InstructAttribute：基于指令的细粒度对象属性编辑
InstructAttribute: Fine-grained Object Attributes editing with Instruction
AI总结：
本文提出InstructAttribute，一种基于指令微调的模型，结合免训练的SPAA框架和多模态大模型，实现细粒度对象级颜色与材质属性编辑，兼顾修改准确性与结构保持。
摘要：
文本到图像（T2I）扩散模型因其强大的生成能力而被广泛应用于图像编辑。然而，实现对特定对象属性（如颜色和材质）的细粒度控制仍然是一个相当大的挑战。现有方法往往无法准确修改这些属性，或者会损害结构完整性和整体图像一致性。为了填补这一空白，我们引入了结构保持与属性增强（SPAA），这是一种新颖的免训练框架，通过智能地操纵扩散模型中的自注意力图和交叉注意力值，能够为同一对象精确生成颜色和材质属性。在SPAA的基础上，我们集成多模态大语言模型（MLLMs）以自动化数据整理和指令生成。利用这一对象属性数据收集引擎，我们构建了属性数据集（Attribute Dataset），涵盖了不同对象类别中全面的颜色和材质范围。利用该生成的数据集，我们提出了InstructAttribute，一个经过指令微调的模型，能够通过自然语言提示实现细粒度和对象级别的属性编辑。这一能力对多个领域具有重要的实际意义，从加速产品设计和电子商务可视化到增强虚拟试穿体验。大量实验表明，InstructAttribute优于现有的基于指令的基线方法，在属性修改准确性和结构保持之间实现了优越的平衡。
英文摘要：
Text-to-image (T2I) diffusion models are widely used in image editing due to their powerful generative capabilities. However, achieving fine-grained control over specific object attributes, such as color and material, remains a considerable challenge. Existing methods often fail to accurately modify these attributes or compromise structural integrity and overall image consistency. To fill this gap, we introduce Structure Preservation and Attribute Amplification (SPAA), a novel training-free framework that enables precise generation of color and material attributes for the same object by intelligently manipulating self-attention maps and cross-attention values within diffusion models. Building on SPAA, we integrate multi-modal large language models (MLLMs) to automate data curation and instruction generation. Leveraging this object attribute data collection engine, we construct the Attribute Dataset, encompassing a comprehensive range of colors and materials across diverse object categories. Using this generated dataset, we propose InstructAttribute, an instruction-tuned model that enables fine-grained and object-level attribute editing through natural language prompts. This capability holds significant practical implications for diverse fields, from accelerating product design and e-commerce visualization to enhancing virtual try-on experiences. Extensive experiments demonstrate that InstructAttribute outperforms existing instruction-based baselines, achieving a superior balance between attribute modification accuracy and structural preservation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
