Create README.md

README.md

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+---
+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