Covid XRay Diffusion Model
This is a diffusion model designed for generating synthetic COVID-19 X-ray images. The model takes random noise as input and iteratively denoises it to produce realistic X-ray images. Used to generate synthetic xray...
Model source
- Kind
- Other
- File size
- 55.0 MB
- Version
- vRevision a3b1d3f
- Updated
- Oct 20, 2026
- Downloads
- 0
- Source
- Hugging Face
Source description
Diffusion Model for COVID-19 X-ray Generation
This is a diffusion model designed for generating synthetic COVID-19 X-ray images. The model takes random noise as input and iteratively denoises it to produce realistic X-ray images. Used to generate synthetic xray image for scarce COVID-19 positive cases, which can be used for data augmentation in training diagnostic models.
Training data from https://data.mendeley.com/datasets/9xkhgts2s6/4 Full project file at https://github.com/teohyc/covid_xray_diffusion
##Usage
from diffusers import DDPMPipeline
import matplotlib.pyplot as plt
# Load the pipeline
pipeline = DDPMPipeline.from_pretrained("teohyc/Covid-XRay-Diffusion-Model")
# Generate a synthetic X-ray
image = pipeline(num_inference_steps=500).images[0] #default is 1000 steps, but you can reduce it for faster generation (at the cost of quality)
# Display
plt.imshow(image, cmap='gray')
plt.axis('off')
plt.show()
Sources
1 source
Sources
1 sourceVerified Oct 20
Model artifacts
1 artifact
Model artifacts
1 artifactunet/diffusion_pytorch_model.safetensors
safetensors · 55.0 MB · SHA-256 727ea9bd341c…757c · Hugging Face
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3 excerpts
Source excerpts
3 excerpts--- tags: - Lung - Pneumonia - Covid-19 - PyTorch license: mit language: - en pipeline_tag: unconditional-image-generation library_name: diffusers --- --- # Diffusion Model for COVID-19 X-ray Generation This is a diffusion model designed for generating synthetic COVID-19 X-ray images. The model takes random noise as input and iteratively denoises it to produce realistic X-ray images. Used to generate synthetic xray image for scarce COVID-19 positive cases, which can be used for data augmentation in training diagnostic models. Training data from https://data.mendeley.com/datasets/9xkhgts2s6/4 Full project file at https://github.com/teohyc/covid_xray_diffusion ##Usage ```python from diffusers import DDPMPipeline import matplotlib.pyplot as plt # Load the pipeline pipeline = DDPMPipeline.from_pretrained("teohyc/Covid-XRay-Diffusion-Model") # Generate a synthetic X-ray image = pipeline(num_inference_steps=500).images[0] #default is 1000 steps, but you can reduce it for faster generation (at the cost of quality) # Display plt.imshow(image, cmap='gray') plt.axis('off') plt.show() ```
Source context: 0 downloads · 0 likes · Pipeline unconditional-image-generation · Library diffusers · Repo teohyc/Covid-XRay-Diffusion-Model
Source context: 1 downloads · 0 likes · Pipeline unconditional-image-generation · Library diffusers · Repo teohyc/Covid-XRay-Diffusion-Model
