AI画图有了调度员：简单需求不再走冤枉路
现在的AI画图工具，不管你的需求多简单，都得走一遍完整流程——查资料、反复推理、生成。这就像你只想买瓶水，却被迫逛完整座商场。GenRouter给这些流程装了个调度员：先判断你的需求有多复杂，再匹配最合适的路径，最后过滤掉不划算的选项。结果是在保持画质的同时，执行成本降了95%，延迟降了65%。它还会从每次任务中积累经验，越用越聪明。这不是你明天就能用的功能，但它是AI画图从“能画”走向“会省”的关键一步。
📄 原文摘要(英文)
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.