A LoRA adapter for FLUX.2 [klein] 4B that sharpens soft and salon-curl hair geometry,
trained as part of Beenga Image.
Read the limitations before using this. The adapter works, and it leaks. Both halves
are documented below, because the leak is the reason it is opt-in rather than default in
the product it was built for.
Adapter
LoRA, rank 32, alpha 32
Base model
black-forest-labs/FLUX.2-klein-4B (Apache 2.0)
Checkpoint
step 500 of a 1500-step run — see Which checkpoint below
1import torch
2from diffusers import Flux2KleinPipeline
34pipe = Flux2KleinPipeline.from_pretrained(5"black-forest-labs/FLUX.2-klein-4B", torch_dtype=torch.bfloat16,6).to("cuda")78pipe.load_lora_weights(9"beenga8/beenga-curl-v1",10 weight_name="beenga_curl_v1.safetensors",11 adapter_name="curl",12)1314image = pipe(15"a young indian woman with soft salon curls, rooftop in Delhi",16 num_inference_steps=4,17).images[0]
Always pass weight_name explicitly. If you run with HF_HUB_OFFLINE=1 or
local_files_only=True — which you may well need to, on serverless workers that cannot
reach the Hub during setup — the loader will not guess a filename and raises
ValueError: When using the offline mode, you must specify a 'weight_name' before it
ever looks at the path. A valid local file fails with a message that does not say so.
This cost us a shipped feature that failed on 100% of calls; the explicit form is correct
either way.
To unload:
pipe.unload_lora_weights()
What it does
Asked for curls, it renders better curl geometry than the base model. Explicit control in
the other direction survives intact: ask for pin-straight hair and it renders pin-straight
hair.
What it does wrong
This is the important section.
Unspecified hair drifts curly. If the prompt says nothing about hair, the adapter
pushes toward curls anyway. It changes a default you did not ask it to change.
The training set's look bleeds into unrelated scenes. Plainer backgrounds, more
ordinary faces, deeper complexions, flatter and more neutral expressions — applied
regardless of what the prompt asked for.
The cause is the dataset, not the training run. All 200 captions came from a single
template and a single generator, with no contrast examples — nothing straight, tight,
coily or glamorous. The adapter had no way to learn that curl geometry is separable from
everything else in the frame, so it learned "curls" and "this visual style" as one thing.
If you use this, use it deliberately, on prompts where curls are the subject. In Beenga
Image it is exposed as an opt-in flag that is off by default, for exactly this reason.
Which checkpoint
Checkpoints were saved at steps 500, 1000 and 1500. Step 500 is published here because
it is the best of the three; step 1500 is visibly overtrained.
Stated plainly because it matters to anyone comparing: the Beenga Image production image
currently serves the step-1500 file, which is a packaging mistake — the final checkpoint
was saved under the plain output name and that is the name the predictor loads. It is
recorded in the project's model card and will be corrected on the next rebuild. The file
in this repository is step 500, verified from its own training_info metadata
({"step": 500, "epoch": 0}) and by the SHA-256 above.
Training
Steps
500 (published) of 1500 run
Optimiser
adamw8bit, lr 1e-4
Scheduler
flowmatch
Precision
bf16, quantised
Resolutions
512 / 768 / 1024
Hardware
1× NVIDIA A40 48GB, ~35 min for the full 1500-step run
Training data
Source
Share
Licence
Z-Image Turbo generations
100%
Apache 2.0, no output restriction
Real photographs
0%
—
200 images, entirely synthetic. No scraped data, no stock imagery, no images of
identifiable real people, no user-contributed photographs. Generation recipes are in
datasets/recipes.mjs.
Evaluation
Assessed by human judgement against the Beenga Image benchmark suites, with fixed seeds so
differences are attributable to the adapter rather than sampling noise. Scoring for hair
geometry is manual and therefore subjective and not reproducible — a real limitation, and
the same one that applies to every axis of that project except complexion, which is
measured by script.
Licence
Apache 2.0, matching the base model. Use of FLUX.2 [klein] 4B is also subject to Black
Forest Labs' Out-of-Scope Use policy, which binds conduct separately from the copyright
licence.
Check the variant before substituting. As of 2026-08-16, Black Forest Labs publishes
the 4B models under Apache 2.0 and the 9B models under the FLUX Non-Commercial
License v2.1. The names differ by two characters and both live in the same
organisation.
This adapter introduces no safety or moderation capability of its own.
Beenga™ is a trademark of Beenga. Apache 2.0 grants no trademark rights.
