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Profiling
Vladimir Mandic edited this page May 13, 2026
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SD.Next has built-in support for both Python and Torch profiling.
You can start profiling for the full session with the --profile command line flag.
You can also start and stop profiling on demand from UI -> System -> Start/Stop profiling.
When profiling is enabled, analysis runs:
- at the end of server startup, to identify startup bottlenecks
- at the end of each generation workflow
Warning
Collecting profile information may take significant resources and time
You can save a full torch profiling dump for external analysis by setting an environment variable with the output path:
SD_PROFILE_FOLDER=/tmp/profile
Each profiling run creates a JSON file in the specified folder. You can open it in tools such as:
- chrome://tracing/
- https://ui.perfetto.dev/
Warning
Each profile trace file is over 100MB in size
You can increase profiling detail with additional environment variables:
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SD_PROFILE_STACK=true: enable torch stack information -
SD_PROFILE_FLOPS=true: enable torch flops calculations -
SD_PROFILE_SHAPES=true: group torch profile information per each shape