#
UniPC

##
Overview

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.

##
UniPCMultistepScheduler

###
class diffusers.UniPCMultistepScheduler

< source >

( 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 use_karras_sigmas: typing.Optional[bool] = False timestep_spacing: str = 'linspace' steps_offset: int = 0 )

Parameters

- num_train_timesteps (`int`) — number of diffusion steps used to train the model.
- beta_start (`float`) — the starting `beta` value of inference.
- beta_end (`float`) — the final `beta` value.
- beta_schedule (`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`.
- trained_betas (`np.ndarray`, optional) — option to pass an array of betas directly to the constructor to bypass `beta_start`, `beta_end` etc.
- solver_order (`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.
- prediction_type (`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)
- thresholding (`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).
- dynamic_thresholding_ratio (`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).
- sample_max_value (`float`, default `1.0`) — the threshold value for dynamic thresholding. Valid only when `thresholding=True` and `predict_x0=True`.
- predict_x0 (`bool`, default `True`) — whether to use the updating algrithm on the predicted x0. See https://arxiv.org/abs/2211.01095 for details
- solver_type (`str`, default `bh2`) — the solver type of UniPC. We recommend use `bh1` for unconditional sampling when steps < 10, and use `bh2` otherwise.
- lower_order_final (`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.
- disable_corrector (`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])
- solver_p (`SchedulerMixin`, default `None`) — can be any other scheduler. If specified, the algorithm will become solver_p + UniC.
- use_karras_sigmas (`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.
- timestep_spacing (`str`, default `"linspace"`) — The way the timesteps should be scaled. Refer to Table 2. of Common Diffusion Noise Schedules and Sample Steps are Flawed for more information.
- steps_offset (`int`, default `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, as done in stable diffusion.

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.

####
convert_model_output

< source >

( model_output: FloatTensor timestep: int sample: FloatTensor ) → `torch.FloatTensor`

Parameters

- model_output (`torch.FloatTensor`) — direct output from learned diffusion model.
- timestep (`int`) — current discrete timestep in the diffusion chain.
- sample (`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.

####
multistep_uni_c_bh_update

< source >

( this_model_output: FloatTensor this_timestep: int last_sample: FloatTensor this_sample: FloatTensor order: int ) → `torch.FloatTensor`

Parameters

- this_model_output (`torch.FloatTensor`) — the model outputs at `x_t`
- this_timestep (`int`) — the current timestep `t`
- last_sample (`torch.FloatTensor`) — the generated sample before the last predictor: `x_{t-1}`
- this_sample (`torch.FloatTensor`) — the generated sample after the last predictor: `x_{t}`
- order (`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).

####
multistep_uni_p_bh_update

< source >

( model_output: FloatTensor prev_timestep: int sample: FloatTensor order: int ) → `torch.FloatTensor`

Parameters

- model_output (`torch.FloatTensor`) — direct outputs from learned diffusion model at the current timestep.
- prev_timestep (`int`) — previous discrete timestep in the diffusion chain.
- sample (`torch.FloatTensor`) — current instance of sample being created by diffusion process.
- order (`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.

####
scale_model_input

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

Parameters

- sample (`torch.FloatTensor`) — input sample

Returns

`torch.FloatTensor`

scaled input sample

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

####
set_timesteps

< source >

( num_inference_steps: int device: 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 timesteps used for the diffusion chain. Supporting function to be run before inference.

####
step

< source >

( model_output: FloatTensor timestep: int sample: FloatTensor return_dict: bool = True ) → `~scheduling_utils.SchedulerOutput` or `tuple`

Parameters

- model_output (`torch.FloatTensor`) — direct output from learned diffusion model.
- timestep (`int`) — current discrete timestep in the diffusion chain.
- sample (`torch.FloatTensor`) — current instance of sample being created by diffusion process.
- return_dict (`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.