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

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

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

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

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

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

r"""Test whether the given object or class is persistent, i.e., whether it will save its source code when pickled.

r"""Returns the names of all statistics broadcasted so far that match the regular expression specified at construction time.

r"""Check that the given object is pickleable, raising an exception if it is not. This function is expected to be considerably more efficient

r"""Query the source code of a given Python module.

r"""Get or create a Python module for the given source code.

r"""Synchronize the global cumulative counters across devices and processes. Called internally by `Collector.update()`.

r"""Returns the averages accumulated between the last two calls to `update()` as an `dnnlib.EasyDict`. The contents are as follows:

Finds the python object with the given name and calls it as a function.

Flush, close possible files, and remove stdout/stderr mirroring.

Finds the python object with the given name.

Determine whether the given object is a top-level function, i.e., defined at module scope using 'def'.

Determine whether the given object is a valid URL string.

Train diffusion-based generative model using the techniques described in the paper "Elucidating the Design Space of Diffusion-Based Generative Mod

Convert an image dataset into a dataset archive usable with StyleGAN2 ADA PyTorch. The input dataset format is guessed from the --source argument

Generate random images using the techniques described in the paper "Elucidating the Design Space of Diffusion-Based Generative Models". Examp

Calculate Frechet Inception Distance (FID). Examples: \b # Generate 50000 images and save them as fid-tmp/*/*.png torchrun --standal

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

r"""Class decorator that extends a given class to save its source code when pickled. Example: from torch_utils import persistence

r"""Broadcasts the given set of scalars to all interested instances of `Collector`, across device and process boundaries. This function is ex

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

r"""Convenience getter. `collector[name]` is a synonym for `collector.mean(name)`.