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.

Run sampling.

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. NaNs and Infs are ig

Set requires_grad flag for all parameters in a model.

Scale image to specified dimensions.

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

Drops labels to enable classifier-free guidance.

x: (N, T, patch_size**2 * C) imgs: (N, C, H, W)

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

Estimate the manifold of given feature vectors. :param session: the TensorFlow session. :param row_batch_size: row batch siz

r"""Hook that is called internally by the `pickle` module to unpickle a persistent object.

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

Finds the python class with the given name and constructs it with the given arguments.

Convert an image dataset into archive format for training. Specifying the input images: \b --source path/ Recursive

Takes in a list of tuples of (src, dst) paths and copies files. Will create all necessary directories.

Encode pixel data to VAE latents. Parallelization and Memory Management: Use --gpus to control the number of GPUs used for parallel

Worker function for parallel VAE encoding.

Evaluate if new feature vectors are at the manifold.

Convert the seconds to human readable string with days, hours, minutes and seconds.

Convert the seconds to human readable string with days, hours, minutes and seconds.

t: (B, Hh, N, D_rot), D_rot == self.rot_dim rope_ids: None | (N,) | (B,N), indexing flattened HW positions for the