.
(
model_output: FloatTensor
timestep: int
sample: FloatTensor
)
→
torch.FloatTensor
Parameters
torch.FloatTensor
) — direct output from learned diffusion model.
int
) — current discrete timestep in the diffusion chain.
torch.FloatTensor
) —
current instance of sample being created by diffusion process.
Returns
torch.FloatTensor
the converted model output.
Convert the model output to the corresponding type that the algorithm (DPM-Solver / DPM-Solver++) 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. So we need to first convert the model output to the corresponding type to match the algorithm.
Note that the algorithm type and the model type is decoupled. That is to say, we can use either DPM-Solver or DPM-Solver++ for both noise prediction model and data prediction model.
(
model_output: FloatTensor
timestep: int
prev_timestep: int
sample: FloatTensor
noise: typing.Optional[torch.FloatTensor] = None
)
→
torch.FloatTensor
Parameters
torch.FloatTensor
) — direct output from learned diffusion model.
int
) — current discrete timestep in the diffusion chain.
int
) — previous discrete timestep in the diffusion chain.
torch.FloatTensor
) —
current instance of sample being created by diffusion process.
Returns
torch.FloatTensor
the sample tensor at the previous timestep.
One step for the first-order DPM-Solver (equivalent to DDIM).
See https://arxiv.org/abs/2206.00927 for the detailed derivation.
(
model_output_list: typing.List[torch.FloatTensor]
timestep_list: typing.List[int]
prev_timestep: int
sample: FloatTensor
noise: typing.Optional[torch.FloatTensor] = None
)
→
torch.FloatTensor
Parameters
List[torch.FloatTensor]
) —
direct outputs from learned diffusion model at current and latter timesteps.
int
) — current and latter discrete timestep in the diffusion chain.
int
) — previous discrete timestep in the diffusion chain.
torch.FloatTensor
) —
current instance of sample being created by diffusion process.
Returns
torch.FloatTensor
the sample tensor at the previous timestep.
One step for the second-order multistep DPM-Solver.
(
model_output_list: typing.List[torch.FloatTensor]
timestep_list: typing.List[int]
prev_timestep: int
sample: FloatTensor
)
→
torch.FloatTensor
Parameters
List[torch.FloatTensor]
) —
direct outputs from learned diffusion model at current and latter timesteps.
int
) — current and latter discrete timestep in the diffusion chain.
int
) — previous discrete timestep in the diffusion chain.
torch.FloatTensor
) —
current instance of sample being created by diffusion process.
Returns
torch.FloatTensor
the sample tensor at the previous timestep.
One step for the third-order multistep DPM-Solver.
(
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 timesteps used for the diffusion chain. Supporting function to be run before inference.
(
model_output: FloatTensor
timestep: int
sample: FloatTensor
generator = None
return_dict: bool = True
)
→
~scheduling_utils.SchedulerOutput
or tuple
Parameters
torch.FloatTensor
) — direct output from learned diffusion model.
int
) — current discrete timestep in the diffusion chain.
torch.FloatTensor
) —
current instance of sample being created by diffusion process.
bool
) — option for returning tuple rather than SchedulerOutput class
Returns
~scheduling_utils.SchedulerOutput
or tuple
~scheduling_utils.SchedulerOutput
if return_dict
is
True, otherwise a tuple
. When returning a tuple, the first element is the sample tensor.
Step function propagating the sample with the multistep DPM-Solver.