Finetuning-Free Personalization of Text to Image Generation via Hypernetworks

• URL: http://arxiv.org/abs/2511.03156v1
• Date: Wed, 05 Nov 2025 03:31:33 GMT
• Title: Finetuning-Free Personalization of Text to Image Generation via Hypernetworks
• Authors: Sagar Shrestha, Gopal Sharma, Luowei Zhou, Suren Kumar,
• Abstract summary: We introduce fine-tuning-free personalization via Hypernetworks that predict LoRA-adapted weights directly from subject images.<n>Our approach achieves strong personalization performance and highlights the promise of hypernetworks as a scalable and effective direction for open-category personalization.
• Score: 15.129799519953139
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalizing text-to-image diffusion models has traditionally relied on subject-specific fine-tuning approaches such as DreamBooth~\cite{ruiz2023dreambooth}, which are computationally expensive and slow at inference. Recent adapter- and encoder-based methods attempt to reduce this overhead but still depend on additional fine-tuning or large backbone models for satisfactory results. In this work, we revisit an orthogonal direction: fine-tuning-free personalization via Hypernetworks that predict LoRA-adapted weights directly from subject images. Prior hypernetwork-based approaches, however, suffer from costly data generation or unstable attempts to mimic base model optimization trajectories. We address these limitations with an end-to-end training objective, stabilized by a simple output regularization, yielding reliable and effective hypernetworks. Our method removes the need for per-subject optimization at test time while preserving both subject fidelity and prompt alignment. To further enhance compositional generalization at inference time, we introduce Hybrid-Model Classifier-Free Guidance (HM-CFG), which combines the compositional strengths of the base diffusion model with the subject fidelity of personalized models during sampling. Extensive experiments on CelebA-HQ, AFHQ-v2, and DreamBench demonstrate that our approach achieves strong personalization performance and highlights the promise of hypernetworks as a scalable and effective direction for open-category personalization.

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