Patch-enhanced Mask Encoder Prompt Image Generation

The document introduces a patch-enhanced mask encoder approach to address the challenge of accurately describing products while preserving diverse backgrounds in advertising imagery generation, consisting of three components: Patch Flexible Visibility, Mask Encoder Prompt Adapter, and an image Foundation Model, with experimental results demonstrating its superior visual results and FID scores compared to other methods.

Abstract

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.

Method

The document presents an innovative method for generating advertising imagery, which is divided into two distinct modules: the Art Control Module (ACM) and the Generation Module (GM). The ACM defines the stylistic aspects of the image's background, while the GM ensures coherence in the synthesis of foreground and background components. The method utilizes diffusion models and incorporates Patch Flexible Visibility, Mask Encoder Prompt Adapter, and an image Foundation Model to achieve accurate product descriptions while preserving diverse backgrounds. Experimental results demonstrate that this method outperforms existing approaches in terms of visual quality and FID scores.

Main Finding

The main finding of this document is the introduction of an innovative method for generating advertising imagery, utilizing an AIGC-empowered methodology for product color-matching design. This approach, consisting of the Art Control Module (ACM) and the Generation Module (GM), incorporates Patch Flexible Visibility, Mask Encoder Prompt Adapter, and an image Foundation Model to ensure accurate product descriptions while preserving diverse backgrounds. Experimental results demonstrate that this method achieves superior visual results and FID scores compared to other methods, establishing its effectiveness in advertising content creation.

Conclusion

The conclusion of the document presents an innovative method for generating advertising imagery, utilizing an AIGC-empowered methodology for product color-matching design. The architecture of the model is divided into two distinct modules: the Art Control Module (ACM) and the Generation Module (GM). The method incorporates Patch Flexible Visibility, Mask Encoder Prompt Adapter, and an image Foundation Model to ensure accurate product descriptions while preserving diverse backgrounds. Experimental results demonstrate that this method achieves superior visual results and FID scores compared to other methods, establishing its effectiveness in advertising content creation.

Keywords

The conclusion of the document presents an innovative method for generating advertising imagery, utilizing an AIGC-empowered methodology for product color-matching design. The architecture of the model is divided into two distinct modules: the Art Control Module (ACM) and the Generation Module (GM). The method incorporates Patch Flexible Visibility, Mask Encoder Prompt Adapter, and an image Foundation Model to ensure accurate product descriptions while preserving diverse backgrounds. Experimental results demonstrate that this method achieves superior visual results and FID scores compared to other methods, establishing its effectiveness in advertising content creation.