HyperLoRA: Parameter-Efficient Adaptive Generation for Portrait Synthesis

2025-03-21

AI Summary

Personalized portrait synthesis faces a trade-off between computationally expensive fine-tuning and adapter-based methods that lack photorealism. Method: We propose a zero-shot, parameter-efficient adaptive generation framework built upon diffusion models. Its core innovation is a hypernetwork that dynamically generates LoRA weights without updating the frozen backbone, enabling plug-and-play lightweight deployment. We further introduce a multi-scale feature alignment training strategy to enhance cross-image generalization. The method supports both single- and multi-image conditioning while preserving high fidelity, fine-grained detail recovery, photorealistic illumination, and semantic editability. Results: Extensive experiments demonstrate that our approach significantly outperforms IP-Adapter and DreamBooth variants under zero-shot settings, achieving state-of-the-art performance in realism, identity preservation, and editing flexibility.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Generation

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Personalized portrait synthesis, essential in domains like social entertainment, has recently made significant progress. Person-wise fine-tuning based methods, such as LoRA and DreamBooth, can produce photorealistic outputs but need training on individual samples, consuming time and resources and posing an unstable risk. Adapter based techniques such as IP-Adapter freeze the foundational model parameters and employ a plug-in architecture to enable zero-shot inference, but they often exhibit a lack of naturalness and authenticity, which are not to be overlooked in portrait synthesis tasks. In this paper, we introduce a parameter-efficient adaptive generation method, namely HyperLoRA, that uses an adaptive plug-in network to generate LoRA weights, merging the superior performance of LoRA with the zero-shot capability of adapter scheme. Through our carefully designed network structure and training strategy, we achieve zero-shot personalized portrait generation (supporting both single and multiple image inputs) with high photorealism, fidelity, and editability.

Problem

• Efficient personalized portrait synthesis with high realism
• Zero-shot adaptation without individual sample training
• Balancing photorealism and editability in generated portraits

Innovation

• Adaptive plug-in network for LoRA weights
• Merges LoRA performance with zero-shot capability
• High photorealism and fidelity in portraits