sglang-diffusion-benchmark-profile

Benchmark diffusion inference latency by running standardized denoise latency tests across multiple presets. This automates test runs using Python and subprocess modules to measure model modifications accurately.

Benchmark diffusion denoise latency by running standardized tests across multiple presets. This automates test runs using Python and subprocess modules to measure model modifications accurately and reduce manual analysis time.

You need Python with torch and subprocess modules installed to run diffusion profiling workflows. These dependencies automate test runs and analyze traces to diagnose system efficiency bottlenecks.

Diagnose performance bottlenecks in diffusion pipelines by classifying hotspots against existing fast paths. This prepares specific kernel work for optimization teams to improve overall inference latency.

Use automated profiling for diffusion model optimization before attempting kernel enhancements. It measures the impact of model modifications on inference speed and visualizes performance bottlenecks to streamline diagnosis.