  Text-to-image model personalization aims to introduce a user-provided concept
to the model, allowing its synthesis in diverse contexts. However, current
methods primarily focus on the case of learning a single concept from multiple
images with variations in backgrounds and poses, and struggle when adapted to a
different scenario. In this work, we introduce the task of textual scene
decomposition: given a single image of a scene that may contain several
concepts, we aim to extract a distinct text token for each concept, enabling
fine-grained control over the generated scenes. To this end, we propose
augmenting the input image with masks that indicate the presence of target
concepts. These masks can be provided by the user or generated automatically by
a pre-trained segmentation model. We then present a novel two-phase
customization process that optimizes a set of dedicated textual embeddings
(handles), as well as the model weights, striking a delicate balance between
accurately capturing the concepts and avoiding overfitting. We employ a masked
diffusion loss to enable handles to generate their assigned concepts,
complemented by a novel loss on cross-attention maps to prevent entanglement.
We also introduce union-sampling, a training strategy aimed to improve the
ability of combining multiple concepts in generated images. We use several
automatic metrics to quantitatively compare our method against several
baselines, and further affirm the results using a user study. Finally, we
showcase several applications of our method. Project page is available at:
https://omriavrahami.com/break-a-scene/
