Generative Photomontage
Authors: name: Sean J. Liu, name: Nupur Kumari, name: Ariel Shamir, name: Jun-Yan Zhu

Text-to-image models are powerful tools for image creation. However, the
generation process is akin to a dice roll and makes it difficult to achieve a
single image that captures everything a user wants. In this paper, we propose a
framework for creating the desired image by compositing it from various parts
of generated images, in essence forming a Generative Photomontage. Given a
stack of images generated by ControlNet using the same input condition and
different seeds, we let users select desired parts from the generated results
using a brush stroke interface. We introduce a novel technique that takes in
the user's brush strokes, segments the generated images using a graph-based
optimization in diffusion feature space, and then composites the segmented
regions via a new feature-space blending method. Our method faithfully
preserves the user-selected regions while compositing them harmoniously. We
demonstrate that our flexible framework can be used for many applications,
including generating new appearance combinations, fixing incorrect shapes and
artifacts, and improving prompt alignment. We show compelling results for each
application and demonstrate that our method outperforms existing image blending
methods and various baselines.