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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.03481 |
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| _version_ | 1866908479324684288 |
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| author | Kim, Hyungjin Ahn, Seokho Seo, Young-Duk |
| author_facet | Kim, Hyungjin Ahn, Seokho Seo, Young-Duk |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_03481 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models Kim, Hyungjin Ahn, Seokho Seo, Young-Duk Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language 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. |
| title | Draw Your Mind: Personalized Generation via Condition-Level Modeling in Text-to-Image Diffusion Models |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2508.03481 |