FlexID: 无训练灵活身份注入 via 意图感知调制 for 文本到图像生成
FlexID: Training-Free Flexible Identity Injection via Intent-Aware Modulation for Text-to-Image Generation
AI总结：
FlexID通过意图感知调制实现无训练灵活身份注入，提升文本到图像生成中身份保真度与文本适应性的平衡。
摘要：
个性化文本到图像生成旨在无缝地将特定身份整合到文本描述中。然而，现有无训练方法往往依赖于刚性的视觉特征注入，导致身份保真度与文本适应性之间的冲突。为了解决这个问题，我们提出了FlexID，一种新的无训练框架，利用意图感知调制。FlexID正交地将身份分解为两个维度：一个语义身份投影器（SIP），它将高级先验注入语言空间；一个视觉特征锚（VFA），它确保在潜在空间中的结构保真度。关键的是，我们引入了一个上下文感知自适应门控（CAG）机制，该机制根据编辑意图和扩散时间步动态调制这些流的权重。通过在检测到强编辑意图时自动放松刚性的视觉约束，CAG实现了身份保持与语义变化之间的协同作用。在IBench上的广泛实验表明，FlexID在身份一致性和文本适应性之间实现了最先进的平衡，为复杂叙述生成提供了一种高效的解决方案。
英文摘要：
Personalized text-to-image generation aims to seamlessly integrate specific identities into textual descriptions. However, existing training-free methods often rely on rigid visual feature injection, creating a conflict between identity fidelity and textual adaptability. To address this, we propose FlexID, a novel training-free framework utilizing intent-aware modulation. FlexID orthogonally decouples identity into two dimensions: a Semantic Identity Projector (SIP) that injects high-level priors into the language space, and a Visual Feature Anchor (VFA) that ensures structural fidelity within the latent space. Crucially, we introduce a Context-Aware Adaptive Gating (CAG) mechanism that dynamically modulates the weights of these streams based on editing intent and diffusion timesteps. By automatically relaxing rigid visual constraints when strong editing intent is detected, CAG achieves synergy between identity preservation and semantic variation. Extensive experiments on IBench demonstrate that FlexID achieves a state-of-the-art balance between identity consistency and text adherence, offering an efficient solution for complex narrative generation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
