TextBoost: Towards One-Shot Personalization of Text-to-Image Models via
  Fine-tuning Text Encoder
Authors: name: NaHyeon Park, name: Kunhee Kim, name: Hyunjung Shim

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.