电商和广告设计团队终于有了解决外绘伪影的实用方案——CCE-Diffusion能直接减少背景中与产品重复的语义区域，让产品更突出。做图像生成或商品展示的开发者可以试试集成这个即插即用模块。
针对商品展示图像生成中，前景条件外绘（FCO）方法常出现伪影（即合成背景中与前景实例语义相同的区域）的问题，本文提出CCE-Diffusion框架。其核心是CCE-Module，通过定制概念嵌入来弥合通用名词语义与特定视觉实例之间的差距，并引入实例感知损失和语义保持提示模板来优化。实验表明，该方法显著减少了伪影，提升了图像质量。CCE-Module作为即插即用组件，可集成到多种FCO方法中，为电商等场景提供低成本高质量背景生成方案。
Improving Text-Instance Alignment Of Foreground Conditioned Out-Painting Via Customized Concept Embedding
To showcase products, merchants often incur substantial costs creating high-quality display images. Foreground Conditioned Outpainting (FCO) meets this demand, allowing users to create desired backgrounds for foreground instances at a low cost by adjusting the text prompt. However, existing text-driven FCO methods exhibit critical flaws in their outputs, most notably the presence of artifacts, which refer to regions in the synthesized background that share the same semantics as the foreground instance. Such artifacts diminish the object's prominence and degrade image quality. We attribute the issue to the misalignment between the given instance and text-derived concept embeddings. To address this, we propose the Customized Concept Embedding Diffusion (CCE-Diffusion) framework. Its core is a CCE-Module to customize concept embeddings, bridging the gap between generic noun semantics and a specific visual instance. An Instance-Aware Loss guides the module's optimization, while a Semantic-Preserving Prompt Template prevents customized embeddings from distorting other words in the prompt. Both qualitative and quantitative evaluations demonstrate that CCE-Diffusion significantly reduces artifacts in the outputs. As a plug-and-play component, the CCE-Module can integrate with various FCO methods, enhancing their performance.