D i F low : A System for Micro-Serving Text-to-image Di ffusion Work flows

Text-to-image generation executes a diffusion workflow comprising multiple models centered on a base diffusion model. Existing serving systems treat each workflow as an opaque monolith, provisioning, placing, and scaling all constituent models together, which obscures internal dataflow, prevents model sharing, and enforces coarse-grained resource management. In this paper, we make a case for micro-serving diffusion workflows with DiFlow, a system that decomposes a workflow into loosely coupled model-execution nodes that can be independently managed and scheduled. By explicitly managing individual model inference, DiFlow unlocks cluster-scale optimizations, including per-model scaling, model sharing, and adaptive model parallelism. Collectively, DiFlow outperforms existing diffusion workflow serving systems, sustaining up to 3× higher request rates and tolerating up to 8× higher burst traffic. We have open-sourced DiFlow at https://github.com/diflow-project/diflow.