This matches the implementation in Denoising Diffusion Probabilistic Models: From Fairseq. Build sinusoidal embeddings. This matches

imgs: list, N images of size WxHxC

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.

Take the mean over all non-batch dimensions.

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

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

source: https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/losses.py#L12 Compute the K

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

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_

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

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

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 dif

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

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

Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the

Given a nested list or dict return the desired value at key expanding callable nodes if necessary and :attr:`expand` is ``True``. The expansion

Importance-sample timesteps for a batch. :param batch_size: the number of timesteps. :param device: the torch device to save