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GS-23/
ddpm-unet-retinal-fundus-image-generator

Name: ddpm-unet-retinal-fundus-image-generator
Availability: InStock

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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

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