Image editing involves a variety of complex tasks and requires efficient and precise manipulation techniques. In this paper, we present MagicQuill, an integrated image editing system that enables swift actualization of creative ideas. Our system features a streamlined yet functionally robust interface, allowing for the articulation of editing operations (e.g., inserting elements, erasing objects, altering color) with minimal input. These interactions are monitored by a multimodal large language model (MLLM) to anticipate editing intentions in real time, bypassing the need for explicit prompt entry. Finally, we apply a powerful diffusion prior, enhanced by a carefully learned two-branch plug-in module, to process editing requests with precise control. Experimental results demonstrate the effectiveness of MagicQuill in achieving high-quality image edits. Please visit https://magic-quill.github.io to try out our system.
@article{arxiv.2411.09703,
title = {MagicQuill: An Intelligent Interactive Image Editing System},
author = {Zichen Liu and Yue Yu and Hao Ouyang and Qiuyu Wang and Ka Leong Cheng and Wen Wang and Zhiheng Liu and Qifeng Chen and Yujun Shen},
journal= {arXiv preprint arXiv:2411.09703},
year = {2025}
}
Comments
Accepted to CVPR 2025. Code and demo available at https://magic-quill.github.io