  Large text-guided diffusion models, such as DALLE-2, are able to generate
stunning photorealistic images given natural language descriptions. While such
models are highly flexible, they struggle to understand the composition of
certain concepts, such as confusing the attributes of different objects or
relations between objects. In this paper, we propose an alternative structured
approach for compositional generation using diffusion models. An image is
generated by composing a set of diffusion models, with each of them modeling a
certain component of the image. To do this, we interpret diffusion models as
energy-based models in which the data distributions defined by the energy
functions may be explicitly combined. The proposed method can generate scenes
at test time that are substantially more complex than those seen in training,
composing sentence descriptions, object relations, human facial attributes, and
even generalizing to new combinations that are rarely seen in the real world.
We further illustrate how our approach may be used to compose pre-trained
text-guided diffusion models and generate photorealistic images containing all
the details described in the input descriptions, including the binding of
certain object attributes that have been shown difficult for DALLE-2. These
results point to the effectiveness of the proposed method in promoting
structured generalization for visual generation. Project page:
https://energy-based-model.github.io/Compositional-Visual-Generation-with-Composable-Diffusion-Models/
