生成树自回归视觉生成
Spanning Tree Autoregressive Visual Generation
AI总结：
提出生成树自回归（STAR）建模，通过均匀生成树的遍历顺序在保持采样性能的同时提供灵活序列顺序，支持图像编辑。
摘要：
我们提出了生成树自回归（STAR）建模，它可以融入图像的先验知识，如中心偏差和局部性，以保持采样性能，同时提供足够灵活的序列顺序以适应推理时的图像编辑。通过随机排列将传统自回归（AR）模型暴露于任意序列顺序的方法会导致采样性能下降或牺牲推理时序列顺序选择的灵活性。相反，STAR利用由图像块位置定义的网格中均匀生成树的遍历顺序。遍历顺序通过广度优先搜索获得，使我们能够通过拒绝采样高效地构建生成树，其遍历顺序确保图像的连通部分观测作为前缀出现，以支持原生图像修复。通过定制但结构化的序列顺序随机化策略，STAR在保持采样性能的同时保留了后缀补全的能力，且无需对语言AR建模中广泛采用的模型架构进行重大更改。
英文摘要：
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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