With the advance of diffusion models, various personalized image generation
methods have been proposed. However, almost all existing work only focuses on
either subject-driven or style-driven personalization. Meanwhile,
state-of-the-art methods face several challenges in realizing compositional
personalization, i.e., composing different subject and style concepts, such as
concept disentanglement, unified reconstruction paradigm, and insufficient
training data. To address these issues, we introduce FreeTuner, a flexible and
training-free method for compositional personalization that can generate any
user-provided subject in any user-provided style (see Figure 1). Our approach
employs a disentanglement strategy that separates the generation process into
two stages to effectively mitigate concept entanglement. FreeTuner leverages
the intermediate features within the diffusion model for subject concept
representation and introduces style guidance to align the synthesized images
with the style concept, ensuring the preservation of both the subject's
structure and the style's aesthetic features. Extensive experiments have
demonstrated the generation ability of FreeTuner across various personalization
settings.

FreeTuner 是一种灵活的、无需训练的组合个性化方法，可以以任意用户提供的主题和风格生成图像，通过分解生成过程为两个阶段来有效缓解概念纠缠，利用扩散模型中的中间特征进行主题概念表达，并引入风格指导以确保生成的图像既保留了主题结构又呈现了风格的美感特征，通过大量实验证明了 FreeTuner 在各种个性化设置下的生成能力。