CoAR: Concept Injection into Autoregressive Models for Personalized Text-to-Image Generation

• URL: http://arxiv.org/abs/2508.07341v1
• Date: Sun, 10 Aug 2025 13:36:39 GMT
• Title: CoAR: Concept Injection into Autoregressive Models for Personalized Text-to-Image Generation
• Abstract summary: CoAR learns effective, specific subject representations with only a minimal number of parameters.<n>Experiments demonstrate that CoAR achieves superior performance on both subject-driven personalization and style personalization.
• Score: 14.820840831692246
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The unified autoregressive (AR) model excels at multimodal understanding and generation, but its potential for customized image generation remains underexplored. Existing customized generation methods rely on full fine-tuning or adapters, making them costly and prone to overfitting or catastrophic forgetting. In this paper, we propose \textbf{CoAR}, a novel framework for injecting subject concepts into the unified AR models while keeping all pre-trained parameters completely frozen. CoAR learns effective, specific subject representations with only a minimal number of parameters using a Layerwise Multimodal Context Learning strategy. To address overfitting and language drift, we further introduce regularization that preserves the pre-trained distribution and anchors context tokens to improve subject fidelity and re-contextualization. Additionally, CoAR supports training-free subject customization in a user-provided style. Experiments demonstrate that CoAR achieves superior performance on both subject-driven personalization and style personalization, while delivering significant gains in computational and memory efficiency. Notably, CoAR tunes less than \textbf{0.05\%} of the parameters while achieving competitive performance compared to recent Proxy-Tuning. Code: https://github.com/KZF-kzf/CoAR

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