AttnDreamBooth: Towards Text-Aligned Personalized Text-to-Image Generation
Recent advances in text-to-image models have enabled high-quality personalized image synthesis of user-provided concepts with flexible textual control. In this work, we analyze the limitations of two primary techniques in text-to-image personalization: Textual Inversion and DreamBooth. When integrating the learned concept into new prompts, Textual Inversion tends to overfit the concept, while DreamBooth often overlooks it. We attribute these issues to the incorrect learning of the embedding alignment for the concept. We introduce AttnDreamBooth, a novel approach that addresses these issues by separately learning the embedding alignment, the attention map, and the subject identity in different training stages. We also introduce a cross-attention map regularization term to enhance the learning of the attention map. Our method demonstrates significant improvements in identity preservation and text alignment compared to the baseline methods.
Code (0)
등록된 구현이 없습니다.
Tasks
AttributeImage GenerationText to Image GenerationText-to-Image GenerationSimilar Papers 제목 키워드 기반
PALP: Prompt Aligned Personalization of Text-to-Image Models
Content creators often aim to create personalized images using personal subjects that go beyond the capabilities of conventional text-to-image models. Additionally, they may want the resulting image to encompass a specif…
Towards Zero-Shot Personalized Table-to-Text Generation with Contrastive Persona Distillation
Existing neural methods have shown great potentials towards generating informative text from structured tabular data as well as maintaining high content fidelity. However, few of them shed light on generating personalize…
Table-to-Text GenerationText GenerationPersonalized Reward Modeling for Text-to-Image Generation
Recent text-to-image (T2I) models generate semantically coherent images from textual prompts, yet evaluating how well they align with individual user preferences remains an open challenge. Conventional evaluation methods…
Text-to-Image GenerationFew-shot Personalization of LLMs with Mis-aligned Responses
As the diversity of users increases, the capability of providing personalized responses by large language models (LLMs) has become increasingly important. Existing approaches have only limited successes in LLM personaliz…
DiversityPersonalized Showcases: Generating Multi-Modal Explanations for Recommendations
Existing explanation models generate only text for recommendations but still struggle to produce diverse contents. In this paper, to further enrich explanations, we propose a new task named personalized showcases, in whi…
Contrastive Learning