Compute the Frechet distance between two sets of statistics.

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.

Compute the KL divergence between two gaussians. Shapes are automatically broadcasted, so batches can be compared to scalars, among other

Evaluate pairwise distances between two batches of feature vectors.

Diffuse the data for a given number of diffusion steps. In other words, sample from q(x_t | x_0). :param x_start: the initial

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

Step the EMA model towards the current model.

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

Slow reference implementation of `bias_act()` using standard TensorFlow ops.

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.

Get the prior KL term for the variational lower-bound, measured in bits-per-dim. This term can't be optimized, as it only dep

Copied from: https://github.com/numpy/numpy/blob/fb215c76967739268de71aa4bda55dd1b062bc2e/numpy/lib/format.py#L788-L886 Read from file-like

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()`.

Slow reference implementation of `upfirdn2d()` using standard PyTorch ops.

Decay the learning rate with half-cycle cosine after warmup

Create a beta schedule that discretizes the given alpha_t_bar function, which defines the cumulative product of (1-beta) over time from t = [

Build optimizer, set weight decay of normalization to 0 by default.

Calculation of the statistics used by the FID. Params: -- files : List of image files paths -- model : Instance of inception m

Numpy implementation of the Frechet Distance. The Frechet distance between two multivariate Gaussians X_1 ~ N(mu_1, C_1) and X_2 ~ N(mu_2, C_2

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

Center cropping implementation from ADM. https://github.com/openai/guided-diffusion/blob/8fb3ad9197f16bbc40620447b2742e13458d2831/guided_diff

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

Compute the mean for the previous step, given a function cond_fn that computes the gradient of a conditional log probability with res

Create a logger that writes to a log file and stdout.

Builds a single .npz file from a folder of .png samples.

Sample x_{t-1} from the model using DDIM. Same usage as p_sample().

Use DDIM to sample from the model and yield intermediate samples from each timestep of DDIM. Same usage as p_sample_loop_prog

Compute the log-likelihood of a Gaussian distribution discretizing to a given image. :param x: the target images. It is assumed that this

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