LegoDiffusion: Micro-Serving Text-to-Image Diffusion Workflows
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 LegoDiffusion, 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, LegoDiffusion unlocks cluster-scale optimizations, including per-mode...
| Authors | Lingyun Yang, Suyi Li, Tianyu Feng et al. |
|---|---|
| Primary category | cs.DC |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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