Personalized generation in T2I diffusion models aims to naturally incorporate individual user preferences into the generation process with minimal user intervention. However, existing studies primarily rely on prompt-level modeling with large-scale models, often leading to inaccurate personalization due to the limited input token capacity of T2I diffusion models. To address these limitations, we propose DrUM, a novel method that integrates user profiling with a transformer-based adapter to enable personalized generation through condition-level modeling in the latent space. DrUM demonstrates strong performance on large-scale datasets and seamlessly integrates with open-source text encoders, making it compatible with widely used foundation T2I models without requiring additional fine-tuning.
@article{arxiv.2508.03481,
title = {Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models},
author = {Hyungjin Kim and Seokho Ahn and Young-Duk Seo},
journal= {arXiv preprint arXiv:2508.03481},
year = {2025}
}