  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.
