TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text Encoder
<|reference_start|>TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text Encoder: Recent breakthroughs in text-to-image models have opened up promising research avenues in personalized image generation, enabling users to create diverse images of a specific subject using natural language prompts. However, existing methods often suffer from performance degradation when given only a single reference image. They tend to overfit the input, producing highly similar outputs regardless of the text prompt. This paper addresses the challenge of one-shot personalization by mitigating overfitting, enabling the creation of controllable images through text prompts. Specifically, we propose a selective fine-tuning strategy that focuses on the text encoder. Furthermore, we introduce three key techniques to enhance personalization performance: (1) augmentation tokens to encourage feature disentanglement and alleviate overfitting, (2) a knowledge-preservation loss to reduce language drift and promote generalizability across diverse prompts, and (3) SNR-weighted sampling for efficient training. Extensive experiments demonstrate that our approach efficiently generates high-quality, diverse images using only a single reference image while significantly reducing memory and storage requirements.<|reference_end|>
@article{park2024textboost:,
    title={TextBoost: Towards One-Shot Personalization of Text-to-Image Models via
  Fine-tuning Text Encoder},
    author={NaHyeon Park, Kunhee Kim, Hyunjung Shim},
    journal={arXiv preprint arXiv:2409.08248},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08248},
    primaryClass={cs.CV}
}