无需训练，通过条件与掩码引导实现电商图像风格一致生成
Training-Free Style Consistent Image Synthesis with Condition and Mask Guidance in E-Commerce
- 在QKV层面修改注意力图，不破坏商品主体结构
- 使用共享键值增强跨注意力相似性，提升风格一致性
- 适合需要快速部署的电商图像生成场景
在电商领域，生成风格一致的图像是一项常见任务。当前方法多基于扩散模型，已取得优异效果。本文提出在UNet与图像条件融合时，引入QKV（查询/键/值）层级的概念，对自注意力和跨注意力的注意力图进行修改。目标是在不改变电商品牌图像主体结构的前提下，采用无训练方法，通过预设条件引导生成。该方法利用共享键值（KV）增强跨注意力中的相似性，并从注意力图中生成掩码引导，巧妙控制风格一致性的生成过程。实验表明，该方法在实际应用中表现良好。
原文摘要 · Abstract (English)
Generating style-consistent images is a common task in the e-commerce field, and current methods are largely based on diffusion models, which have achieved excellent results. This paper introduces the concept of the QKV (query/key/value) level, referring to modifications in the attention maps (self-attention and cross-attention) when integrating UNet with image conditions. Without disrupting the product's main composition in e-commerce images, we aim to use a train-free method guided by pre-set conditions. This involves using shared KV to enhance similarity in cross-attention and generating mask guidance from the attention map to cleverly direct the generation of style-consistent images. Our method has shown promising results in practical applications.
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