Generative artificial intelligence (GenAI) has made significant progress in
understanding world knowledge and generating content from human languages
across various modalities, like text-to-text large language models,
text-to-image stable diffusion, and text-to-video Sora. While in this paper, we
investigate the capability of GenAI for text-to-model generation, to see
whether GenAI can comprehend hyper-level knowledge embedded within AI itself
parameters. Specifically, we study a practical scenario termed
train-once-for-all personalization, aiming to generate personalized models for
diverse end-users and tasks using text prompts. Inspired by the recent
emergence of neural network diffusion, we present Tina, a text-conditioned
neural network diffusion for train-once-for-all personalization. Tina leverages
a diffusion transformer model conditioned on task descriptions embedded using a
CLIP model. Despite the astronomical number of potential personalized tasks
(e.g., $1.73\times10^{13}$), by our design, Tina demonstrates remarkable
in-distribution and out-of-distribution generalization even trained on small
datasets ($\sim 1000$). We further verify whether and how \Tina understands
world knowledge by analyzing its capabilities under zero-shot/few-shot image
prompts, different numbers of personalized classes, prompts of natural language
descriptions, and predicting unseen entities.

通过研究 train-once-for-all 个性化场景，本文调查了生成人工智能（GenAI）的文本到模型生成能力，以探讨其是否能理解内嵌在 AI 参数中的超级级别知识。作者提出了 Tina，一个基于文本的神经网络扩散模型，以实现 train-once-for-all 个性化。研究结果显示，即使在小数据集上进行训练（约 1000 个），Tina 表现出显着的分布内和分布外泛化能力，进一步验证了其对世界知识的理解能力。