Implicit-Explicit Decoupling and Mixture-of-Experts Fusion for Subject-Driven Text-to-Image Generation

Recent advances in large-scale text-to-image (T2I) models have spurred the development of subject-driven T2I generation, which aims to synthesize customized images that align with textual prompts while preserving subject identity. However, existing approaches still struggle to disentangle identity-related information from identity-irrelevant details, leading to overfitting or inadequate preservation of subject identity. In this work, we propose a novel method that enhances the separation of identity-related and identity-irrelevant features, and introduces a feature fusion mechanism to improve image quality and text alignment. Our method consists of two key components: an implicit-explicit foreground-background decoupling module (IEDM) and a feature fusion module (FFM) based on a Mixture of Experts. IEDM uses a learnable adapter for implicit feature-level decoupling and an inpainting technique for explicit foreground-background separation. This decoupling process is facilitated by three complementary loss functions. FFM adaptively integrates identity-irrelevant and identity-related features, enabling more refined feature representations. Extensive experiments demonstrate the effectiveness of our method in improving image quality, enhancing scene adaptability, and increasing the diversity of outputs across various textual prompts.