r"""Returns the mean of the scalars that were accumulated for the given statistic between the last two calls to `update()`, or NaN if

Extract values from a 1-D numpy array for a batch of indices. :param arr: the 1-D numpy array. :param timesteps: a tensor of indices into

r"""Copies current values of the internal counters to the user-visible state and resets them for the next round. If `keep_previous=Tr

r"""Returns the standard deviation of the scalars that were accumulated for the given statistic between the last two calls to `update(

Wrapper for the underlying `conv2d()` and `conv_transpose2d()` implementations.

Take the mean over all non-batch dimensions.

Construct a GSPNFusion object for the given pipeline.

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

Build pretrained InceptionV3 Parameters ---------- output_blocks : list of int Indices of blocks to return featur

Apply the model to get p(x_{t-1} | x_t), as well as a prediction of the initial x, x_0. :param model: the model, which takes

Compute the mean and variance of the diffusion posterior: q(x_{t-1} | x_t, x_0)

r"""Returns the raw moments that were accumulated for the given statistic between the last two calls to `update()`, or zero if no scal

Get a term for the variational lower-bound. The resulting units are bits (rather than nats, as one might expect). This allows

Get the default configuration for the GSPNFusion model.

Traverses the object name and returns the last (rightmost) python object.

Mounts the modules in the `modules_dict` to the given `pipeline`.

Rescale `noise_cfg` according to `guidance_rescale`. Based on findings of [Common Diffusion Noise Schedules and Sample Steps are Flawed](http

r"""Pad, upsample, filter, and downsample a batch of 2D images. Performs the following sequence of operations for each channel: 1. Upsample

Write text to stdout (and a file) and optionally flush.

Compute pairwise distances between two batches of feature vectors.

Wraps `torchvision.models.inception_v3` Skips default weight inititialization if supported by torchvision version. See https://github.com/mse

Fast CUDA implementation of `upfirdn2d()` using custom ops.

A fast approximation of the cumulative distribution function of the standard normal.

Compute image features for downstream evals. :param batches: a iterator over NHWC numpy arrays in [0, 255]. :return: a tuple

Compute what the p_mean_variance output would have been, should the model's score function be conditioned by cond_fn. See con

r""" Encodes the prompt into text encoder hidden states. Args: prompt (`str` or `List[str]`, *optional*):

Evaluate precision and recall efficiently. :param features_1: [N1 x D] feature vectors for reference batch. :param radii_1:

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