Zero-Shot Personalization of Objects via Textual Inversion

2026-03-24

AI Summary

Existing approaches to zero-shot personalized image generation for arbitrary object categories suffer from low efficiency and limited generalization, particularly when handling non-human subjects. This work proposes a novel training-free framework that enables high-quality personalized synthesis in a single forward pass. The method leverages a learnable network to predict object-specific textual inversion embeddings, which are dynamically injected into the temporal layers of a diffusion model’s UNet architecture. By doing so, it achieves zero-shot customization for objects of any category—overcoming the class restrictions inherent in prior techniques—and demonstrates highly efficient, flexible, and scalable generation capabilities across diverse tasks and scenarios.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Deep Generative Models & Autoencoders

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Recent advances in text-to-image diffusion models have substantially improved the quality of image customization, enabling the synthesis of highly realistic images. Despite this progress, achieving fast and efficient personalization remains a key challenge, particularly for real-world applications. Existing approaches primarily accelerate customization for human subjects by injecting identity-specific embeddings into diffusion models, but these strategies do not generalize well to arbitrary object categories, limiting their applicability. To address this limitation, we propose a novel framework that employs a learned network to predict object-specific textual inversion embeddings, which are subsequently integrated into the UNet timesteps of a diffusion model for text-conditional customization. This design enables rapid, zero-shot personalization of a wide range of objects in a single forward pass, offering both flexibility and scalability. Extensive experiments across multiple tasks and settings demonstrate the effectiveness of our approach, highlighting its potential to support fast, versatile, and inclusive image customization. To the best of our knowledge, this work represents the first attempt to achieve such general-purpose, training-free personalization within diffusion models, paving the way for future research in personalized image generation.

Problem

• zero-shot personalization
• text-to-image diffusion models
• object customization
• textual inversion
• generalization

Innovation

• zero-shot personalization
• textual inversion
• diffusion models
• object customization
• training-free