MagicQuill: An Intelligent Interactive Image Editing System
Zichen Liu, Yue Yu, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Wen Wang, Zhiheng Liu, Qifeng Chen, Yujun Shen
2024-11-15
Summary
This paper introduces MagicQuill, an advanced image editing system that uses AI to make editing images faster and easier by predicting user intentions and allowing for precise modifications.
What's the problem?
Image editing can be complicated and time-consuming, requiring users to have specific skills to achieve the desired results. Traditional editing tools often require detailed prompts and manual adjustments, which can slow down the creative process.
What's the solution?
MagicQuill addresses these challenges by providing a user-friendly interface that allows users to perform various editing tasks, such as adding elements, erasing parts of an image, or changing colors, with minimal input. It uses a multimodal large language model (MLLM) to understand what the user wants in real time, eliminating the need for explicit instructions. Additionally, it incorporates a powerful diffusion model to ensure that edits are made with high precision.
Why it matters?
This system is important because it simplifies the image editing process, making it accessible to more people, including those without advanced skills. By enhancing creativity and efficiency in image editing, MagicQuill could benefit artists, designers, and anyone looking to create visually appealing content quickly.
Abstract
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.