Magic Insert: Style-Aware Drag-and-Drop
We present Magic Insert, a method for dragging-and-dropping subjects from a
user-provided image into a target image of a different style in a physically
plausible manner while matching the style of the target image. This work
formalizes the problem of style-aware drag-and-drop and presents a method for
tackling it by addressing two sub-problems: style-aware personalization and
realistic object insertion in stylized images. For style-aware personalization,
our method first fine-tunes a pretrained text-to-image diffusion model using
LoRA and learned text tokens on the subject image, and then infuses it with a
CLIP representation of the target style. For object insertion, we use
Bootstrapped Domain Adaption to adapt a domain-specific photorealistic object
insertion model to the domain of diverse artistic styles. Overall, the method
significantly outperforms traditional approaches such as inpainting. Finally,
we present a dataset, SubjectPlop, to facilitate evaluation and future progress
in this area. Project page: https://magicinsert.github.io/
66857aa5ef8d151431928941
Nataniel Ruiz
claimed_verified
2024-07-04T08:03:58.411Z
6318c6e6cb116eab31d70a16
https://cdn-avatars.huggingface.co/v1/production/uploads/6318c6e6cb116eab31d70a16/yAw5XtvqaPUkPj2B6JOzz.jpeg
Nataniel
user
flavoredquark
66857aa5ef8d151431928942
Yuanzhen Li
66857aa5ef8d151431928943
Neal Wadhwa
66857aa5ef8d151431928944
Yael Pritch
66857aa5ef8d151431928945
Michael Rubinstein
66857aa5ef8d151431928946
David E. Jacobs
66857aa5ef8d151431928947
Shlomi Fruchter
Magic Insert: Style-Aware Drag-and-Drop
We present Magic Insert, a method for dragging-and-dropping subjects from a
user-provided image into a target image of a different style in a physically
plausible manner while matching the style of the target image. This work
formalizes the problem of style-aware drag-and-drop and presents a method for
tackling it by addressing two sub-problems: style-aware personalization and
realistic object insertion in stylized images. For style-aware personalization,
our method first fine-tunes a pretrained text-to-image diffusion model using
LoRA and learned text tokens on the subject image, and then infuses it with a
CLIP representation of the target style. For object insertion, we use
Bootstrapped Domain Adaption to adapt a domain-specific photorealistic object
insertion model to the domain of diverse artistic styles. Overall, the method
significantly outperforms traditional approaches such as inpainting. Finally,
we present a dataset, SubjectPlop, to facilitate evaluation and future progress
in this area. Project page: https://magicinsert.github.io/
Magic Insert allows style-aware drag-and-drop insertion of subjects from one image into another of a different style using domain adaptation and a fine-tuned diffusion model.
text-to-image diffusion model LoRA text tokens CLIP representation Bootstrapped Domain Adaptation photorealistic object insertion model style-aware drag-and-drop style-aware personalization realistic object insertion stylized images inpainting