arXiv:2605.22743v1 Announce Type: new Abstract: 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
The rapid adoption of diffusion models for generative AI and the increasing demand for personalized, multi-concept visual generation highlight the current limitations of existing fine-tuning methods.
This development allows for more efficient and higher-fidelity personalization of text-to-image diffusion models, which has significant implications for content creation, design, and various AI applications.
The ability to compose multiple custom concepts without significant representation interference improves the flexibility and quality of AI-generated content and reduces computational overhead for customization.
- · AI content creators
- · Generative AI platforms
- · Design and advertising industries
- · AI researchers
- · Companies reliant on expensive custom model training
- · Creators limited by current generative AI capabilities
Improved parameter-efficient fine-tuning for text-to-image models becomes widely available.
The cost and complexity of generating highly specific custom visual content decrease significantly, democratizing advanced AI artistry.
New forms of personalized and interactive media emerge, blurring the lines between user-generated and AI-generated content at scale.
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Read at arXiv cs.LG