Cost-Aware Routing Framework Optimizes Text-to-Image Generation Efficiency
A team of researchers has developed a routing framework that automatically directs text-to-image prompts to the most computationally appropriate generation model based on prompt complexity. Diffusion models, while capable of producing high-fidelity images, are computationally expensive due to their sequential denoising process, making uniform cost-reduction techniques like distillation or quantization suboptimal. The framework addresses this by reserving expensive, high-step generation only for complex prompts, potentially improving both average output quality and computational efficiency simultaneously.
The proposed framework, accepted by the journal Transactions on Machine Learning Research (TMLR), learns to route each text prompt to one of nine pre-trained text-to-image models, which may differ in the number of denoising steps used or represent entirely distinct model architectures. Rather than applying a blanket cost-reduction strategy, the system dynamically allocates computation based on prompt complexity, using lightweight or distilled models for simpler prompts and reserving 100+ denoising step pipelines for more demanding ones. Empirical evaluations on the COCO and DiffusionDB benchmarks show that the routing approach achieves higher average image quality than any single constituent model used alone. This contrasts with uniform techniques such as model quantization or knowledge distillation, which reduce cost across the board but may sacrifice quality on complex inputs. The authors have made their code publicly available, and the work represents a broader trend of adaptive inference strategies in generative AI to manage the growing computational demands of large models.
What's missing
It is unclear how the framework performs on out-of-distribution prompts beyond the COCO and DiffusionDB benchmarks, or how sensitive results are to the specific set of nine models chosen.
What different sources said
- arXiv cs.LGCenter
Cost-Aware Routing for Efficient Text-To-Image Generation
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