metadata
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 - Scheduler:
DDPMScheduler - Resolution:
128x128 - Channels:
RGB (3) - Attention: Spatial self-attention in mid-resolution blocks
- Framework: 🤗 Diffusers + PyTorch
Dataset
- Source: Kaggle - Retinal Fundus Images
- Subset Used:
train/Moderate Diabetic Retinopathy - Preprocessing:
- Resized to
128x128 - Normalized to
[-1, 1] - Random horizontal flip
- Resized to
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
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