AI RESEARCH
Premier: Personalized Preference Modulation with Learnable User Embedding in Text-to-Image Generation
arXiv CS.CV
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ArXi:2603.20725v1 Announce Type: new Text-to-image generation has advanced rapidly, yet it still struggles to capture the nuanced user preferences. Existing approaches typically rely on multimodal large language models to infer user preferences, but the derived prompts or latent codes rarely reflect them faithfully, leading to suboptimal personalization. We present Premier, a novel preference modulation framework for personalized image generation. Premier represents each user's preference as a learnable embedding and.