MagicQuillV2: Precise and Interactive Image Editing with Layered Visual Cues

2025-12-02

AI Summary

Existing diffusion models exhibit strong semantic capabilities in image editing but rely solely on monolithic text prompts, failing to disambiguate users’ distinct intentions regarding content, spatial placement, structural layout, and color—resulting in coarse-grained control and imprecise edits. To address this, we propose the Hierarchical Generation Editing (HGE) framework, the first to explicitly decouple creative intent into four orthogonal visual layers: content, spatial, structural, and chromatic. HGE introduces a hierarchical visual cue architecture and a unified control network, integrated with diffusion Transformers, a customized data generation pipeline, and a fine-grained spatial branch to enable precise interactive editing. Experiments demonstrate that HGE significantly improves editing accuracy and controllability across object generation, localized editing, and object removal tasks. It effectively bridges the gap between high-level semantic generation and low-level pixel manipulation, advancing human-AI collaborative creation toward greater naturalness and intuitiveness.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Humans and AI: Game Design — Procedural Content Generation & Storytelling
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

We propose MagicQuill V2, a novel system that introduces a extbf{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.

Problem

• Bridges diffusion models' semantics with traditional graphics control
• Deconstructs user intent into layered visual cues for editing
• Enables precise local editing and object removal in images

Innovation

• Layered composition paradigm for image editing
• Deconstructs intent into controllable visual cues
• Specialized data pipeline and unified control module