---
library_name: diffusers
datasets:
- NNNan/UniEM-3M
base_model:
- stabilityai/stable-diffusion-xl-base-1.0
pipeline_tag: text-to-image
tags:
- materials
- microstructure
- electron_micrograph
- characterization
---

# UniEM-Gen

<!-- Provide a quick summary of what the model is/does. -->

## 📘 Model Summary  
This is the text-to-image diffusion model trained on the complete **[UniEM-3M](https://huggingface.co/datasets/NNNan/UniEM-3M)** dataset.  
It is designed for **electron microscopy (EM)-style image generation**, enabling:  
- Scientific data augmentation  
- Proxy generation for microstructural distributions  
- Multimodal research in materials science

---

## 🚀 Usage Example  

### Using `diffusers`
```python
from diffusers import StableDiffusionPipeline
import torch

# Load model from Hugging Face
model_id = "NNNan/UniEM-Gen"
pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
pipe = pipe.to("cuda")

# Example prompt, sampled from UniEM-3M.
prompt = "SEM of Ceramic Powder: A mix of plate-like and rod-shaped particles. nanostructured. high density. densely packed and agglomerated. Multilayer. Wide range of particle sizes. Grayscale particles on a dark background."

# Generate image
image = pipe(prompt).images[0]

# Save or display
image.save("generated_em.png")
image.show()
```
---

## 🖼️ Example Outputs  

![Generated Example](examples/figure1.png)