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Magic Insert: Style-Aware Drag-and-Drop

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arxiv 2407.02489 v1 pith:G7IW4YIQ submitted 2024-07-02 cs.CV cs.AIcs.GRcs.HCcs.LG

Nataniel Ruiz , Yuanzhen Li , Neal Wadhwa , Yael Pritch , Michael Rubinstein , David E. Jacobs , Shlomi Fruchter This is my paper · ORCID
classification cs.CVcs.AIcs.GRcs.HCcs.LG
keywords imagemethodstyle-awareinsertionobjectstyletargetdomain
verification ladder T0 review T1 audit T2 compute T3 formal
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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/

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Forward citations

Cited by 5 Pith papers

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