Personalized Text-to-Image Generation with Auto-Regressive Models
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.
6801ba19bd3257c552d201ea
Kaiyue Sun
admin_assigned
2025-04-23T13:51:20.826Z
63640ce5ff4b318d1b7b6f5c
/avatars/9336d1dab1491f3c8e84b1ea287eb891.svg
Kaiyue Sun
user
Kaiyue
6801ba19bd3257c552d201eb
Xian Liu
admin_assigned
2025-04-23T13:51:26.870Z
65f8f638cb0325b3826fed67
/avatars/e8fbcdb0445c8b43bd6dc8e84185a588.svg
Xian Liu
user
xianliu
6801ba19bd3257c552d201ec
Yao Teng
6801ba19bd3257c552d201ed
Xihui Liu
admin_assigned
2025-04-23T13:51:32.976Z
65d5ec74cd05bc1eaa125040
/avatars/2de1b1539a86452c2c89570eeb02f5ab.svg
Xihui Liu
user
XihuiLiu
Personalized Text-to-Image Generation with Auto-Regressive Models
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.
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.
auto-regressive models diffusion models personalized image synthesis text embeddings transformer layers subject fidelity prompt following