Premier: Personalized Preference Modulation with Learnable User Embedding in Text-to-Image Generation
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 introduces a preference adapter that fuses the user embedding with the text prompt. To enable accurate and fine-grained preference control, the fused preference embedding is further used to modulate the generati...
| Authors | Zihao Wang, Yuxiang Wei, Xinpeng Zhou et al. |
|---|---|
| Primary category | cs.CV |
| Published | 21 March 2026 |
| Last updated at source | 12 April 2026 |
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