MagicQuillV2: Precise and Interactive Image Editing with Layered Visual Cues
We propose MagicQuill V2, a novel system that introduces a layered composition paradigm to generative image editing, bridging the gap between the semantic power of diffusion models and the granular control of traditional graphics software. While diffusion transformers excel at holistic generation, their use of singular, monolithic prompts fails to disentangle distinct user intentions for content, position, and appearance. To overcome this, our method deconstructs creative intent into a stack of controllable visual cues: a content layer for what to create, a spatial layer for where to place it, a structural layer for how it is shaped, and a color layer for its palette. Our technical contributions include a specialized data generation pipeline for context-aware content integration, a unified control module to process all visual cues, and a fine-tuned spatial branch for precise local editing, including object removal. Extensive experiments validate that this layered approach effectively resolves the user intention gap, granting creators direct, intuitive control over the generative process.
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Zichen Liu
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2025-12-04T08:48:06.257Z
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LIU Zichen
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LiuZichen
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Yue Yu
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2025-12-04T08:48:04.143Z
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Bruce Yu
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bruceyyu
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Hao Ouyang
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Qiuyu Wang
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2025-12-04T08:48:01.980Z
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Qiuyu Wang
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Shuailei Ma
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Ka Leong Cheng
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2026-01-29T14:50:54.137Z
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Ka Leong Cheng
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felixcheng97
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Wen Wang
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2025-12-12T09:17:59.344Z
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Wen Wang
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wwen1997
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Qingyan Bai
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Yuxuan Zhang
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Yanhong Zeng
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2025-12-05T08:30:04.334Z
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Yanhong Zeng
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Yixuan Li
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Xing Zhu
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Yujun Shen
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Qifeng Chen
MagicQuillV2: Precise and Interactive Image Editing with Layered Visual Cues
We propose MagicQuill V2, a novel system that introduces a layered composition paradigm to generative image editing, bridging the gap between the semantic power of diffusion models and the granular control of traditional graphics software. While diffusion transformers excel at holistic generation, their use of singular, monolithic prompts fails to disentangle distinct user intentions for content, position, and appearance. To overcome this, our method deconstructs creative intent into a stack of controllable visual cues: a content layer for what to create, a spatial layer for where to place it, a structural layer for how it is shaped, and a color layer for its palette. Our technical contributions include a specialized data generation pipeline for context-aware content integration, a unified control module to process all visual cues, and a fine-tuned spatial branch for precise local editing, including object removal. Extensive experiments validate that this layered approach effectively resolves the user intention gap, granting creators direct, intuitive control over the generative process.
MagicQuill V2 introduces a layered composition paradigm for generative image editing, combining diffusion models with granular control, enabling clear separation and manipulation of user intentions for content, position, shape, and color.
diffusion transformers layered composition generative image editing semantic power diffusion models granular control creative intent content layer spatial layer structural layer color layer context-aware content integration unified control module fine-tuned spatial branch object removal