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: Union = 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 startingbeta
value of inference. - beta_end (
float
, defaults to 0.02) — The finalbeta
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 fromlinear
,scaled_linear
, orsquaredcos_cap_v2
. - trained_betas (
np.ndarray
, optional) — Pass an array of betas directly to the constructor to bypassbeta_start
andbeta_end
. - clip_sample (
bool
, defaults toTrue
) — Clip the predicted sample for numerical stability. - clip_sample_range (
float
, defaults to 1.0) — The maximum magnitude for sample clipping. Valid only whenclip_sample=True
. - set_alpha_to_one (
bool
, defaults toTrue
) — 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 isTrue
the previous alpha product is fixed to 0, otherwise it uses the alpha value at stepnum_train_timesteps - 1
. - steps_offset (
int
, defaults to 0) — An offset added to the inference steps, as required by some model families. - prediction_type (
str
, defaults toepsilon
, optional) — Prediction type of the scheduler function; can beepsilon
(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 toFalse
) — 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: Tensor timestep: Optional = None ) → torch.Tensor
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: Union = None )
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
step
< source >( model_output: Tensor timestep: int sample: Tensor return_dict: bool = True ) → ~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput
or tuple
Parameters
- model_output (
torch.Tensor
) — The direct output from learned diffusion model. - timestep (
float
) — The current discrete timestep in the diffusion chain. - sample (
torch.Tensor
) — 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 toFalse
) — IfTrue
, computes “corrected”model_output
from the clipped predicted original sample. Necessary because predicted original sample is clipped to [-1, 1] whenself.config.clip_sample
isTrue
. If no clipping has happened, “corrected”model_output
would coincide with the one provided as input anduse_clipped_model_output
has no effect. - variance_noise (
torch.Tensor
) — Alternative to generating noise withgenerator
by directly providing the noise for the variance itself. Useful for methods such asCycleDiffusion
. - return_dict (
bool
, optional, defaults toTrue
) — Whether or not to return a~schedulers.scheduling_ddim_inverse.DDIMInverseSchedulerOutput
ortuple
.
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).