Personalized Text-to-Image Generation with Auto-Regressive Models

• URL: http://arxiv.org/abs/2504.13162v1
• Date: Thu, 17 Apr 2025 17:58:26 GMT
• Title: Personalized Text-to-Image Generation with Auto-Regressive Models
• Authors: Kaiyue Sun, Xian Liu, Yao Teng, Xihui Liu,
• Abstract summary: This paper investigates the potential of optimizing auto-regressive models for personalized image synthesis.<n>We propose a two-stage training strategy that combines optimization of text embeddings and fine-tuning of transformer layers.
• Score: 17.294962891093373
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalized image synthesis has emerged as a pivotal application in text-to-image generation, enabling the creation of images featuring specific subjects in diverse contexts. While diffusion models have dominated this domain, auto-regressive models, with their unified architecture for text and image modeling, remain underexplored for personalized image generation. This paper investigates the potential of optimizing auto-regressive models for personalized image synthesis, leveraging their inherent multimodal capabilities to perform this task. We propose a two-stage training strategy that combines optimization of text embeddings and fine-tuning of transformer layers. Our experiments on the auto-regressive model demonstrate that this method achieves comparable subject fidelity and prompt following to the leading diffusion-based personalization methods. The results highlight the effectiveness of auto-regressive models in personalized image generation, offering a new direction for future research in this area.

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