TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text Encoder

• URL: http://arxiv.org/abs/2409.08248v1
• Date: Thu, 12 Sep 2024 17:47:51 GMT
• Title: TextBoost: Towards One-Shot Personalization of Text-to-Image Models via Fine-tuning Text Encoder
• Authors: NaHyeon Park, Kunhee Kim, Hyunjung Shim,
• Abstract summary: This paper addresses the challenge of one-shot personalization by mitigating overfitting, enabling the creation of controllable images through text prompts. 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.
• Score: 13.695128139074285
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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