Usage

Please first install the LakonLab v0.2.

We provide a Diffusers-style pipeline for AsymFLUX.2 klein. The example below loads the FLUX.2 klein Base 9B model, attaches the AsymFlow adapter, and generates an image directly in pixel space.

python

import math
import torch
from lakonlab.models.architectures import OklabColorEncoder
from lakonlab.models.diffusions.schedulers import FlowAdapterScheduler
from lakonlab.pipelines.pipeline_pixelflux2_klein import PixelFlux2KleinPipeline

pipe = PixelFlux2KleinPipeline.from_pretrained(
    'black-forest-labs/FLUX.2-klein-base-9B',
    vae=OklabColorEncoder(
        use_affine_norm=True,
        mean=(0.56, 0.0, 0.01),
        std=0.16),
    scheduler=FlowAdapterScheduler(
        shift=17.0,
        use_dynamic_shifting=True,
        base_seq_len=1024 ** 2,
        max_seq_len=2048 ** 2,
        base_logshift=math.log(17.0),
        max_logshift=math.log(34.0),
        dynamic_shifting_type='sqrt',
        base_scheduler='UniPCMultistep'),
    torch_dtype=torch.bfloat16)
adapter_name = pipe.load_lakonlab_adapter(  # you may later call `pipe.set_adapters([adapter_name, ...])` to combine other adapters (e.g., style LoRAs)
    'Lakonik/AsymFLUX.2-klein-9B-collection',
    subfolder='asymflux2_klein_9b_sft_zimage_turbo',
    target_module_name='transformer')
pipe = pipe.to('cuda')

# Text-to-image generation example
prompt = 'Restored color photo from the 1900s. A middle-aged man with cybernetic metal hands is sitting on an old wooden chair and reading the newspaper. The newspaper has the prominent headline "AsymFLOW RELEASED" in large bold font. Close-up shot focusing on the newspaper.'
neg_prompt = 'Low quality, worst quality, blurry, deformed, bad anatomy, unclear text'
out = pipe(
    prompt=prompt,
    negative_prompt=neg_prompt,
    width=960,
    height=1280,
    num_inference_steps=38,
    guidance_scale=4.0,
    generator=torch.Generator().manual_seed(42),
).images[0]
out.save('asymflux2_klein.png')

Citation

@article{chen2026asymmetric,
  title={Asymmetric Flow Models},
  author={Hansheng Chen and Jan Ackermann and Minseo Kim and Gordon Wetzstein and Leonidas Guibas},
  journal={arXiv preprint arXiv:2605.12964},
  url={https://arxiv.org/abs/2605.12964},
  year={2026},
}