title: Break-A-Scene: Extracting Multiple Concepts from a Single Image
summary: 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/
paper_authors._id: 64743c99a7f23affe7797221
paper_authors.name: Omri Avrahami
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-02T12:46:20.705Z
paper_authors._id: 62f6ab4fc3372328414c8689
paper_authors.avatarUrl: /avatars/4f728a5b70c9fe4a64e80e2b643ca620.svg
paper_authors.fullname: Omri Avrahami
paper_authors.type: user
paper_authors.user: omriav
paper_authors._id: 64743c99a7f23affe7797222
paper_authors.name: Kfir Aberman
paper_authors._id: 64743c99a7f23affe7797223
paper_authors.name: Ohad Fried
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-06-01T16:31:27.613Z
paper_authors._id: 6478c5c4a68454566352db53
paper_authors.avatarUrl: /avatars/827fd5e4ccc5cb38e12edb573e31dbf2.svg
paper_authors.fullname: Ohad Fried
paper_authors.type: user
paper_authors.user: ohadf
paper_authors._id: 64743c99a7f23affe7797224
paper_authors.name: Daniel Cohen-Or
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2023-05-31T12:31:32.401Z
paper_authors._id: 628507161949ebcae8e24ec3
paper_authors.avatarUrl: /avatars/008ecb3daa4c8187b5f339f1176b3c39.svg
paper_authors.fullname: Daniel Cohen-Or
paper_authors.type: user
paper_authors.user: cohenor
paper_authors._id: 64743c99a7f23affe7797225
paper_authors.name: Dani Lischinski
paper_title: Break-A-Scene: Extracting Multiple Concepts from a Single Image
paper_summary: 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/
paper_ai_summary: The method decomposes a single image into distinct text tokens for each concept and fine-tunes textual embeddings using masked diffusion loss to enable synthesis with fine-grained control.
paper_ai_keywords: text-to-image model textual scene decomposition masks pre-trained segmentation model masked diffusion loss cross-attention maps union-sampling