Diffusers

You are viewing v0.14.0 version. A newer version v0.32.1 is available.

Join the Hugging Face community

and get access to the augmented documentation experience

Collaborate on models, datasets and Spaces

Faster examples with accelerated inference

Switch between documentation themes

to get started

Inverse Denoising Diffusion Implicit Models (DDIMInverse)

Overview

This scheduler is the inverted scheduler of Denoising Diffusion Implicit Models (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon. The implementation is mostly based on the DDIM inversion definition of Null-text Inversion for Editing Real Images using Guided Diffusion Models

DDIMInverseScheduler

class diffusers.DDIMInverseScheduler

< 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 clip_sample: bool = True set_alpha_to_one: bool = True steps_offset: int = 0 prediction_type: str = 'epsilon' )

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.
clip_sample (bool, default True) — option to clip predicted sample between -1 and 1 for numerical stability.
set_alpha_to_one (bool, default True) — each diffusion step uses the value of alphas product at that step and at the previous one. For the final step there is no previous alpha. When this option is True the previous alpha product is fixed to 1, otherwise it uses the value of alpha at step 0.
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.
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)

DDIMInverseScheduler is the reverse scheduler of DDIMScheduler.

~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/abs/2010.02502

scale_model_input

< source >

( 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.

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.

Sets the discrete timesteps used for the diffusion chain. Supporting function to be run before inference.

←DDIM DDPM→