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.
( 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
int
, defaults to 1000) —
The number of diffusion steps to train the model. float
, defaults to 0.0001) —
The starting beta
value of inference. float
, defaults to 0.02) —
The final beta
value. 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
. np.ndarray
, optional) —
Pass an array of betas directly to the constructor to bypass beta_start
and beta_end
. bool
, defaults to True
) —
Clip the predicted sample for numerical stability. float
, defaults to 1.0) —
The maximum magnitude for sample clipping. Valid only when clip_sample=True
. 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
. 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. 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). 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. 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.
( sample: FloatTensor timestep: typing.Optional[int] = None ) → torch.FloatTensor
Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep.
( num_inference_steps: int device: typing.Union[str, torch.device] = None )
Sets the discrete timesteps used for the diffusion chain (to be run before inference).
( 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
torch.FloatTensor
) —
The direct output from learned diffusion model. float
) —
The current discrete timestep in the diffusion chain. torch.FloatTensor
) —
A current instance of a sample created by the diffusion process. float
) —
The weight of noise for added noise in diffusion step. 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. torch.FloatTensor
) —
Alternative to generating noise with generator
by directly providing the noise for the variance
itself. Useful for methods such as CycleDiffusion
. 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).