This is the deprecated API for creating beta schedules. See get_named_beta_schedule() for the new library of schedules.

Calculate the integral of exp(-x) * x^order dx from interval_start to interval_end

Calculate the integral of exp(-x) * x^order dx from interval_start to interval_end For calculating the coefficient of gradient terms

Calculate the integral of exp(x(1+tau^2)) * x^order dx from interval_start to interval_end

Calculate the integral of exp(x(1+tau^2)) * x^order dx from interval_start to interval_end For calculating the coefficient of gradien

Create a schedule with a cosine annealing lr followed by a constant lr. Args: optimizer ([`~torch.optim.Optimizer`]): Th

Get the order of each step for sampling by the singlestep DPM-Solver. We combine both DPM-Solver-1,2,3 to use all the function evalu

Calculate the coefficient of lagrange polynomial

Calculate the coefficient of lagrange polynomial For lagrange interpolation

The noise predicition model function that is used for DPM-Solver.

The noise predicition model function that is used for DPM-Solver.

Print only from rank 0.

Multistep solver DPM-Solver-2 from time `t_prev_list[-1]` to time `t`. Args: x: A pytorch tensor. The initial value at t

Multistep solver DPM-Solver-3 from time `t_prev_list[-1]` to time `t`. Args: x: A pytorch tensor. The initial value at t

For some beta schedules such as cosine schedule, the log-SNR has numerical isssues. We clip the log-SNR near t=T within -5.1 to ensur

Sample x_{t-1} from the model at the given timestep. :param model: the model to sample from. :param x: the current tensor at

Generate samples from the model and yield intermediate samples from each timestep of diffusion. Arguments are the same as p_s

Get the distribution q(x_t | x_0). :param x_start: the [N x C x ...] tensor of noiseless inputs. :param t: the number of diff

Rescales betas to have zero terminal SNR Based on https://arxiv.org/pdf/2305.08891.pdf (Algorithm 1) Args: betas (`torch.FloatTensor`

Compute the sample at time `t_end` by DPM-Solver, given the initial `x` at time `t_start`. =========================================

For the PC-mode, please refer to the wiki page https://en.wikipedia.org/wiki/Predictor%E2%80%93corrector_method#PEC_mode_and_PECE_mo

For the PC-mode, please refer to the wiki page https://en.wikipedia.org/wiki/Predictor%E2%80%93corrector_method#PEC_mode_and_PECE_mo

Sets the discrete timesteps used for the diffusion chain (to be run before inference). Args: num_inference_steps (`int`):

Singlestep DPM-Solver with the order `order` from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at

Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portio

Predict the sample from the previous timestep by reversing the SDE. This function propagates the diffusion process from the learned m

One step for the SA-Predictor. Args: model_output (`torch.FloatTensor`): The direct output from the lear

One step for the SA-Corrector. Args: this_model_output (`torch.FloatTensor`): The model outputs at `x_t`