mflux-testing

This repo uses pytest with image-producing tests. Always preserve outputs for inspection and never update reference images unless explicitly asked.

When to Use

• You need to run tests (fast/slow/full) or debug failing tests.
• There are image/golden mismatches and you need to report paths/output for review.

Instructions

• Prefer the justfile test recipes:
  • just test-fast (fast tests, no image generation)
  • just test-slow (slow tests, image generation)
  • just test (default selection, skips slow model tests)
  • just test-all (all except high-memory tests; slow tests download model weights)
• Always keep MFLUX_PRESERVE_TEST_OUTPUT=1 on test runs (already built into the justfile test recipes).
• If a change affects defaults, config resolution, metadata fields, or CLI behavior, add or update tests that cover the changed behavior directly instead of relying only on manual verification.
• If tests fail:
  • Summarize the failing test names and the key assertion output.
  • Point to any generated images/artifacts on disk for manual review.
• Do not regenerate/replace reference (“golden”) images unless the user explicitly requests it.

Updating golden images (new model or hardware refresh)

Golden tests compare generated PNGs to tests/resources/reference_*.png (typically 15% mismatch threshold).

When to update (only with explicit user approval):

• After validating the port on target hardware (CI Mac) via slow tests with MFLUX_PRESERVE_TEST_OUTPUT=1
• After choosing a stable prompt/seed/settings via diffusers comparison and/or latent-injection confidence (mflux-debugging)
• When old references used a bad prompt (e.g. ambiguous subject) or wrong seed for mflux’s RNG

Workflow:

1. Run slow test → inspect tests/resources/output_*.png vs reference_*.png
2. If output is correct but reference is stale, re-run generation with same test parameters and replace reference PNGs
3. Commit test + reference images together with a clear message (e.g. test(<model>): update golden images for local hardware)

Important: Golden tests lock mflux-native sampling (mx.random + mflux schedulers), not diffusers pixel parity. A good diffusers side-by-side or injected-latent run builds confidence in the model code; the golden still reflects mflux’s full recipe on CI hardware.

Manual validation (config resolution + local model paths)

Use when a change touches model config resolution, mflux-save, or the model’s generate CLI, or when a PR fixes local model-path handling for the model under investigation. Refer to the mflux-cli skill to find the correct generate command for the model you are testing.

• Run a local-path quantize/save:
  • Use the mflux-cli skill to look up the correct command and flags.
  • Verify CLI usage with the command’s --help before running it.
  • Save to a known location (e.g., Desktop) to make follow-up steps explicit.
• Run generation from the saved model using the correct model-specific generate CLI:
  • Use the mflux-cli skill to find the generate command and required flags.
  • Verify CLI usage with the command’s --help before running it.
• If the model has multiple size variants, repeat the above for each variant to confirm the correct overrides are applied.
• Do not commit output artifacts; delete or leave them untracked.