MIGE：基於多模態指令的圖像生成與編輯統一框架

MIGE: A Unified Framework for Multimodal Instruction-Based Image Generation and Editing

February 28, 2025

作者: Xueyun Tian, Wei Li, Bingbing Xu, Yige Yuan, Yuanzhuo Wang, Huawei Shen

cs.AI

摘要

尽管基于扩散模型的图像生成已取得显著进展，但主题驱动的生成和基于指令的编辑仍面临挑战。现有方法通常将这两者分开处理，受限于高质量数据的匮乏和泛化能力的不足。然而，这两项任务均需在捕捉复杂视觉变化的同时，保持输入与输出间的一致性。为此，我们提出了MIGE，一个利用多模态指令标准化任务表示的统一框架。它将主题驱动生成视为在空白画布上的创作，而将基于指令的编辑视为对现有图像的修改，从而建立了一个共享的输入-输出表述。MIGE引入了一种新颖的多模态编码器，将自由形式的多模态指令映射到一个统一的视觉-语言空间，通过特征融合机制整合视觉与语义特征。这种统一性使得两项任务能够联合训练，带来两大关键优势：(1) 跨任务增强：通过共享视觉与语义表示，联合训练提升了主题驱动生成和基于指令编辑中的指令遵循度与视觉一致性。(2) 泛化能力：在统一格式下的学习促进了跨任务知识迁移，使MIGE能够泛化至包括基于指令的主题驱动编辑在内的新颖组合任务。实验表明，MIGE在主题驱动生成和基于指令的编辑上均表现出色，并在基于指令的主题驱动编辑这一新任务上树立了新的标杆。代码与模型已公开于https://github.com/Eureka-Maggie/MIGE。

English

Despite significant progress in diffusion-based image generation, subject-driven generation and instruction-based editing remain challenging. Existing methods typically treat them separately, struggling with limited high-quality data and poor generalization. However, both tasks require capturing complex visual variations while maintaining consistency between inputs and outputs. Therefore, we propose MIGE, a unified framework that standardizes task representations using multimodal instructions. It treats subject-driven generation as creation on a blank canvas and instruction-based editing as modification of an existing image, establishing a shared input-output formulation. MIGE introduces a novel multimodal encoder that maps free-form multimodal instructions into a unified vision-language space, integrating visual and semantic features through a feature fusion mechanism.This unification enables joint training of both tasks, providing two key advantages: (1) Cross-Task Enhancement: By leveraging shared visual and semantic representations, joint training improves instruction adherence and visual consistency in both subject-driven generation and instruction-based editing. (2) Generalization: Learning in a unified format facilitates cross-task knowledge transfer, enabling MIGE to generalize to novel compositional tasks, including instruction-based subject-driven editing. Experiments show that MIGE excels in both subject-driven generation and instruction-based editing while setting a state-of-the-art in the new task of instruction-based subject-driven editing. Code and model have been publicly available at https://github.com/Eureka-Maggie/MIGE.