title: Personalized Text-to-Image Generation with Auto-Regressive Models
summary: 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.
paper_authors._id: 6801ba19bd3257c552d201ea
paper_authors.name: Kaiyue Sun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-23T13:51:20.826Z
paper_authors._id: 63640ce5ff4b318d1b7b6f5c
paper_authors.avatarUrl: /avatars/9336d1dab1491f3c8e84b1ea287eb891.svg
paper_authors.fullname: Kaiyue Sun
paper_authors.type: user
paper_authors.user: Kaiyue
paper_authors._id: 6801ba19bd3257c552d201eb
paper_authors.name: Xian Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-23T13:51:26.870Z
paper_authors._id: 65f8f638cb0325b3826fed67
paper_authors.avatarUrl: /avatars/e8fbcdb0445c8b43bd6dc8e84185a588.svg
paper_authors.fullname: Xian Liu
paper_authors.type: user
paper_authors.user: xianliu
paper_authors._id: 6801ba19bd3257c552d201ec
paper_authors.name: Yao Teng
paper_authors._id: 6801ba19bd3257c552d201ed
paper_authors.name: Xihui Liu
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-23T13:51:32.976Z
paper_authors._id: 65d5ec74cd05bc1eaa125040
paper_authors.avatarUrl: /avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
paper_authors.fullname: Xihui Liu
paper_authors.type: user
paper_authors.user: XihuiLiu
paper_title: Personalized Text-to-Image Generation with Auto-Regressive Models
paper_summary: 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.
paper_ai_summary: Auto-regressive models achieve comparable performance to diffusion models in personalized image synthesis through a two-stage training strategy that optimizes text embeddings and fine-tunes transformer layers.
paper_ai_keywords: auto-regressive models diffusion models personalized image synthesis text embeddings transformer layers subject fidelity prompt following