Break-A-Scene: Extracting Multiple Concepts from a Single Image
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/
64743c99a7f23affe7797221
Omri Avrahami
claimed_verified
2023-06-02T12:46:20.705Z
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/avatars/4f728a5b70c9fe4a64e80e2b643ca620.svg
Omri Avrahami
user
omriav
64743c99a7f23affe7797222
Kfir Aberman
64743c99a7f23affe7797223
Ohad Fried
claimed_verified
2023-06-01T16:31:27.613Z
6478c5c4a68454566352db53
/avatars/827fd5e4ccc5cb38e12edb573e31dbf2.svg
Ohad Fried
user
ohadf
64743c99a7f23affe7797224
Daniel Cohen-Or
extracted_confirmed
2023-05-31T12:31:32.401Z
628507161949ebcae8e24ec3
/avatars/008ecb3daa4c8187b5f339f1176b3c39.svg
Daniel Cohen-Or
user
cohenor
64743c99a7f23affe7797225
Dani Lischinski
Break-A-Scene: Extracting Multiple Concepts from a Single Image
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/
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.
text-to-image model textual scene decomposition masks pre-trained segmentation model masked diffusion loss cross-attention maps union-sampling