README.md

---
pretty_name: MicroGen3D
tags:
  - GenAI
  - LDM
  - 3d
  - microstructure
  - diffusion-model
  - materials-science
  - synthetic-data
  - voxel
license: mit
datasets:
  - microgen3D
language:
  - en
---


# microgen3D

[![Code](https://img.shields.io/badge/GitHub-Code-black?logo=github)](https://github.com/baskargroup/MicroGen3D)

## Dataset Summary

**microgen3D** is a dataset of 3D voxelized microstructures designed for training, evaluation, and benchmarking of generative models—especially Conditional Latent Diffusion Models (LDMs). It includes both synthetic (Cahn-Hilliard) and experimental microstructures with multiple phases (2 to 3). The voxel grids range from `64³` up to `128×128×64`.

The dataset consists of three microstructure types:
- **Experimental microstructures**
- **2-phase Cahn-Hilliard microstructures**
- **3-phase Cahn-Hilliard microstructures**

The two Cahn-Hilliard datasets are thresholded versions of the same simulation source. For each dataset type, we also provide pretrained generative model weights, comprising:
- `vae.ckpt` – Variational Autoencoder
- `fp.ckpt` – Feature Predictor
- `ddpm.ckpt` – Denoising Diffusion Probabilistic Model

---

## 📁 Repository Structure

```
microgen3D/
├── data/
│   └── sample_data.h5                 # Experimental or synthetic HDF5 microstructure file
├── models/
│   └── weights/
│       ├── experimental/
│       │   ├── vae.ckpt
│       │   ├── fp.ckpt
│       │   └── ddpm.ckpt
│       ├── two_phase/
│       └── three_phase/
└── ...
```

---

## 🚀 Quick Start

### 🔧 Setup Instructions

```bash
# 1. Clone the repo
git clone https://github.com/baskargroup/MicroGen3D.git
cd MicroGen3D

# 2. Set up environment
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Download dataset and weights (Hugging Face)
# Make sure HF CLI is installed and you're logged in: `huggingface-cli login`
```

```python
from huggingface_hub import hf_hub_download

# Download sample data
hf_hub_download(repo_id="BGLab/microgen3D", filename="sample_data.h5", repo_type="dataset", local_dir="data")

# Download model weights
hf_hub_download(repo_id="BGLab/microgen3D", filename="vae.ckpt", local_dir="models/weights/experimental")
hf_hub_download(repo_id="BGLab/microgen3D", filename="fp.ckpt", local_dir="models/weights/experimental")
hf_hub_download(repo_id="BGLab/microgen3D", filename="ddpm.ckpt", local_dir="models/weights/experimental")
```

## ⚙️ Configuration

### Training Config (`config.yaml`)
- **task**: Auto-generated if left null  
- **data_path**: Path to training dataset (`../data/sample_train.h5`)  
- **model_dir**: Directory to save model weights  
- **batch_size**: Batch size for training  
- **image_shape**: Shape of the 3D images `[C, D, H, W]`  

#### VAE Settings:
- `latent_dim_channels`: Latent space channels size.  
- `kld_loss_weight`: Weight of KL divergence loss  
- `max_epochs`: Training epochs  
- `pretrained`: Whether to use pretrained VAE  
- `pretrained_path`: Path to pretrained VAE model  

#### FP Settings:
- `dropout`: Dropout rate  
- `max_epochs`: Training epochs  
- `pretrained`: Whether to use pretrained FP  
- `pretrained_path`: Path to pretrained FP model  

#### DDPM Settings:
- `timesteps`: Number of diffusion timesteps  
- `n_feat`: Number of feature channels for Unet. Higher the channels more model capacity. 
- `learning_rate`: Learning rate  
- `max_epochs`: Training epochs  

### Inference Parameters (`params.yaml`)
- **data_path**: Path to inference/test dataset (`../data/sample_test.h5`)  

#### Training (for model init only):
- `batch_size`, `num_batches`, `num_timesteps`, `learning_rate`, `max_epochs`  : Optional parameters

#### Model:
- `latent_dim_channels`: Latent space channels size.  
- `n_feat`: Number of feature channels for Unet.
- `image_shape`: Expected image input shape  

#### Attributes:
- List of features/targets to predict:
  - `ABS_f_D`
  - `CT_f_D_tort1`
  - `CT_f_A_tort1`

#### Paths:
- `ddpm_path`: Path to trained DDPM model  
- `vae_path`: Path to trained VAE model  
- `fc_path`: Path to trained FP model  
- `output_dir`: Where to store inference results  

## 🏋️ Training

Navigate to the training folder and run:
```bash
cd training
python training.py
```

## 🧠 Inference

After training, switch to the inference folder and run:
```bash
cd ../inference
python inference.py
```

---

## 📜 Citation

If you use this dataset or models, please cite:

```
@article{baishnab2025microgen3d,
  title={3D Multiphase Heterogeneous Microstructure Generation Using Conditional Latent Diffusion Models},
  author={Baishnab, Nirmal and Herron, Ethan and Balu, Aditya and Sarkar, Soumik and Krishnamurthy, Adarsh and Ganapathysubramanian, Baskar},
  journal={arXiv preprint arXiv:2503.10711},
  year={2025}
}
```

---

## ⚖️ License

This project is licensed under the **MIT License**.

---