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

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

Create a ScheduleSampler from a library of pre-defined samplers. :param name: the name of the sampler. :param diffusion: the diffusion o

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

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

Convert Euler angles to quaternions.

Solves for the depth offset of the model to approx. orth with persp camera.

Initialize the camera translation via triangle similarity, by using the torso joints . :param model_joints: SMPL model with pre joints

Create graph operation for negative log-likelihood calculation

Log a value of some diagnostic Call this once for each diagnostic quantity, each iteration If called many times, last value will be used.

Copy the master parameter data back into the model parameters.

Convert rotations given as rotation matrices to Euler angles in radians. Args: matrix: Rotation matrices as tensor of shape (..., 3,

Convert rotations given as rotation matrices to quaternions. Args: matrix: Rotation matrices as tensor of shape (..., 3, 3). Re

Copy the gradients from the model parameters into the master parameters from make_master_params().

Utility function to convert model output to a representation used by HumanIK skeletons in Maya and Motion Builder by converting joint positio

Copied from: https://github.com/openai/baselines/blob/ea25b9e8b234e6ee1bca43083f8f3cf974143998/baselines/common/mpi_util.py#L110 Perform a we

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

Parse filenames of the form path/to/modelNNNNNN.pt, where NNNNNN is the checkpoint's number of steps.

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

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