Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only

Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only

"Dynamic thresholding: At each sampling step we set s to a certain percentile absolute pixel value in xt0 (the prediction of x_0 at t

Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t =

Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [

Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t =

Compute the mean for the previous step, given a function cond_fn that computes the gradient of a conditional log probability with r

Compute the mean for the previous step, given a function cond_fn that computes the gradient of a conditional log probability with r

Sample x_{t-1} from the model using DDIM. Same usage as p_sample().

Sample x_{t-1} from the model using DDIM. Same usage as p_sample().

Generate samples from the model using DDIM. Same usage as p_sample_loop().

Use DDIM to sample from the model and yield intermediate samples from each timestep of DDIM. Same usage as p_sample_loop_p

Use DDIM to sample from the model and yield intermediate samples from each timestep of DDIM. Same usage as p_sample_loop_p

r""" Disable sliced VAE decoding. If `enable_slicing` was previously invoked, this method will go back to computing decoding in one step.

r""" Disable tiled VAE decoding. If `enable_vae_tiling` was previously invoked, this method will go back to computing decoding in one step.

Compute the log-likelihood of a Gaussian distribution discretizing to a given image. :param x: the target images. It is assumed that this

Compute the log-likelihood of a Gaussian distribution discretizing to a given image. :param x: the target images. It is assumed that this

r""" Enable sliced VAE decoding. When this option is enabled, the VAE will split the input tensor in slices to compute decoding in several steps.

r""" Args: sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor timestep (`torch.FloatTen

r""" Args: sample (`torch.FloatTensor`): (batch, channel, seq_length, height, width) noisy inputs tensor timestep (`to

r""" Args: sample (`torch.FloatTensor`): (batch, channel, height, width) noisy inputs tensor timestep (`torch.FloatTen

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

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

warp an image/tensor (im2) back to im1, according to the optical flow x: [B, C, H, W] (im2) flo: [B, 2, H, W] flow p

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

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

Generate samples from the model. :param model: the model module. :param shape: the shape of the samples, (N, C, H, W).

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

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

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 d

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 d

r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to

r""" Enable sliced attention computation. When this option is enabled, the attention module will split the input tensor in slices, to