A Training-Free Style-Personalization via Scale-wise Autoregressive Model
This addresses the problem of efficient and flexible style control in image generation for users, though it is incremental as it builds on existing autoregressive models.
The paper tackles style-personalized image generation without training by using a scale-wise autoregressive model with a three-path design, achieving competitive style and prompt fidelity compared to fine-tuned baselines while offering faster inference.
We present a training-free framework for style-personalized image generation that controls content and style information during inference using a scale-wise autoregressive model. Our method employs a three-path design--content, style, and generation--each guided by a corresponding text prompt, enabling flexible and efficient control over image semantics without any additional training. A central contribution of this work is a step-wise and attention-wise intervention analysis. Through systematic prompt and feature injection, we find that early-to-middle generation steps play a pivotal role in shaping both content and style, and that query features predominantly encode content-specific information. Guided by these insights, we introduce two targeted mechanisms: Key Stage Attention Sharing, which aligns content and style during the semantically critical steps, and Adaptive Query Sharing, which reinforces content semantics in later steps through similarity-aware query blending. Extensive experiments demonstrate that our method achieves competitive style fidelity and prompt fidelity compared to fine-tuned baselines, while offering faster inference and greater deployment flexibility.