| --- |
| 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 Lesions in Histopathology Images}, |
| author={mkoohim}, |
| year={2025}, |
| url={https://huggingface.co/mkoohim/PathoGen} |
| } |
| ``` |
|
|
| ## License |
|
|
| This model is released under the MIT License. |
|
|
| ## Links |
|
|
| - **GitHub:** [https://github.com/mkoohim/PathoGen](https://github.com/mkoohim/PathoGen) |
| - **Hugging Face:** [https://huggingface.co/mkoohim/PathoGen](https://huggingface.co/mkoohim/PathoGen) |
|
|
|
|
