做多概念图像生成的团队终于有了一个兼顾保真度和可扩展性的方案——SeqLoRA 用双层优化解决了 LoRA 的干扰问题，支持上百个概念组合，做个性化扩散模型的开发者值得一试。
SeqLoRA 是一种针对文本到图像扩散模型的高效微调方法，解决了多概念组合生成中的表示干扰问题。现有模块化方法要么依赖昂贵的后处理融合，要么冻结适配子空间，限制了表达力和概念保真度。SeqLoRA 通过双层优化联合优化两个 LoRA 因子，并建立强收敛保证，从理论上证明学习 LoRA 基比固定基方法更有效减少干扰。实验显示，SeqLoRA 在多达 101 个概念上提升了身份保持和可扩展性，无需昂贵融合，减少了属性干扰。
SeqLoRA: Bilevel Orthogonal Adaptation for Continual Multi-Concept Generation
Parameter-efficient fine-tuning enables fast personalization of text-to-image diffusion models, but composing multiple custom concepts remains challenging due to representation interference. Existing modular methods either rely on expensive post-hoc fusion or freeze adaptation subspaces, which limit expressiveness and concept fidelity. To address this trade-off, we propose Sequential regularized LoRA (SeqLoRA), a constrained continual learning framework that jointly optimizes both LoRA factors via bilevel optimization. Theoretically, we establish strong convergence guarantees for our algorithm and model the residual layer activations as a matrix sub-Gaussian process to derive high-probability bounds on catastrophic forgetting. We further prove that learning the LoRA basis from data minimizes residual interference energy more effectively than frozen-basis methods. Experiments on multi-concept image generation demonstrate that SeqLoRA improves identity preservation and scalability across up to 101 concepts, while avoiding costly fusion and reducing attribute interference in composed generations.