b4ph / independent research

PILOT / FUNCTIONAL TESTING

Blackwell Diffusers Lab

A working notebook for FLUX.2 Klein image-to-image LoRA caption dropout. Inspect each execution, its limits, and the evidence behind it.

Read the record.
Keep the failed runs.
Make it reproducible.

Scope, before results

Bounded functional tests of a real pretrained model on programmatic image fixtures. Loss and memory are diagnostics. They do not establish training quality, steady-state performance, or a speedup.

Read the method ↓

Checking the evidence package…

METHOD & LIMITS

What this evidence can support

A functional pilot

The question is whether the bounded training and adapter checks execute under the recorded configuration. Programmatic image fixtures exercise the code path; they do not evaluate image quality or establish a useful fine-tune.

Only observed loss points are drawn. Missing measurements remain missing, with an explanation. Memory uses a zero baseline and the selected run's own scale. Time and memory are descriptive diagnostics; no cross-run ranking, percentage improvement, or performance comparison is calculated.

Trace a value to its source

Open a numeric value to inspect its raw JSON evidence. The metric table includes the exact traversal key. Each run also exposes the validated record, original artifact hashes, exact model/source revisions, configuration and dependency versions.

A passing data audit checks the supplied records. It is not a release approval, a reproduction by another machine, or maintainer acceptance. Independently review the commands, licenses and claims before redistributing this package.

Why there is no model or benchmark dataset here

The artifact is a reproduction and test-harness notebook. Model checkpoints and LoRA adapter weights are deliberately withheld. A small functional pilot does not justify a benchmark dataset or a general-purpose fine-tune claim. Public evidence downloads contain text records and reproduction code, not model weights.

REPRODUCTION

Take the experiment apart.

Use the per-run configuration and pinned revisions. Restore each downloaded file to its recorded relative path in a clean workspace. Review the scripts before running them; GPU execution and model downloads happen on your machine.

Upstream coordination

Diffusers issue #13762 ↗

No PR opened. Maintainer acknowledgment and human-authored wording are pending.

This notebook is independent work. The issue discussion and earlier proposal by chinmayirm are prior art, not an endorsement of this implementation.