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Euler scheduler

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Euler scheduler


Euler scheduler (Algorithm 2) from the paper Elucidating the Design Space of Diffusion-Based Generative Models by Karras et al. (2022). Based on the original k-diffusion implementation by Katherine Crowson. Fast scheduler which often times generates good outputs with 20-30 steps.


class diffusers.EulerDiscreteScheduler

< >

( 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 prediction_type: str = 'epsilon' interpolation_type: str = 'linear' )


  • 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 or scaled_linear.
  • trained_betas (np.ndarray, optional) — option to pass an array of betas directly to the constructor to bypass beta_start, beta_end etc.
  • 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)
  • interpolation_type (str, default "linear", optional) — interpolation type to compute intermediate sigmas for the scheduler denoising steps. Should be one of ["linear", "log_linear"].

Euler scheduler (Algorithm 2) from Karras et al. (2022) https://arxiv.org/abs/2206.00364. . Based on the original k-diffusion implementation by Katherine Crowson: https://github.com/crowsonkb/k-diffusion/blob/481677d114f6ea445aa009cf5bd7a9cdee909e47/k_diffusion/sampling.py#L51

~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.


< >

( sample: FloatTensor timestep: typing.Union[float, torch.FloatTensor] ) torch.FloatTensor


  • sample (torch.FloatTensor) — input sample
  • timestep (float or torch.FloatTensor) — the current timestep in the diffusion chain



scaled input sample

Scales the denoising model input by (sigma**2 + 1) ** 0.5 to match the Euler algorithm.


< >

( num_inference_steps: int device: typing.Union[str, torch.device] = None )


  • 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.


< >

( model_output: FloatTensor timestep: typing.Union[float, torch.FloatTensor] sample: FloatTensor s_churn: float = 0.0 s_tmin: float = 0.0 s_tmax: float = inf s_noise: float = 1.0 generator: typing.Optional[torch._C.Generator] = None return_dict: bool = True ) ~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput or tuple


  • model_output (torch.FloatTensor) — direct output from learned diffusion model.
  • timestep (float) — current timestep in the diffusion chain.
  • sample (torch.FloatTensor) — current instance of sample being created by diffusion process.
  • s_churn (float) —
  • s_tmin (float) —
  • s_tmax (float) —
  • s_noise (float) —
  • generator (torch.Generator, optional) — Random number generator.
  • return_dict (bool) — option for returning tuple rather than EulerDiscreteSchedulerOutput class


~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput or tuple

~schedulers.scheduling_utils.EulerDiscreteSchedulerOutput if return_dict is True, otherwise a tuple. When returning a tuple, the first element is the sample tensor.

Predict the sample at the previous timestep by reversing the SDE. Core function to propagate the diffusion process from the learned model outputs (most often the predicted noise).