Build pretrained Inception model for FID computation The Inception model for FID computation uses a different set of weights and has a slight

r""" The [`UNet2DConditionModel`] forward method. Args: sample (`torch.Tensor`): The noisy input tensor with the followin

PyTorch implementation of GateRecurrent2dnoind

Calculates the activations of the pool_3 layer for all images. Params: -- files : List of image files paths -- model : Instan

This is the deprecated API for creating beta schedules. See get_named_beta_schedule() for the new library of schedules.

Get a yacs CfgNode object with default values.

See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 Args: w (`tor

See https://github.com/google-research/vdm/blob/dc27b98a554f65cdc654b800da5aa1846545d41b/model_vdm.py#L298 Args: w (`tor

Finds the python object with the given name.

Checks if a file is an allowed extension. Args: filename (string): path to a file Returns: bool: True if the filename ends wit

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.

judge if this is a zip path

Trains a new GSPN model.

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

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. :param model: the model module. :param shape: the shape of the samples, (N, C, H, W).

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

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

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

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

Set requires_grad flag for all parameters in a model.

Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs

Calls the scheduler's `set_timesteps` method and retrieves timesteps from the scheduler after the call. Handles custom timesteps. Any kwargs

This function disables printing when not in master process

This function disables printing when not in master process

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