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RePaint scheduler

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RePaint scheduler

Overview

DDPM-based inpainting scheduler for unsupervised inpainting with extreme masks. Intended for use with RePaintPipeline. Based on the paper RePaint: Inpainting using Denoising Diffusion Probabilistic Models and the original implementation by Andreas Lugmayr et al.: https://github.com/andreas128/RePaint

RePaintScheduler

class diffusers.RePaintScheduler

< >

( num_train_timesteps: int = 1000 beta_start: float = 0.0001 beta_end: float = 0.02 beta_schedule: str = 'linear' eta: float = 0.0 trained_betas: typing.Optional[numpy.ndarray] = None clip_sample: 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, squaredcos_cap_v2 or sigmoid.
  • eta (float) — The weight of noise for added noise in a diffusion step. Its value is between 0.0 and 1.0 -0.0 is DDIM and 1.0 is DDPM scheduler respectively.
  • trained_betas (np.ndarray, optional) — option to pass an array of betas directly to the constructor to bypass beta_start, beta_end etc.
  • variance_type (str) — options to clip the variance used when adding noise to the denoised sample. Choose from fixed_small, fixed_small_log, fixed_large, fixed_large_log, learned or learned_range.
  • clip_sample (bool, default True) — option to clip predicted sample between -1 and 1 for numerical stability.

RePaint is a schedule for DDPM inpainting inside a given mask.

~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.

For more details, see the original paper: https://arxiv.org/pdf/2201.09865.pdf

scale_model_input

< >

( sample: FloatTensor timestep: typing.Optional[int] = None ) → torch.FloatTensor

Parameters

  • sample (torch.FloatTensor) — input sample
  • timestep (int, optional) — current timestep

Returns

torch.FloatTensor

scaled input sample

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

step

< >

( model_output: FloatTensor timestep: int sample: FloatTensor original_image: FloatTensor mask: FloatTensor generator: typing.Optional[torch._C.Generator] = None return_dict: bool = True ) → ~schedulers.scheduling_utils.RePaintSchedulerOutput 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.
  • original_image (torch.FloatTensor) — the original image to inpaint on.
  • mask (torch.FloatTensor) — the mask where 0.0 values define which part of the original image to inpaint (change).
  • generator (torch.Generator, optional) — random number generator.
  • return_dict (bool) — option for returning tuple rather than DDPMSchedulerOutput class

Returns

~schedulers.scheduling_utils.RePaintSchedulerOutput or tuple

~schedulers.scheduling_utils.RePaintSchedulerOutput 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).

←PNDM Singlestep DPM-Solver→