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:<ul><li>Output: Binary Masks (Black background/White cells or vice versa).</li><li>Usage: Perfect for generating synthetic ground truth data for medical image segmentation tasks (e.g., training U-Net models) or creating procedural biological textures.</li><li>Workflow &amp; ControlNet Integration: This model is highly effective for synthetic data generation when combined with ControlNet.<ul><li>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".</li><li>Recommended ControlNet Weight: 1.5</li><li>Result: This workflow produces perfectly aligned (Image, Label) pairs, which are essential for training segmentation networks (like U-Net) without manual annotation.</li></ul></li></ul>Recommended LoRA Weight: 1.5 Base Model: SD 1.5Training Data &amp; Configuration:<ul><li>Dataset: 50 manually annotated masks of corneal endothelial photomicrographs.</li><li>Training Strategy: Robust training with 40 repeats per image.</li><li>Total Steps: 1,000 steps.</li><li>Batch Size: 2</li><li>Resolution: 512x512</li></ul>

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