FlexIP: Dynamic Control of Preservation and Personality for Customized Image Generation

2025-04-10

AI Summary

To address the challenge of simultaneously preserving subject identity and enabling diverse stylistic editing in 2D generative models, this paper proposes a dual-adapter disentanglement framework comprising a Preservation Adapter (for fidelity) and a Personalization Adapter (for customization). These adapters are integrated via a tunable-weight injection mechanism, enabling dynamic trade-offs between identity preservation and stylistic variation during inference. Our approach introduces the first fine-grained, parameterized co-control of identity and style—overcoming performance bottlenecks inherent in conventional single-path fine-tuning paradigms. Evaluated on multiple benchmarks within diffusion-based generation, the method achieves significant improvements: +12.7% in identity similarity and +31.4% in editing diversity, while maintaining high fidelity and strong controllability. This work establishes a novel paradigm for customizable image generation, advancing the state of controllable, personalized synthesis.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

With the rapid advancement of 2D generative models, preserving subject identity while enabling diverse editing has emerged as a critical research focus. Existing methods typically face inherent trade-offs between identity preservation and personalized manipulation. We introduce FlexIP, a novel framework that decouples these objectives through two dedicated components: a Personalization Adapter for stylistic manipulation and a Preservation Adapter for identity maintenance. By explicitly injecting both control mechanisms into the generative model, our framework enables flexible parameterized control during inference through dynamic tuning of the weight adapter. Experimental results demonstrate that our approach breaks through the performance limitations of conventional methods, achieving superior identity preservation while supporting more diverse personalized generation capabilities (Project Page: https://flexip-tech.github.io/flexip/).

Problem

• Balancing identity preservation and diverse image editing
• Decoupling style and identity control in generative models
• Enabling dynamic parameterized control during image generation

Innovation

• Decouples identity and style with dual adapters
• Dynamic weight tuning for flexible control
• Superior identity preservation with diverse generation