File size: 5,077 Bytes
800a79d 1914d9d 10cf102 800a79d 10cf102 800a79d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 |
---
pretty_name: MicroGen3D
tags:
- GenAI
- LDM
- 3d
- microstructure
- diffusion-model
- materials-science
- synthetic-data
- voxel
license: mit
datasets:
- microgen3D
language:
- en
---
# microgen3D
[](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**.
---
|