GenRouter: 面向智能体图像生成的统一工作流路由

摘要

文本到图像（T2I）生成模型的快速发展已有效解决了原始像素合成的根本性挑战，将研究界的关注重心转向满足日益复杂精细的用户需求。尽管近年来的智能体图像生成工作流通过外部知识检索和迭代推理等高级能力增强了静态推理，但这些工作流大多以固定的“一刀切”拓扑在相互隔离的孤岛中运行。这不可避免地导致了严重的计算失配，即简单查询被迫经过计算量庞大的流水线。为弥合这一差距，我们提出了GenRouter——首个面向智能体图像生成的统一工作流路由框架。我们首先构建了GenCanvas，将多种多样的智能体流水线标准化为一组通用的基础原语和可执行模板。在这一统一空间之上，GenRouter通过（i）需求剖析、（ii）经验匹配和（iii）帕累托过滤，自适应地将异构提示词路由至其最优工作流。在多个基准上的大量实验表明，与重量级静态流水线相比，GenRouter在实现更优视觉对齐的同时，将执行成本降低超过95%，延迟降低65%。此外，该系统通过持续积累经验实现自我进化，支持鲁棒的零样本泛化，从而提升性能并将计算开销减半。

English

The rapid evolution of text-to-image (T2I) generation models has effectively solved the foundational challenge of raw pixel synthesis, shifting the community's focus toward fulfilling increasingly intricate user requests. While recent agentic image generation workflows enhance static inference with advanced capabilities like external knowledge retrieval and iterative reasoning, they mostly operate in isolated silos with fixed ``one-size-fits-all" topologies. This inevitably leads to severe compute-mismatch, where simple queries are forced through computationally heavy pipelines. To bridge this gap, we present GenRouter, the first unified workflow routing framework for agentic image generation. We first formulate GenCanvas, standardizing diverse agentic pipelines into a universal set of foundational primitives and executable templates. Operating over this unified space, GenRouter adaptively routes heterogeneous prompts to their optimal workflows via (i) demand profiling, (ii) experience matching, and (iii) Pareto filtering. Extensive experiments across diverse benchmarks demonstrate that GenRouter achieves superior visual alignment while reducing execution costs by over 95% and latency by 65% compared to heavyweight static pipelines. Furthermore, the system continuously self-evolves via accumulated experience, enabling robust zero-shot generalization that boosts performance and halves computational overhead.