Cost-Aware Routing Framework Optimizes Text-to-Image Generation Efficiency
Researchers propose a framework that automatically routes text-to-image prompts to the most computationally appropriate generation method based on prompt complexity. The approach varies computational cost per prompt—using expensive high-step diffusion models for complex requests and faster distilled models for simpler ones—rather than applying uniform cost reduction across all inputs. This enables better quality-to-cost tradeoffs than existing uniform optimization techniques like distillation or quantization.
A new framework addresses the computational inefficiency of diffusion-based text-to-image generation by implementing intelligent routing that adapts processing cost to prompt complexity. Rather than uniformly reducing computational demands across all inputs through techniques like model distillation or quantization, the system learns to allocate expensive resources (100+ denoising steps) selectively to complex prompts while using lightweight alternatives for simpler requests. Empirical evaluation on COCO and DiffusionDB datasets demonstrates that routing across nine pre-trained text-to-image models achieves higher average quality than any single model alone. The approach represents an optimization strategy that preserves quality for demanding inputs while reducing unnecessary computation for straightforward generation tasks. Code has been made publicly available to support reproducibility and adoption.
What's missing
The paper does not discuss potential limitations of the routing mechanism, such as how accurately the framework identifies prompt complexity, failure modes when routing decisions are suboptimal, or computational overhead of the routing process itself. Additionally, comparison with other adaptive or conditional computation approaches in the literature is not detailed in the abstract.
What different sources said
- arXiv cs.LGCenter
Cost-Aware Routing for Efficient Text-To-Image Generation
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