Learning Joint ID-Textual Representation for ID-Preserving Image Synthesis

• URL: http://arxiv.org/abs/2504.14202v1
• Date: Sat, 19 Apr 2025 06:31:07 GMT
• Title: Learning Joint ID-Textual Representation for ID-Preserving Image Synthesis
• Authors: Zichuan Liu, Liming Jiang, Qing Yan, Yumin Jia, Hao Kang, Xin Lu,
• Abstract summary: We propose a novel framework for ID-preserving generation using a multi-modal encoding strategy.<n>FaceCLIP learns a joint embedding space for both identity and textual semantics.<n>We then build FaceCLIP-SDXL, an ID-preserving image synthesis pipeline.
• Score: 19.869955517856273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a novel framework for ID-preserving generation using a multi-modal encoding strategy rather than injecting identity features via adapters into pre-trained models. Our method treats identity and text as a unified conditioning input. To achieve this, we introduce FaceCLIP, a multi-modal encoder that learns a joint embedding space for both identity and textual semantics. Given a reference face and a text prompt, FaceCLIP produces a unified representation that encodes both identity and text, which conditions a base diffusion model to generate images that are identity-consistent and text-aligned. We also present a multi-modal alignment algorithm to train FaceCLIP, using a loss that aligns its joint representation with face, text, and image embedding spaces. We then build FaceCLIP-SDXL, an ID-preserving image synthesis pipeline by integrating FaceCLIP with Stable Diffusion XL (SDXL). Compared to prior methods, FaceCLIP-SDXL enables photorealistic portrait generation with better identity preservation and textual relevance. Extensive experiments demonstrate its quantitative and qualitative superiority.

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