InceptionC block patched for FID computation

The output of [`UNet2DConditionModel`]. Args: sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):

InceptionA block patched for FID computation

this code is borrowed from https://github.com/ajbrock/BigGAN-PyTorch MIT License Copyright (c) 2019 Andy Brock

The final layer of DiT.

Pretrained InceptionV3 network returning feature maps

A generic data loader where the images are arranged in this way: :: root/dog/xxx.png root/dog/xxy.png root/dog/xxz.png

r"""Collects the scalars broadcasted by `report()` and `report0()` and computes their long-term averages (mean and standard deviation) over us

Cosine decay with restarts. This is described in the paper https://arxiv.org/abs/1608.03983. Inspiration from https://github.com/all

Calculate pairwise distances between vectors. Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f25e5ad93

First InceptionE block patched for FID computation

Second InceptionE block patched for FID computation

GRN (Global Response Normalization) layer

Utilities for training and sampling diffusion models. Original ported from this codebase: https://github.com/hojonathanho/diffusion/blob/

Embeds class labels into vector representations. Also handles label dropout for classifier-free guidance.

A helper for comparing manifolds of feature vectors. Adapted from https://github.com/kynkaat/improved-precision-and-recall-metric/blob/f60f2

A diffusion process which can skip steps in a base diffusion process. :param use_timesteps: a collection (sequence or set) of timesteps from

r"""Samples elements randomly from a given list of indices, without replacement. Arguments: indices (sequence): a sequence of indices

Embeds scalar timesteps into vector representations.

A generic data loader where the samples are arranged in this way: :: root/class_x/xxx.ext root/class_x/xxy.ext root/class_x/xx

Convenience class that behaves like a dict but allows access with the attribute syntax.

Redirect stderr to stdout, optionally print stdout to a file, and optionally force flushing on both stdout and the file.

Which type of output the model predicts.

What is used as the model's output variance. The LEARNED_RANGE option has been added to allow the model to predict values between FIXED_S

A distribution over timesteps in the diffusion process, intended to reduce variance of the objective. By default, samplers perform unbias

r""" Pipeline for text-to-image generation using Stable Diffusion XL. This model inherits from [`DiffusionPipeline`]. Check the superclass do

r""" Pipeline for image super-resolution using Stable Diffusion XL. This model inherits from [`DiffusionPipeline`]. Check the superclass docu

A class to read zipped files