绘制你的思维：文本到图像扩散模型中基于条件级别的个性化生成
Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models
AI总结：
针对T2I扩散模型个性化不准确问题，提出DrUM方法，结合用户画像与Transformer适配器，在潜在空间进行条件级别建模，实现高效个性化生成且无需微调。
摘要：
文本到图像（T2I）扩散模型中的个性化生成旨在以最少的用户干预，将个体用户偏好自然地融入生成过程。然而，现有研究主要依赖大规模模型的提示级别建模，由于T2I扩散模型有限的输入令牌容量，往往导致个性化不准确。为解决这些局限，我们提出DrUM，一种新颖方法，将用户画像与基于Transformer的适配器相结合，通过在潜在空间中进行条件级别建模实现个性化生成。DrUM在大规模数据集上展现出强劲性能，并可与开源文本编码器无缝集成，使其兼容广泛使用的基础T2I模型，且无需额外微调。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
