UniPCMultistepScheduler
Last updated
Last updated
UniPCMultistepScheduler is a training-free framework designed for fast sampling of diffusion models. It was introduced in by Wenliang Zhao, Lujia Bai, Yongming Rao, Jie Zhou, Jiwen Lu.
It consists of a corrector (UniC) and a predictor (UniP) that share a unified analytical form and support arbitrary orders. UniPC is by design model-agnostic, supporting pixel-space/latent-space DPMs on unconditional/conditional sampling. It can also be applied to both noise prediction and data prediction models. The corrector UniC can be also applied after any off-the-shelf solvers to increase the order of accuracy.
The abstract from the paper is:
Diffusion probabilistic models (DPMs) have demonstrated a very promising ability in high-resolution image synthesis. However, sampling from a pre-trained DPM usually requires hundreds of model evaluations, which is computationally expensive. Despite recent progress in designing high-order solvers for DPMs, there still exists room for further speedup, especially in extremely few steps (e.g., 5~10 steps). Inspired by the predictor-corrector for ODE solvers, we develop a unified corrector (UniC) that can be applied after any existing DPM sampler to increase the order of accuracy without extra model evaluations, and derive a unified predictor (UniP) that supports arbitrary order as a byproduct. Combining UniP and UniC, we propose a unified predictor-corrector framework called UniPC for the fast sampling of DPMs, which has a unified analytical form for any order and can significantly improve the sampling quality over previous methods. We evaluate our methods through extensive experiments including both unconditional and conditional sampling using pixel-space and latent-space DPMs. Our UniPC can achieve 3.87 FID on CIFAR10 (unconditional) and 7.51 FID on ImageNet 256times256 (conditional) with only 10 function evaluations. Code is available at .
The original codebase can be found at .
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 predict_x0=True and thresholding=True to use dynamic thresholding. This thresholding method is unsuitable for latent-space diffusion models such as Stable Diffusion.
( 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 = 1.0predict_x0: bool = Truesolver_type: str = 'bh2'lower_order_final: bool = Truedisable_corrector: typing.List[int] = []solver_p: SchedulerMixin = Noneuse_karras_sigmas: typing.Optional[bool] = Falsetimestep_spacing: str = 'linspace'steps_offset: int = 0 )
Parameters
num_train_timesteps (int, defaults to 1000) β The number of diffusion steps to train the model.
beta_start (float, defaults to 0.0001) β The starting beta value of inference.
beta_end (float, defaults to 0.02) β The final beta value.
beta_schedule (str, defaults to "linear") β 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) β Pass an array of betas directly to the constructor to bypass beta_start and beta_end.
solver_order (int, default 2) β The UniPC order which can be any positive integer. The effective order of accuracy is solver_order + 1 due to the UniC. 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) β 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 paper).
thresholding (bool, defaults to False) β Whether to use the βdynamic thresholdingβ method. This is unsuitable for latent-space diffusion models such as Stable Diffusion.
dynamic_thresholding_ratio (float, defaults to 0.995) β The ratio for the dynamic thresholding method. Valid only when thresholding=True.
sample_max_value (float, defaults to 1.0) β The threshold value for dynamic thresholding. Valid only when thresholding=True and predict_x0=True.
predict_x0 (bool, defaults to True) β Whether to use the updating algorithm on the predicted x0.
solver_type (str, default bh2) β Solver type for UniPC. It is recommended to use bh1 for unconditional sampling when steps < 10, and bh2 otherwise.
lower_order_final (bool, default True) β 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.
disable_corrector (list, default []) β Decides which step to disable the corrector to mitigate the misalignment between epsilon_theta(x_t, c) and epsilon_theta(x_t^c, c) which can influence convergence for a large guidance scale. Corrector is usually disabled during the first few steps.
solver_p (SchedulerMixin, default None) β Any other scheduler that if specified, the algorithm becomes solver_p + UniC.
use_karras_sigmas (bool, optional, defaults to False) β 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 {Οi}.
timestep_spacing (str, defaults to "linspace") β The way the timesteps should be scaled. Refer to Table 2 of the for more information.
steps_offset (int, defaults to 0) β 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.
UniPCMultistepScheduler is a training-free framework designed for the fast sampling of diffusion models.
convert_model_output
( model_output: FloatTensortimestep: intsample: FloatTensor ) β torch.FloatTensor
Parameters
model_output (torch.FloatTensor) β The direct output from the learned diffusion model.
timestep (int) β The current discrete timestep in the diffusion chain.
sample (torch.FloatTensor) β 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 UniPC algorithm needs.
multistep_uni_c_bh_update
( this_model_output: FloatTensorthis_timestep: intlast_sample: FloatTensorthis_sample: FloatTensororder: int ) β torch.FloatTensor
Parameters
this_model_output (torch.FloatTensor) β The model outputs at x_t.
this_timestep (int) β The current timestep t.
last_sample (torch.FloatTensor) β The generated sample before the last predictor x_{t-1}.
this_sample (torch.FloatTensor) β The generated sample after the last predictor x_{t}.
order (int) β The p of UniC-p at this step. 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).
multistep_uni_p_bh_update
( model_output: FloatTensorprev_timestep: intsample: FloatTensororder: int ) β torch.FloatTensor
Parameters
model_output (torch.FloatTensor) β The direct output from the learned diffusion model at the current timestep.
prev_timestep (int) β The previous discrete timestep in the diffusion chain.
sample (torch.FloatTensor) β A current instance of a sample created by the diffusion process.
order (int) β The order of UniP at this timestep (corresponds to 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.
scale_model_input
( sample: FloatTensor*args**kwargs ) β torch.FloatTensor
Parameters
sample (torch.FloatTensor) β 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: intdevice: typing.Union[str, torch.device] = None )
Parameters
num_inference_steps (int) β The number of diffusion steps used when generating samples with a pre-trained model.
device (str or torch.device, optional) β 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
Parameters
model_output (torch.FloatTensor) β The direct output from learned diffusion model.
timestep (int) β The current discrete timestep in the diffusion chain.
sample (torch.FloatTensor) β A current instance of a sample created by the diffusion process.
Returns
Predict the sample from the previous timestep by reversing the SDE. This function propagates the sample with the multistep UniPC.
( prev_sample: FloatTensor )
Parameters
prev_sample (torch.FloatTensor of shape (batch_size, num_channels, height, width) for images) β 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 schedulerβs step function.
This model inherits from and . Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.
( model_output: FloatTensortimestep: intsample: FloatTensorreturn_dict: bool = True ) β or tuple
return_dict (bool) β Whether or not to return a or tuple.
or tuple
If return_dict is True, is returned, otherwise a tuple is returned where the first element is the sample tensor.