Recent remarkable improvements in large-scale text-to-image generative models have shown promising results in generating high-fidelity images. To further enhance editability and enable fine-grained generation, we introduce a multi-input-conditioned image composition model that incorporates a sketch as a novel modal, alongside a reference image. Thanks to the edge-level controllability using sketches, our method enables a user to edit or complete an image sub-part with a desired structure (i.e., sketch) and content (i.e., reference image). Our framework fine-tunes a pre-trained diffusion model to complete missing regions using the reference image while maintaining sketch guidance. Albeit simple, this leads to wide opportunities to fulfill user needs for obtaining the in-demand images. Through extensive experiments, we demonstrate that our proposed method offers unique use cases for image manipulation, enabling user-driven modifications of arbitrary scenes.
@article{arxiv.2304.09748,
title = {Reference-based Image Composition with Sketch via Structure-aware Diffusion Model},
author = {Kangyeol Kim and Sunghyun Park and Junsoo Lee and Jaegul Choo},
journal= {arXiv preprint arXiv:2304.09748},
year = {2023}
}