GenRouter: Unified Workflow Routing for Agentic Image Generation
GenRouter optimizes image generation workflows, reducing computational costs by 95%.
Key Findings
Methodology
GenRouter standardizes image generation workflows through GenCanvas, decomposing complex tasks into foundational primitives. It achieves dynamic routing via demand profiling, experience matching, and Pareto filtering, integrating external knowledge retrieval and iterative reasoning to enhance efficiency.
Key Results
- GenRouter reduces execution costs by 95% and latency by 65% across multiple benchmarks while maintaining excellent visual alignment.
- In the WISE benchmark, GenRouter achieved a performance of 0.88 with a cost of only $1.57.
- In the GenEval2 benchmark, GenRouter's performance was 71.6%, significantly outperforming static pipelines.
Significance
GenRouter addresses the computational mismatch in image generation, significantly reducing costs and latency. Its modular design provides a robust foundation for diverse applications, advancing intelligent image generation systems.
Technical Contribution
GenRouter introduces GenCanvas, a unified workflow space that standardizes execution paradigms, achieving dynamic optimization through a self-evolving router. This approach offers greater flexibility and efficiency compared to existing static systems.
Novelty
GenRouter is the first to apply dynamic routing to image generation, overcoming the limitations of traditional static pipelines and achieving zero-shot generalization through experience-driven self-evolution.
Limitations
- GenRouter may still require substantial computational resources for extremely complex multi-constraint tasks.
- The system's self-evolution relies on extensive historical data, potentially underperforming during cold starts.
Future Work
Future research could explore GenRouter's application in other generative tasks like text and video generation, and optimize its performance in resource-constrained environments.
AI Executive Summary
The rapid evolution of image generation has shifted focus to meeting complex user requests. Traditional static inference systems often suffer from computational mismatch, where simple queries are forced through complex pipelines, wasting resources and increasing latency. To address this, the research team introduced GenRouter, a unified workflow routing framework for agentic image generation.
GenRouter standardizes workflows through GenCanvas, decomposing complex tasks into foundational primitives and achieving dynamic routing via demand profiling, experience matching, and Pareto filtering. Experiments show that GenRouter significantly reduces computational costs and latency while maintaining excellent visual alignment across multiple benchmarks.
The modular design of this system not only enhances generation efficiency but also provides a robust foundation for diverse applications. In the future, GenRouter is expected to play a crucial role in other generative tasks like text and video generation, further advancing intelligent generation systems.
Deep Analysis
Background
Recent advancements in deep learning have significantly improved image generation models. However, as user demands become more complex, traditional static inference systems struggle with complex tasks. Existing research often focuses on developing specific functionalities, lacking a unified framework to integrate diverse capabilities.
Core Problem
Current image generation systems commonly face computational mismatch, where simple queries are forced through complex pipelines, wasting resources and increasing latency. Solving this issue is crucial for improving generation efficiency and reducing computational costs.
Innovation
GenRouter's core innovation lies in introducing GenCanvas, a standardized workflow space that decomposes complex tasks into foundational primitives, achieving dynamic routing. This approach not only improves generation efficiency but also provides a robust foundation for diverse applications.
Methodology
- �� GenCanvas standardizes image generation workflows, decomposing tasks into foundational primitives.
- �� Achieves dynamic routing via demand profiling, experience matching, and Pareto filtering.
- �� The system self-evolves to achieve zero-shot generalization, continuously optimizing performance.
Experiments
Experiments were conducted across multiple benchmarks, including WISE, DPG-Bench, and GenEval2. Generators used include Z-Image-Turbo and Qwen-Image-2512. Evaluation metrics include performance, execution cost, and latency.
Results
Experimental results show that GenRouter significantly reduces computational costs and latency while performing excellently across multiple benchmarks. Compared to static pipelines, GenRouter demonstrates significant improvements in visual alignment and generation efficiency.
Applications
GenRouter can be applied to various image generation tasks, such as artistic creation, advertisement design, and virtual reality. Its modular design allows it to adapt to different application scenarios, significantly improving generation efficiency.
Limitations & Outlook
Although GenRouter performs excellently in many aspects, it may still require substantial computational resources for extremely complex multi-constraint tasks. Additionally, the system's self-evolution relies on extensive historical data, potentially underperforming during cold starts.
Plain Language Accessible to non-experts
Imagine an intelligent kitchen where GenRouter acts as a smart chef assistant. It automatically selects the most suitable cooking tools and steps based on different recipe demands. Sometimes, it just needs simple stirring, while other times it requires complex seasoning and plating. In this way, GenRouter greatly improves cooking efficiency, reducing unnecessary time and resource waste.
ELI14 Explained like you're 14
Hey there! Imagine you're playing a super cool game called 'Image Generation Master.' In this game, you need to choose the best tools and strategies for different tasks. GenRouter is like your super assistant, helping you complete tasks in the shortest time and score the highest points! Isn't that awesome?
Glossary
GenRouter
A dynamic routing framework for agentic image generation, achieving optimal workflow selection through demand profiling and experience matching.
Used to optimize image generation workflows, reducing computational costs.
GenCanvas
A standardized workflow space that decomposes complex tasks into foundational primitives, achieving modular design.
Provides a unified workflow foundation for GenRouter.
Pareto Filtering
A method for selecting optimal configurations by comparing the multi-dimensional performance of different plans.
Used in dynamic routing to select optimal workflows.
Demand Profiling
Analyzing task demands to extract task features for guiding workflow selection.
Used in GenRouter's dynamic routing decisions.
Experience Matching
Utilizing historical execution records to predict the overall utility of candidate plans, optimizing routing selection.
Enhances GenRouter's self-evolution capability.
Open Questions Unanswered questions from this research
- 1 How to optimize GenRouter's performance in resource-constrained environments?
- 2 How to reduce computational resource requirements for GenRouter when handling extremely complex tasks?
Applications
Immediate Applications
Advertisement Design
Advertising companies can use GenRouter to quickly generate high-quality ad images, saving time and costs.
Virtual Reality
VR developers can use GenRouter to generate realistic virtual scenes, enhancing user experience.
Long-term Vision
Intelligent Creation
In the future, GenRouter may become the core of intelligent creation tools, promoting the automation and personalization of artistic creation.
Abstract
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.