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This is a specialized LoRA designed to generate binary segmentation masks for corneal endothelial cells. It produces high-contrast, strictly monochrome (black and white) images representing cell boundaries and structures.
Key Features:
Output: Binary Masks (Black background/White cells or vice versa).
Usage: Perfect for generating synthetic ground truth data for medical image segmentation tasks (e.g., training U-Net models) or creating procedural biological textures.
Workflow & ControlNet Integration: This model is highly effective for synthetic data generation when combined with ControlNet.
Creating Paired Datasets: You can use this LoRA to generate a binary mask first, and then feed that mask into ControlNet to guide my model "Medical SEM Style: Corneal Cells".
Recommended ControlNet Weight: 1.5
Result: This workflow produces perfectly aligned (Image, Label) pairs, which are essential for training segmentation networks (like U-Net) without manual annotation.
Recommended LoRA Weight: 1.5 Base Model: SD 1.5
Training Data & Configuration:
Dataset: 50 manually annotated masks of corneal endothelial photomicrographs.
Training Strategy: Robust training with 40 repeats per image.
Total Steps: 1,000 steps.
Batch Size: 2
Resolution: 512x512