Contributing to SGLang Diffusion

Contributing to SGLang Diffusion#

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

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.

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”).

Performance Reporting#

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

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

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

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