An Efficient and Harmonized Framework for Balanced Cross-Domain Feature Integration

Understand

Despite significant advancements in image generation using advanced generative frameworks, cross-image integration of content and style remains a key challenge.

• Current generative models, while powerful, frequently depend on vague textual prompts to define styles--creating difficulties in balancing content semantics and style preservation.
• We propose a novel framework that utilizes customized models to learn style representations.
• It enhances content preservation through cross-model feature and attention modulation, leveraging the inherent semantic consistency across models.