  Artificial Intelligence Generated Content(AIGC), known for its superior
visual results, represents a promising mitigation method for high-cost
advertising applications. Numerous approaches have been developed to manipulate
generated content under different conditions. However, a crucial limitation
lies in the accurate description of products in advertising applications.
Applying previous methods directly may lead to considerable distortion and
deformation of advertised products, primarily due to oversimplified content
control conditions. Hence, in this work, we propose a patch-enhanced mask
encoder approach to ensure accurate product descriptions while preserving
diverse backgrounds. Our approach consists of three components Patch Flexible
Visibility, Mask Encoder Prompt Adapter and an image Foundation Model. Patch
Flexible Visibility is used for generating a more reasonable background image.
Mask Encoder Prompt Adapter enables region-controlled fusion. We also conduct
an analysis of the structure and operational mechanisms of the Generation
Module. Experimental results show our method can achieve the highest visual
results and FID scores compared with other methods.
