UniPC is a training-free framework designed for the fast sampling of diffusion models, which consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders.
For more details about the method, please refer to the paper and the code.
Fast Sampling of Diffusion Models with Exponential Integrator.
( 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 predict_x0: bool = True solver_type: str = 'bh2' lower_order_final: bool = True disable_corrector: typing.List[int] = [] solver_p: SchedulerMixin = 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 UniPC, also the p in UniPC-p; can be any positive integer. Note that the effective order of
accuracy is solver_order + 1 due to the UniC. 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 predict_x0=True 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 predict_x0=True.
bool, default True) —
whether to use the updating algrithm on the predicted x0. See https://arxiv.org/abs/2211.01095 for details
str, default bh2) —
the solver type of UniPC. We recommend use bh1 for unconditional sampling when steps < 10, and use bh2
otherwise.
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.
list, default []) —
decide which step to disable the corrector. For large guidance scale, the misalignment between the
epsilon_theta(x_t, c)and epsilon_theta(x_t^c, c) might influence the convergence. This can be mitigated
by disable the corrector at the first few steps (e.g., disable_corrector=[0])
SchedulerMixin, default None) —
can be any other scheduler. If specified, the algorithm will become solver_p + UniC.
UniPC is a training-free framework designed for the fast sampling of diffusion models, which consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders. UniPC is by desinged model-agnostic, supporting pixel-space/latent-space DPMs on unconditional/conditional sampling. It can also be applied to both noise prediction model and data prediction model. The corrector UniC can be also applied after any off-the-shelf solvers to increase the order of accuracy.
For more details, see the original paper: https://arxiv.org/abs/2302.04867
Currently, we support the multistep UniPC for both noise prediction models and data prediction models. We recommend
to use solver_order=2 for guided sampling, and solver_order=3 for unconditional sampling.
We also support the “dynamic thresholding” method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space
diffusion models, you can set both predict_x0=True 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 PC needs.
(
this_model_output: FloatTensor
this_timestep: int
last_sample: FloatTensor
this_sample: FloatTensor
order: int
)
→
torch.FloatTensor
Parameters
torch.FloatTensor) — the model outputs at x_t
int) — the current timestep t
torch.FloatTensor) — the generated sample before the last predictor: x_{t-1}
torch.FloatTensor) — the generated sample after the last predictor: x_{t}
int) — the p of UniC-p at this step. Note that the effective order of accuracy
should be order + 1
Returns
torch.FloatTensor
the corrected sample tensor at the current timestep.
One step for the UniC (B(h) version).
(
model_output: FloatTensor
prev_timestep: int
sample: FloatTensor
order: int
)
→
torch.FloatTensor
Parameters
torch.FloatTensor) —
direct outputs from learned diffusion model at the current timestep.
int) — previous discrete timestep in the diffusion chain.
torch.FloatTensor) —
current instance of sample being created by diffusion process.
int) — the order of UniP at this step, also the p in UniPC-p.
Returns
torch.FloatTensor
the sample tensor at the previous timestep.
One step for the UniP (B(h) version). Alternatively, self.solver_p is used if is specified.
(
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 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
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 UniPC.