BachNgoH/flux2-klein-4b-overlap-ownership-lora

FLUX.2 Klein 4B — Overlap-Ownership LoRA

LoRA adapter fine-tuned on FLUX.2-klein-base-4B for count-preserving dense image generation with overlap-ownership attention zones.

Model Details

• —Base model: black-forest-labs/FLUX.2-klein-base-4B
• —LoRA rank: 16, alpha: 32
• —Target modules: tok, tov, toq, toout.0, toqkvmlp_proj
• —Training steps: 2000
• —Training framework: PEFT + diffusers

Training Configuration

• —Effective batch size: 4 (bs=1 x grad_accum=4)
• —Learning rate: 5e-5
• —Optimizer: AdamW
• —Precision: bf16
• —Features: GLIGEN-style layout attention + AIBL loss + Overlap-Ownership zones

Overlap-Ownership Zones (Idea 7)

Three-zone classification for overlapping bboxes:

• —Winner zone: pixels owned by one bbox (strong positive bias)
• —Contested zone: pixels claimed by multiple bboxes (moderate positive bias)
• —Loser zone: pixels occluded by other bboxes (reduced bias)

Evaluation Results (1000 samples, val split)

Usage

from diffusers import Flux2KleinPipeline
from peft import PeftModel

pipe = Flux2KleinPipeline.from_pretrained("black-forest-labs/FLUX.2-klein-base-4B")
pipe.transformer = PeftModel.from_pretrained(pipe.transformer, "BachNgoH/flux2-klein-4b-overlap-ownership-lora")

Framework Versions

• —PEFT 0.18.1
• —Diffusers
• —PyTorch 2.x