Preserve and Personalize: Personalized Text-to-Image Diffusion Models without Distributional Drift

TL;DR

This paper introduces a Lipschitz-based regularization method for personalized text-to-image diffusion models that prevents distributional drift, improves fidelity, and maintains diversity during concept adaptation.

Contribution

It proposes a novel regularization technique that constrains model updates, ensuring distributional consistency while enabling effective personalization of diffusion models.

Findings

Achieves superior visual fidelity and prompt adherence in experiments.

Reduces overfitting and preserves diversity compared to prior methods.

Offers a computationally efficient alternative to resource-intensive sampling.

Abstract

Personalizing text-to-image diffusion models involves integrating novel visual concepts from a small set of reference images while retaining the model's original generative capabilities. However, this process often leads to overfitting, where the model ignores the user's prompt and merely replicates the reference images. We attribute this issue to a fundamental misalignment between the true goals of personalization, which are subject fidelity and text alignment, and the training objectives of existing methods that fail to enforce both objectives simultaneously. Specifically, prior approaches often overlook the need to explicitly preserve the pretrained model's output distribution, resulting in distributional drift that undermines diversity and coherence. To resolve these challenges, we introduce a Lipschitz-based regularization objective that constrains parameter updates during personalization, ensuring bounded deviation from the original distribution. This promotes consistency with the pretrained model's behavior while enabling accurate adaptation to new concepts. Furthermore, our method offers a computationally efficient alternative to commonly used, resource-intensive sampling techniques. Through extensive experiments across diverse diffusion model architectures, we demonstrate that our approach achieves superior performance in both quantitative metrics and qualitative evaluations, consistently excelling in visual fidelity and prompt adherence. We further support these findings with comprehensive analyses, including ablation studies and visualizations.