提出新型图像生成序列机制，兼顾采样质量与编辑灵活性。
Spanning Tree Autoregressive Visual Generation
- 基于网格上生成树的遍历顺序，结构化随机化生成序列
- 保持与传统自回归模型相当的采样性能，支持灵活编辑
- 适合需要图像局部修改或补全的生成任务
我们提出一种名为星型自回归（STAR）的建模方法，可融合图像中的先验知识（如中心偏好和局部性），在维持采样性能的同时，提供足够灵活的生成序列顺序以支持推理时的图像编辑。传统自回归模型通过随机重排任意序列顺序进行生成时，往往导致采样性能下降或序列选择灵活性受限。STAR 则采用由图像块位置构成的网格上的均匀生成树的遍历顺序，通过广度优先搜索生成，并利用拒绝采样高效构建生成树。该遍历顺序保证了已知部分图像呈现为前缀，天然支持原生图像修复。通过精心设计但结构化的序列随机化策略，STAR 在不改变广泛应用于语言建模的自回归模型架构的前提下，既保留了后缀补全能力，又维持了高质量采样性能。
原文摘要 · Abstract (English)
We present Spanning Tree Autoregressive (STAR) modeling, which can incorporate prior knowledge of images, such as center bias and locality, to maintain sampling performance while also providing sufficiently flexible sequence orders to accommodate image editing at inference time. Approaches that expose conventional autoregressive (AR) models in visual generation to arbitrary sequence orders via random permutation suffer from degraded sampling performance or compromise the flexibility in sequence order choice at inference time. Instead, STAR utilizes traversal orders of uniform spanning trees in a lattice defined by the positions of image patches. Traversal orders are obtained via breadth-first search, allowing us to efficiently construct a spanning tree via rejection sampling whose traversal order ensures that the connected partial observation of the image appears as a prefix for native image inpainting support. Through the tailored yet structured sequence order randomization strategy, STAR preserves the capability of postfix completion while maintaining sampling performance, without any significant changes to the model architecture widely adopted in language AR modeling.
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