README.md

---
license: mit
library_name: diffusers
tags:
- diffusion
- ddpm
- retinal-fundus
- image-generation
---
# Model Card for ddpm-unet-retinal-fundus-image-generator


A **U-Net–based Denoising Diffusion Probabilistic Model (DDPM)** trained to generate **retinal fundus images**. This model can be used for synthetic medical image generation to augment datasets for training diagnostic models or other biomedical tasks.

---

## Model Architecture

- **Base**: [`UNet2DModel`](https://huggingface.co/docs/diffusers/main/en/api/models/unet2d)
- **Scheduler**: [`DDPMScheduler`](https://huggingface.co/docs/diffusers/main/en/api/schedulers/ddpm)
- **Resolution**: `128x128`
- **Channels**: `RGB (3)`
- **Attention**: Spatial self-attention in mid-resolution blocks
- **Framework**: [🤗 Diffusers](https://github.com/huggingface/diffusers) + PyTorch

---

## Dataset

- **Source**: [Kaggle - Retinal Fundus Images](https://www.kaggle.com/datasets/kssanjaynithish03/retinal-fundus-images)
- **Subset Used**: `train/Moderate Diabetic Retinopathy`
- **Preprocessing**:
  - Resized to `128x128`
  - Normalized to `[-1, 1]`
  - Random horizontal flip

---

## Training Configuration

| Setting                     | Value              |
|----------------------------|--------------------|
| Epochs                     | 35                 |
| Batch size                 | 16                 |
| Optimizer                  | AdamW              |
| Learning rate              | 1e-4               |
| Scheduler                  | Cosine w/ warmup   |
| Precision                  | Mixed (fp16)       |
| Diffusion Timesteps        | 1000               |
| Image Samples Saved        | Every 10 epochs    |

Training was done using 🤗 Accelerate and TensorBoard logging.

---

## How to Use

```python
from diffusers import DDPMPipeline
import torch
import matplotlib.pyplot as plt

pipeline = DDPMPipeline.from_pretrained("GS-23/ddpm-unet-retinal-fundus-image-generator")
images = pipeline(batch_size=1, generator=torch.manual_seed(0)).images

for img in images:
    plt.imshow(img)
    plt.axis("off")
    plt.show()
```

---

## 📌 Use Cases

- Data augmentation for diabetic retinopathy classifiers  
- Retinal pathology simulation and training  
- Medical generative AI research  
- Domain-specific image synthesis