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> **Abstract:** *Recent progress in text-to-image (T2I) diffusion models has greatly improved image quality and flexibility. However, a major challenge in personalized generation remains: preserving the subject’s identity (ID) while allowing diverse visual changes. We address this with a new framework for ID-preserving image generation. Instead of relying on adapter modules to inject identity features into pre-trained models, we propose a unified multi-modal encoding strategy that jointly captures identity and text information. Our method, called FaceCLIP, learns a shared embedding space for facial identity and textual semantics. Given a reference face image and a text prompt, FaceCLIP produces a joint representation that guides the generative model to synthesize images consistent with both the subject’s identity and the prompt. To train FaceCLIP, we introduce a multi-modal alignment loss that aligns features across face, text, and image domains. We then integrate FaceCLIP with existing UNet and Diffusion Transformer (DiT) architectures, forming a complete synthesis pipeline FaceCLIP-x. Compared to existing ID-preserving approaches, our method produces more photorealistic portraits with better identity retention and text alignment. Extensive experiments demonstrate that FaceCLIP-x outperforms prior methods in both qualitative and quantitative evaluations.*
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 diffusion models has greatly improved image quality and flexibility. However, a major challenge in personalized generation remains: preserving the subject’s identity (ID) while allowing diverse visual changes. We address this with a new framework for ID-preserving image generation. Instead of relying on adapter modules to inject identity features into pre-trained models, we propose a unified multi-modal encoding strategy that jointly captures identity and text information. Our method, called FaceCLIP, learns a shared embedding space for facial identity and textual semantics. Given a reference face image and a text prompt, FaceCLIP produces a joint representation that guides the generative model to synthesize images consistent with both the subject’s identity and the prompt. To train FaceCLIP, we introduce a multi-modal alignment loss that aligns features across face, text, and image domains. We then integrate FaceCLIP with existing UNet and Diffusion Transformer (DiT) architectures, forming a complete synthesis pipeline FaceCLIP-x. Compared to existing ID-preserving approaches, our method produces more photorealistic portraits with better identity retention and text alignment. Extensive experiments demonstrate that FaceCLIP-x outperforms prior methods in both qualitative and quantitative evaluations.*
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## 🔧 Installation and Usage
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