Draws samples from an lognormal distribution.

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

Ensures interchangeability with schedulers that need to scale the denoising model input depending on the current timestep. A

Scales the consistency model input by `(sigma**2 + sigma_data**2) ** 0.5`. Args: sample (`torch.FloatTensor`):

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

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

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

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

Draws stratified samples from a uniform distribution.

Update target parameters to be closer to those of source parameters using an exponential moving average. :param target_params: the targe

r""" The call function to the pipeline for generation. Args: image (`PIL.Image.Image` or `List[PIL.Image.Image]` or `torc

Initializes I3D model instance. Args: num_classes: The number of outputs in the logit layer (default 400, which matche

Appends dimensions to the end of a tensor until it has target_dims dimensions.

Creates a config for the diffusers based on the config of the LDM model.