{"ID":2886382,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.03481","arxiv_id":"2508.03481","title":"Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2508.03481","url_pdf":"https://arxiv.org/pdf/2508.03481v1","authors":"[\"Hyungjin Kim\",\"Seokho Ahn\",\"Young-Duk Seo\"]","published":"2025-08-05T14:14:55Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\",\"cs.CL\"]","methods":"[\"Diffusion Model\",\"Transformer\"]","has_code":false}