Personalized Text-to-Image Generation with Auto-Regressive Models
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912333122502656 |
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| author | Sun, Kaiyue Liu, Xian Teng, Yao Liu, Xihui |
| author_facet | Sun, Kaiyue Liu, Xian Teng, Yao Liu, Xihui |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_13162 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Personalized Text-to-Image Generation with Auto-Regressive Models Sun, Kaiyue Liu, Xian Teng, Yao Liu, Xihui Computer Vision and Pattern Recognition 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. |
| title | Personalized Text-to-Image Generation with Auto-Regressive Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.13162 |