Zero-Shot Image Personalization from Personas

Text-to-image diffusion models are increasingly deployed increative contexts, yet remain impersonal, optimized for aggregate aes-thetics rather than individual taste. Human preferences are inherentlypluralistic: one user who favors muted, nostalgic portraits may prefer vi-brant compositions for street photography, while another gravitates to-ward dreamy, overexposed film aesthetics. Existing personalization meth-ods require dense interaction histories or per-user fine-tuning, failing incold-start settings and collapsing each user’s context-dependent prefer-ences into a single static representation. We introduce zero-shot imagepersonalization from personas (ZIPP), a paradigm that conditions im-age generation on natural-language personas (concise descriptors of auser’s identity, interests, and aesthetic sensibilities) without any user-specific data or weight updates. ZIPP uses an LLM in a roleplay set-ting to rewrite input prompts from the perspective of a given persona,steering diffusion models toward personalized outputs. To mine per-sonas at scale, we develop an inductive Graph Attention Network over a23M-user Reddit interaction graph with dual contrastive objectives thatalign graph structure with users’ visual behavior, and verbalize learnedrepresentations into coherent natural-language personas via an MLLM.We further introduce ZIP-Bench the first zero-shot image personal-ization benchmark, pairing 1.5K users with graph-mined personas and40K generated images. Across four benchmarks and 14 LLMs spanningfive model families, persona conditioning yields consistent improvementsin both zero-shot and few-shot settings, with frontier models achievingthe strongest gains (13–20%). In the few-shot setting, ZIPP matchesor exceeds fine-tuned baselines requiring per-user adapters trained on100+ examples. Unlike baselines that collapse preferences into a fixedstyle, ZIPP preserves intra-user preference diversity, achieving the low-est distributional divergence from users’ true preference distributions(CMMD 0.16 vs. 0.55 for fine-tuned alternatives). IPF-normalized eval-uation against global population demographics further reveals that ex-isting methods exhibit substantial bias toward narrow subpopulations,which persona conditioning significantly mitigates. A human evaluationconfirms these findings: ZIPP achieves a 79% win rate over genericgeneration and outperforms all fine-tuned baselines (58–65% win rate)without any user-specific training. We release ZIP-Bench at: https://huggingface.co/datasets/behavior-in-the-wild/zip-bench