Converts a loaded safetensor file that contains Textual Inversion embeds into a dictionary of embed_token: Tensor

Singlestep solver DPM-Solver-2 from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

Initializes Unit3D module.

Estimate a covariance matrix given data. Covariance indicates the level to which two variables vary together. If we examine N-dimensional

Return the data prediction model (with thresholding).

decode frame by frame

Flush written text to both stdout and a file, if open.

Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time. For discrete-time DPMs, we convert `t_continuou

Searches for the underlying module behind the name to some python object. Returns the module and the object name (original name with module part

A piecewise linear function y = f(x), using xp and yp as keypoints. We implement f(x) in a differentiable way (i.e. applicable for autograd).

Multistep DPM-Solver with the order `order` from time `t_prev_list[-1]` to time `t`. Args: x: A pytorch tensor. The init

Return the noise prediction model.

close all handlers to a file >>> release_logger_files() >>> len([1 for lh in logging.getLogger().handlers ... if type(lh) is logg

Saves the Lora from multiple modules in a single safetensor file. modelmap is a dictionary of { "module name": (module, target_r

Singlestep solver DPM-Solver-3 from time `s` to time `t`. Args: x: A pytorch tensor. The initial value at time `s`.

Create sinusoidal timestep embeddings. :param timesteps: a 1-D Tensor of N indices, one per batch element. These may be