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
class diffusers.RePaintScheduler< source >
( 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 )
int) — number of diffusion steps used to train the model.
float) — the starting
betavalue of inference.
float) — the final
str) — the beta schedule, a mapping from a beta range to a sequence of betas for stepping the model. Choose from
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.
np.ndarray, optional) — option to pass an array of betas directly to the constructor to bypass
str) — options to clip the variance used when adding noise to the denoised sample. Choose from
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
function, such as
num_train_timesteps. They can be accessed via
SchedulerMixin provides general loading and saving functionality via the SchedulerMixin.save_pretrained() and
For more details, see the original paper: https://arxiv.org/pdf/2201.09865.pdf
scale_model_input< source >
timestep: typing.Optional[int] = None
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
step< source >
generator: typing.Optional[torch._C.Generator] = None
return_dict: bool = True
torch.FloatTensor) — direct output from learned diffusion model.
int) — current discrete timestep in the diffusion chain.
torch.FloatTensor) — current instance of sample being created by diffusion process.
torch.FloatTensor) — the original image to inpaint on.
torch.FloatTensor) — the mask where 0.0 values define which part of the original image to inpaint (change).
torch.Generator, optional) — random number generator.
bool) — option for returning tuple rather than DDPMSchedulerOutput class
return_dict is True, otherwise a
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).