arXiv:2511.17089v2 Announce Type: replace-cross Abstract: 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, S
The continuous evolution of AI models demands increasingly flexible and efficient methods for visual generation, and this research addresses current limitations.
This development could significantly enhance the user-friendliness and creative control of AI image generation, making it more adaptable for diverse applications beyond standard outputs.
The ability to integrate prior knowledge and offer flexible sequence orders for image editing fundamentally improves autoregressive visual generation, moving beyond rigid, random permutations.
- · AI content creators
- · Creative industries
- · AI model developers
- · Generative AI platforms
- · Generative AI models with rigid architectures
- · Users limited by inflexible image editing tools
More sophisticated and editable AI-generated images become accessible.
This could lead to new categories of personalized content and refined digital art.
The integration of such flexible models might accelerate the adoption of AI in niche creative fields currently requiring high manual intervention.
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Read at arXiv cs.AI