InstructBooth: Instruction-following Personalized Text-to-Image Generation

• URL: http://arxiv.org/abs/2312.03011v2
• Date: Thu, 15 Feb 2024 16:38:46 GMT
• Title: InstructBooth: Instruction-following Personalized Text-to-Image Generation
• Authors: Daewon Chae, Nokyung Park, Jinkyu Kim, Kimin Lee
• Abstract summary: InstructBooth is a novel method designed to enhance image-text alignment in personalized text-to-image models. Our approach first personalizes text-to-image models with a small number of subject-specific images using a unique identifier. After personalization, we fine-tune personalized text-to-image models using reinforcement learning to maximize a reward that quantifies image-text alignment.
• Score: 30.89054609185801
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Personalizing text-to-image models using a limited set of images for a specific object has been explored in subject-specific image generation. However, existing methods often face challenges in aligning with text prompts due to overfitting to the limited training images. In this work, we introduce InstructBooth, a novel method designed to enhance image-text alignment in personalized text-to-image models without sacrificing the personalization ability. Our approach first personalizes text-to-image models with a small number of subject-specific images using a unique identifier. After personalization, we fine-tune personalized text-to-image models using reinforcement learning to maximize a reward that quantifies image-text alignment. Additionally, we propose complementary techniques to increase the synergy between these two processes. Our method demonstrates superior image-text alignment compared to existing baselines, while maintaining high personalization ability. In human evaluations, InstructBooth outperforms them when considering all comprehensive factors. Our project page is at https://sites.google.com/view/instructbooth.

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