CogVideoXDPMScheduler
is based on DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps and DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models, specifically for CogVideoX models.
( num_train_timesteps: int = 1000 beta_start: float = 0.00085 beta_end: float = 0.012 beta_schedule: str = 'scaled_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 sample_max_value: float = 1.0 timestep_spacing: str = 'leading' rescale_betas_zero_snr: bool = False snr_shift_scale: float = 3.0 )
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 1
,
otherwise it uses the alpha value at step 0. int
, defaults to 0) —
An offset added to the inference steps, as required by some model families. 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). bool
, defaults to False
) —
Whether to use the “dynamic thresholding” method. This is unsuitable for latent-space diffusion models such
as Stable Diffusion. float
, defaults to 0.995) —
The ratio for the dynamic thresholding method. Valid only when thresholding=True
. float
, defaults to 1.0) —
The threshold value for dynamic thresholding. Valid only when thresholding=True
. 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
. DDIMScheduler
extends the denoising procedure introduced in denoising diffusion probabilistic models (DDPMs) with
non-Markovian guidance.
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: Tensor timestep: typing.Optional[int] = None ) → torch.Tensor
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: Tensor old_pred_original_sample: Tensor timestep: int timestep_back: int sample: Tensor eta: float = 0.0 use_clipped_model_output: bool = False generator = None variance_noise: typing.Optional[torch.Tensor] = None return_dict: bool = False ) → DDIMSchedulerOutput or tuple
Parameters
torch.Tensor
) —
The direct output from learned diffusion model. float
) —
The current discrete timestep in the diffusion chain. torch.Tensor
) —
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.Generator
, optional) —
A random number generator. torch.Tensor
) —
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 DDIMSchedulerOutput or tuple
. Returns
DDIMSchedulerOutput or tuple
If return_dict is True
, DDIMSchedulerOutput 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).