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GenRouter: Unified Workflow Routing for Agentic Image Generation
Abstract
GenRouter is a unified routing framework that adaptively directs prompts to optimal agentic image-generation workflows, cutting costs and latency while improving visual alignment and enabling continuous self-evolution.
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.
Community
GenCanvas: The first unified workflow space that standardizes the execution paradigm of agentic image generation. It systematically deconstructs the generative process into universal foundational primitives (e.g., search, reason, verify, and sketch) and establishes a scalable library of workflow templates.
GenRouter: A dynamic, self-evolving workflow router driven by demand profiling, memory-guided utility matching, and Pareto filtering. It seamlessly pairs diverse heterogeneous prompts with optimal execution plans to balance visual performance and computational cost.
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Cite arxiv.org/abs/2608.16721 in a model README.md to link it from this page.
Cite arxiv.org/abs/2608.16721 in a dataset README.md to link it from this page.
Cite arxiv.org/abs/2608.16721 in a Space README.md to link it from this page.
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