diffusion-perf

You can profile diffusion models by running generation with specific flags to warmup and collect performance data, followed by executing a comparison script to analyze resource utilization and identify bottlenecks.

To identify bottlenecks in diffusion models, use performance benchmarking to measure inference speed and resource utilization. This process requires running generation with warmup flags and analyzing the collected performance data.

To compare diffusion model performance against a baseline, run the provided comparison script after collecting performance data through generation runs with warmup flags. This evaluates inference speed and resource utilization metrics against your baseline.

Yes, you can optimize resource utilization for a new diffusion model by profiling its execution. This involves running generation with warmup flags to collect performance data, which helps identify bottlenecks and facilitates inference speed optimization.

No external dependencies are required to run this benchmarking and profiling skill. It operates using included scripts and references to measure diffusion model performance, analyze resource utilization, and compare metrics against a baseline.