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DDIMInverseScheduler

DDIMInverseScheduler is the inverted scheduler from Denoising Diffusion Implicit Models (DDIM) by Jiaming Song, Chenlin Meng and Stefano Ermon. The implementation is mostly based on the DDIM inversion definition from 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' clip_sample_range: float = 1.0 timestep_spacing: str = 'leading' rescale_betas_zero_snr: bool = False **kwargs )

Parameters

num_train_timesteps (int, defaults to 1000) — The number of diffusion steps to train the model.
beta_start (float, defaults to 0.0001) — The starting beta value of inference.
beta_end (float, defaults to 0.02) — The final beta value.
beta_schedule (str, defaults to "linear") — 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) — Pass an array of betas directly to the constructor to bypass beta_start and beta_end.
clip_sample (bool, defaults to True) — Clip the predicted sample for numerical stability.
clip_sample_range (float, defaults to 1.0) — The maximum magnitude for sample clipping. Valid only when clip_sample=True.
set_alpha_to_one (bool, defaults to True) — Each diffusion step uses the alphas product value 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 0, otherwise it uses the alpha value at step num_train_timesteps - 1.
steps_offset (int, defaults to 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 num_train_timesteps - 1 for the previous alpha product.
prediction_type (str, defaults to epsilon, optional) — Prediction type of the scheduler function; can be epsilon (predicts the noise of the diffusion process), sample (directly predicts the noisy sample) or v_prediction` (see section 2.4 of Imagen Video paper).
timestep_spacing (str, defaults to "leading") — The way the timesteps should be scaled. Refer to Table 2 of the Common Diffusion Noise Schedules and Sample Steps are Flawed for more information.
rescale_betas_zero_snr (bool, defaults to False) — Whether to rescale the betas to have zero terminal SNR. This enables the model to generate very bright and dark samples instead of limiting it to samples with medium brightness. Loosely related to --offset_noise.

DDIMInverseScheduler is the reverse scheduler of DDIMScheduler.

This model inherits from SchedulerMixin and ConfigMixin. Check the superclass documentation for the generic methods the library implements for all schedulers such as loading and saving.

scale_model_input

< source >

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

Parameters

sample (torch.FloatTensor) — The input sample.
timestep (int, optional) — The current timestep in the diffusion chain.

Returns

torch.FloatTensor

A 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 (to be run before inference).

step

< source >

( model_output: FloatTensor timestep: int sample: FloatTensor eta: float = 0.0 use_clipped_model_output: bool = False variance_noise: typing.Optional[torch.FloatTensor] = None return_dict: bool = True ) → ~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput or tuple

Parameters

model_output (torch.FloatTensor) — The direct output from learned diffusion model.
timestep (float) — The current discrete timestep in the diffusion chain.
sample (torch.FloatTensor) — A current instance of a sample created by the diffusion process.
eta (float) — The weight of noise for added noise in diffusion step.
use_clipped_model_output (bool, defaults to False) — If True, computes “corrected” model_output from the clipped predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] when self.config.clip_sample is True. If no clipping has happened, “corrected” model_output would coincide with the one provided as input and use_clipped_model_output has no effect.
variance_noise (torch.FloatTensor) — Alternative to generating noise with generator by directly providing the noise for the variance itself. Useful for methods such as CycleDiffusion.
return_dict (bool, optional, defaults to True) — Whether or not to return a ~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput or tuple.

Returns

~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput or tuple

If return_dict is True, ~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput is returned, otherwise a tuple is returned where the first element is the sample tensor.

Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned model outputs (most often the predicted noise).

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