Given an image of a natural scene, we are able to quickly decompose it into a
set of components such as objects, lighting, shadows, and foreground. We can
then envision a scene where we combine certain components with those from other
images, for instance a set of objects from our bedroom and animals from a zoo
under the lighting conditions of a forest, even if we have never encountered
such a scene before. In this paper, we present a method to decompose an image
into such compositional components. Our approach, Decomp Diffusion, is an
unsupervised method which, when given a single image, infers a set of different
components in the image, each represented by a diffusion model. We demonstrate
how components can capture different factors of the scene, ranging from global
scene descriptors like shadows or facial expression to local scene descriptors
like constituent objects. We further illustrate how inferred factors can be
flexibly composed, even with factors inferred from other models, to generate a
variety of scenes sharply different than those seen in training time. Website
and code at this https URL

本文提出了一种图像分解的方法，使用 Diffusion 模型进行无监督训练，从而推断出图像中的各种组成部分，并展示了如何使用这些组成部分灵活地生成不同于训练时的场景。

使用扩散模型进行图像分解

Compositional Image Decomposition with Diffusion Models

Large monolithic generative models trained on massive amounts of data have
become an increasingly dominant approach in AI research. In this paper, we
argue that we should instead construct large generative systems by composing
smaller generative models together. We show how such a compositional generative
approach enables us to learn distributions in a more data-efficient manner,
enabling generalization to parts of the data distribution unseen at training
time. We further show how this enables us to program and construct new
generative models for tasks completely unseen at training. Finally, we show
that in many cases, we can discover separate compositional components from
data.

通过组合较小的生成模型构建大型生成系统，在数据分布中发现并学习更加节约数据的分布，实现对训练中未见的数据分布的泛化，并能够针对完全未见的任务编程和构建新的生成模型。此外，通过数据，我们还能够发现许多情况下的分离组成部分。