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DEIS

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DEIS

Fast Sampling of Diffusion Models with Exponential Integrator.

Overview

Original paper can be found here. The original implementation can be found here.

DEISMultistepScheduler

class diffusers.DEISMultistepScheduler

< >

( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' trained_betas: typing.Optional[numpy.ndarray] = 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 = 'deis' solver_type: str = 'logrho' lower_order_final: bool = True )

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 DEIS; can be 1 or 2 or 3. We recommend to use solver_order=2 for guided sampling, and solver_order=3 for unconditional sampling.
  • prediction_type (str, default epsilon) — indicates whether the model predicts the noise (epsilon), or the data / x0. One of epsilon, sample, or v-prediction.
  • thresholding (bool, default False) — whether to use the “dynamic thresholding” method (introduced by Imagen, https://arxiv.org/abs/2205.11487). 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 woks when thresholding=True
  • algorithm_type (str, default deis) — the algorithm type for the solver. current we support multistep deis, we will add other variants of DEIS in the future
  • 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 DEIS for steps < 15, especially for steps <= 10.

DEIS (https://arxiv.org/abs/2204.13902) is a fast high order solver for diffusion ODEs. We slightly modify the polynomial fitting formula in log-rho space instead of the original linear t space in DEIS paper. The modification enjoys closed-form coefficients for exponential multistep update instead of replying on the numerical solver. More variants of DEIS can be found in https://github.com/qsh-zh/deis.

Currently, we support the log-rho multistep DEIS. We recommend to use solver_order=2 / 3 while solver_order=1 reduces to DDIM.

We also support the “dynamic thresholding” method in Imagen (https://arxiv.org/abs/2205.11487). For pixel-space diffusion models, you can set thresholding=True to use the dynamic thresholding.

~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

< >

( 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 DEIS needs.

deis_first_order_update

< >

( model_output: FloatTensor timestep: int prev_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.
  • prev_timestep (int) — previous discrete timestep in the diffusion chain.
  • sample (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 DEIS (equivalent to DDIM).

multistep_deis_second_order_update

< >

( model_output_list: typing.List[torch.FloatTensor] timestep_list: typing.List[int] prev_timestep: int sample: FloatTensor ) → torch.FloatTensor

Parameters

  • model_output_list (List[torch.FloatTensor]) — direct outputs from learned diffusion model at current and latter timesteps.
  • timestep (int) — current and latter discrete timestep in the diffusion chain.
  • prev_timestep (int) — previous discrete timestep in the diffusion chain.
  • sample (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 DEIS.

multistep_deis_third_order_update

< >

( model_output_list: typing.List[torch.FloatTensor] timestep_list: typing.List[int] prev_timestep: int sample: FloatTensor ) → torch.FloatTensor

Parameters

  • model_output_list (List[torch.FloatTensor]) — direct outputs from learned diffusion model at current and latter timesteps.
  • timestep (int) — current and latter discrete timestep in the diffusion chain.
  • prev_timestep (int) — previous discrete timestep in the diffusion chain.
  • sample (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 DEIS.

scale_model_input

< >

( 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

< >

( 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

< >

( 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 DEIS.

←DDPM DPM Discrete Scheduler→