PathoGen

--- license: mit library_name: diffusers tags: - diffusion - inpainting - histopathology - medical-imaging - pathology - pytorch pipeline_tag: image-to-image --- # PathoGen - Histopathology Image Inpainting PathoGen is a diffusion-based model for histopathology image inpainting. It enables realistic tissue pattern generation for filling masked regions in pathology whole slide images (WSI). ## Model Description - **Model Type:** Diffusion model with custom attention processors - **Task:** Image inpainting for histopathology images - **Architecture:** UNet2DConditionModel with custom SkipAttnProcessor - **Framework:** PyTorch, Diffusers, PyTorch Lightning ## Usage ### Installation ```bash git clone https://github.com/mkoohim/PathoGen.git cd PathoGen pip install -r requirements.txt ``` ### Download Weights Download the attention weights and place them in your checkpoint directory: ```python from huggingface_hub import hf_hub_download # Download attention weights hf_hub_download( repo_id="mkoohim/PathoGen", filename="attention.pt", local_dir="./checkpoints" ) ``` ### Inference ```python from src.models.pathogen import PathoGenModel from omegaconf import OmegaConf from PIL import Image # Load configuration config = OmegaConf.load("configs/config.yaml") # Initialize model model = PathoGenModel(config) model.load_attention_weights("./checkpoints/attention.pt") model.eval() # Load images image = Image.open("your_wsi_crop.jpg") mask = Image.open("your_mask.jpg") condition = Image.open("your_source_image.jpg") # Run inference result = model(image, mask, condition) ``` ### Training ```bash python train.py ``` See the [GitHub repository](https://github.com/mkoohim/PathoGen) for full training instructions. ## Model Files | File | Description | Size | |------|-------------|------| | `attention.pt` | Trained attention module weights | ~190MB | ## Training Details - **Base Model:** Stable Diffusion Inpainting UNet - **Training Data:** Histopathology whole slide image crops - **Optimizer:** AdamW - **Learning Rate:** 1e-5 - **Precision:** Mixed precision (FP16) ## Intended Use This model is designed for: - Histopathology image inpainting and augmentation - Research in computational pathology - Data augmentation for pathology AI training ## Citation ```bibtex @misc{pathogen2025, title={PathoGen: Diffusion-Based Synthesis of Realistic L...

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