Dual-Identity Preservation in Text-to-Image Generation: A Unified Pipeline Integrating Latent Coupling, Cross-Attention Adaptation, and Artifact Mitigation

This paper tackles the problem of maintaining dual identities in text-to-image generation, crucial for multi-concept image synthesis. It aims to overcome two main challenges: identity loss and partition boundary artifacts.The study presents a refined pipeline for dual-identity image generation. This method combines IP-Adapter with Latent Couple technology to preserve two distinct identities and uses a personalized LoRA model to reduce suture line artifacts. Extensive experiments show that this study's method outperforms existing techniques in identity preservation and image quality for both raw photo and style image generation.