TextBoost: Boosting Text Encoder for Personalized Text-to-Image Generation

Understand

In this paper, we introduce TextBoost, an efficient one-shot personalization approach for text-to-image diffusion models.

• Traditional personalization methods typically involve fine-tuning extensive portions of the model, leading to substantial storage requirements and slow convergence.
• In contrast, we propose selectively fine-tuning only the text encoder, significantly improving computational and storage efficiency.
• To preserve the original semantic integrity, we develop a novel causality-preserving adaptation mechanism.