Single image generative models perform synthesis and manipulation tasks by
capturing the distribution of patches within a single image. The classical (pre
Deep Learning) prevailing approaches for these tasks are based on an
optimization process that maximizes patch similarity between the input and
generated output. Recently, however, Single Image GANs were introduced both as
a superior solution for such manipulation tasks, but also for remarkable novel
generative tasks. Despite their impressiveness, single image GANs require long
training time (usually hours) for each image and each task. They often suffer
from artifacts and are prone to optimization issues such as mode collapse. In
this paper, we show that all of these tasks can be performed without any
training, within several seconds, in a unified, surprisingly simple framework.
We revisit and cast the "good-old" patch-based methods into a novel
optimization-free framework. We start with an initial coarse guess, and then
simply refine the details coarse-to-fine using patch-nearest-neighbor search.
This allows generating random novel images better and much faster than GANs. We
further demonstrate a wide range of applications, such as image editing and
reshuffling, retargeting to different sizes, structural analogies, image
collage and a newly introduced task of conditional inpainting. Not only is our
method faster ($\times 10^3$-$\times 10^4$ than a GAN), it produces superior
results (confirmed by quantitative and qualitative evaluation), less artifacts
and more realistic global structure than any of the previous approaches
(whether GAN-based or classical patch-based).

本文提出了一种不需要训练的基于补丁的优化框架，比单个图像 GAN 方法快 1000~10000 倍，并产生比任何以前的方法（无论是基于 GAN 还是基于经典补丁的方法）更优越的结果和更真实的整体结构，应用广泛，如图像编辑和重塑大小。