English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This study introduces Text-Guided Subject-Driven Image Inpainting, a noveltask that combines text and exemplar images for image inpainting. While bothtext and exemplar images have been used independently in previous efforts,their combined utilization remains unexplored. Simultaneously accommodatingboth conditions poses a significant challenge due to the inherent balancerequired between editability and subject fidelity. To tackle this challenge, wepropose a two-step approach DreamInpainter. First, we compute dense subjectfeatures to ensure accurate subject replication. Then, we employ adiscriminative token selection module to eliminate redundant subject details,preserving the subject's identity while allowing changes according to otherconditions such as mask shape and text prompts. Additionally, we introduce adecoupling regularization technique to enhance text control in the presence ofexemplar images. Our extensive experiments demonstrate the superior performanceof our method in terms of visual quality, identity preservation, and textcontrol, showcasing its effectiveness in the context of text-guidedsubject-driven image inpainting.

DreamInpainter: Text-Guided Subject-Driven Image Inpainting with Diffusion Models