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This guide outlines the requirements for contributing to the SGLang Diffusion module (sglang.multimodal_gen).

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Contributor Guides

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On AI-Assisted (“Vibe Coding”) PRs

Vibe-coded PRs are welcome — we judge code quality, not how it was produced. The bar is the same for all PRs:
  • No over-commenting. If the name says it all, skip the docstring.
  • No over-catching. Don’t guard against errors that virtually never happen in practice.
  • Test before submitting. AI-generated code can be subtly wrong — verify correctness end-to-end.

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Commit Message Convention

We follow a structured commit message format to maintain a clean history. Format: 
[diffusion] <scope>: <subject>
Examples:
  • [diffusion] cli: add --perf-dump-path argument
  • [diffusion] scheduler: fix deadlock in batch processing
  • [diffusion] model: support Stable Diffusion 3.5
Rules:
  • Prefix: Always start with [diffusion].
  • Scope (Optional): cli, scheduler, model, pipeline, docs, etc.
  • Subject: Imperative mood, short and clear (e.g., “add feature” not “added feature”).

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Performance Reporting

For PRs that impact latency, throughput, or memory usage, you should provide a performance comparison report.

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How to Generate a Report

  1. Baseline: run the benchmark (for a single generation task) 
    $ sglang generate --model-path <model> --prompt "A benchmark prompt" --perf-dump-path baseline.json
  2. New: run the same benchmark, without modifying any server_args or sampling_params 
    $ sglang generate --model-path <model> --prompt "A benchmark prompt" --perf-dump-path new.json
  3. Compare: run the compare script, which will print a Markdown table to the console 
    $ python python/sglang/multimodal_gen/benchmarks/compare_perf.py baseline.json new.json [new2.json ...]
### Performance Comparison Report
...
  4. Paste: paste the table into the PR description

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CI-Based Change Protection

Consider adding tests to the pr-test or nightly-test suites to safeguard your changes, especially for PRs that:
  • support a new model
    • add a testcase for this new model to testcase_configs.py
  • support or fix important features
  • significantly improve performance
Please run the according testcase, then update/add the baseline to perf_baselines.json by following the instruction in console if applicable. See test for examples