Accelerate Serving Stable Diffusion by Optimizing the Inference Scheduler

Diffusion models generate images by iteratively refining random noise over a configurable number of inference steps, commonly around 30, but conventional serving groups requests into fixed batches, causing early requests to wait for batch formation and later requests to wait until the entire batch completes. Lightning’s proposed serving strategy instead dynamically adds new requests to an active batch at each diffusion step, independently tracks every request’s intermediate image and step state, and removes requests immediately when their generation is complete. Built with asynchronous tasks, request futures, shared state, and per-request identifiers, the approach aims to improve responsiveness while preserving efficient GPU utilization. Benchmarks on Lightning deployments using A10 and T4 GPUs, load-tested with Locust, reported speedups of 3–12.8% on A10 machines and 2–18.5% on T4 machines, with the largest improvements occurring at higher batch sizes.

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