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Overpainting: Localized Context-aware Diffusion Image Editing

arxiv.org/abs/2609.10811

Updated 50 min ago · first seen 11 Sept 2026

paper_01M294H1PRGP2EFP598DM6CKS5

Published
11 Sept 2026
T1 · 50 min ago
arXiv
2609.10811
T1 · 50 min ago
Category
cs.CV
T1 · 50 min ago

Abstract

We present "overpainting", an image editing operation which offers both control over the location of the edit and awareness of the previous content in that location. The overpainted area is given by a trimap, where white-annotated pixels must be edited, gray-annotated pixels may be edited, and black-annotated pixels must not be edited. This enables both precise and loose control, depending on user intent. We implement overpainting by adapting a pretrained image editing diffusion model using a combination of joint attention and low-rank adaption across input images with attention-dropout to balance the information flow between noise, source and mask images. We present a novel, automated, training data generation pipeline that (1) generates a set of candidate image pairs leveraging existing language-based editing models, (2) carefully curates those pairs, and (3) extracts a trimap from each usable pair. We demonstrate the versatility of our overpainting model on a wide range of editing tasks.

Authors 5

Sam Sartor, Iliyan Georgiev, Michael Fischer, Valentin Deschaintre, Pieter Peers

Specification

Official page

Source:arXiv (Atom API + RSS)T1observed 50 min agohigh

Arxiv announce type
new

Source:arXiv (Atom API + RSS)T1observed 50 min agohigh

arXiv id
2609.10811

Source:arXiv (Atom API + RSS)T1observed 50 min agohigh

Categories
cs.CV

Source:arXiv (Atom API + RSS)T1observed 50 min agohigh

PDF

Source:arXiv (Atom API + RSS)T1observed 50 min agohigh

Primary category
cs.CV

Source:arXiv (Atom API + RSS)T1observed 50 min agohigh

Published
11 Sept 2026

Source:arXiv (Atom API + RSS)T1observed 50 min agohigh

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Provenance

Attributed facts

9

Source tiers

T19

Freshest observation

50 min ago

Conflicts

None