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.

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

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

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

Create a list of timesteps to use from an original diffusion process, given the number of timesteps we want to take from equally-sized portio

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

Update the reweighting using losses from a model. Sub-classes should override this method to update the reweighting using lo

Update the reweighting using losses from a model. Call this method from each rank with a batch of timesteps and the correspo

Get a numpy array of weights, one per diffusion step. The weights needn't be normalized, but must be positive.

directory is expected to contain some folder structure: if some subfolder contains only files, all of these

Convert primitive modules to float16.

Convert primitive modules to float32, undoing convert_module_to_f16().

Convert the torso of the model to float16.

Convert the torso of the model to float32.

Convert the torso of the model to float32.

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

Get the device to use for torch.distributed.

Write all of the diagnostics from the current iteration

Apply the module to `x` given `emb` timestep embeddings.

Apply the block to a Tensor, conditioned on a timestep embedding. :param x: an [N x C x ...] Tensor of features. :param emb:

Apply QKV attention. :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor aft

Apply QKV attention. :param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs. :return: an [N x (H * C) x T] tensor aft