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MagicQuill: An Intelligent Interactive Image Editing System

About

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.

Zichen Liu, Yue Yu, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Wen Wang, Zhiheng Liu, Qifeng Chen, Yujun Shen• 2024

Related benchmarks

TaskDatasetResultRank
Image EditingMagicBrush Single-Turn
L1 Loss0.033
11
Multimodal Oil Painting GenerationDiffusionDB stylized
Gram Similarity0.585
8
Image EditingMagicBrush Multi-Turn
L1 Loss0.035
7
Editing Intent PredictionScribbles
Intent Prediction Accuracy30.2
6
Conditional Image EditingConstructed dataset (test)
LPIPS0.0667
5
Image EditingDiffusionDB User Study (test)
Semantic Alignment Score4.01
5
3D Texture Editing24 3D meshes (test)
CLIP Score28.04
5
Intent PredictionPainting Assistor Evaluation Set (test)
GPT-4 Similarity2.712
4
Line-guided Region RedrawingLine-guided Region Redrawing (test)
PSNR17.78
4
Line-guided Region RedrawingLine-guided Region Redrawing dataset
LPIPS0.1472
4
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