通过词向量算术思想，实现图像生成中属性的精细连续控制。
Attribute Token Arithmetic: Disentangled and Continuous Semantic Control for Visual Autoregressive Models
- 在预训练潜空间中直接学习属性方向，无需重训练。
- 仅用单张参考图即可实现多属性连续调整与组合。
- 适合需要高精度图像编辑的研究者与开发者使用。
自回归文本到图像生成近期取得显著进展，通过统一生成框架实现了高质量合成。然而，由于属性耦合及文本与细粒度视觉表征间的错位，精细语义控制仍具挑战。本文提出属性令牌算术（ATA），一种在视觉自回归建模中实现解耦且连续属性控制的方法。受词嵌入向量算术性质启发，ATA 直接在预训练自回归潜空间中识别对应于视觉属性（如年龄、胖瘦、情绪）的语义方向。这些方向仅需一张参考图像即可学习，无需模型微调或大规模监督。生成时，可通过与其他属性令牌的简单算术操作实现属性的连续调节与组合。大量实验表明，ATA 能实现身份保持、细粒度且多属性的调整，在可控性、泛化性和计算效率上优于现有自回归编辑基线方法。代码将公开于 https://github.com/Madaoer/ATA。
原文摘要 · Abstract (English)
Autoregressive text-to-image generation has recently achieved remarkable progress, offering high-fidelity synthesis via a unified generative framework. However, fine-grained semantic control remains challenging due to the attribute entanglement and the misalignment between textual and fine-grained visual representations. In this paper, we introduce Attribute Token Arithmetic (ATA), a method that enables disentangled and continuous attribute control in visual autoregressive modelling. Inspired by the vector arithmetic property observed in word embeddings, ATA identifies semantic directions corresponding to visual attributes (e.g., aging, fatness, emotion) directly within the pretrained autoregressive latent space. These directions are learned from a single reference image, without model retraining or large-scale supervision. During generation, attributes can be continuously adjusted and compositionally combined through simple arithmetic operations with other attribute tokens. Extensive experiments demonstrate that ATA achieves identity-preserving, fine-grained, and multi-attribute adjustment, outperforming existing autoregressive editing baselines in controllability, generality, and computational efficiency. Our code will be available at https://github.com/Madaoer/ATA.
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