Unconditional Image Generation
Diffusers
Safetensors
English
DDPMPipeline
Lung
Pneumonia
Covid-19
PyTorch
Instructions to use teohyc/Covid-XRay-Diffusion-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use teohyc/Covid-XRay-Diffusion-Model with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("teohyc/Covid-XRay-Diffusion-Model", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
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# Diffusion Model for COVID-19 X-ray Generation
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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.
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Used to generate synthetic xray image for scarce COVID-19 positive cases, which can be used for data augmentation in training diagnostic models.
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tags:
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- Lung
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- Pneumonia
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- Covid-19
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- PyTorch
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# Diffusion Model for COVID-19 X-ray Generation
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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.
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Used to generate synthetic xray image for scarce COVID-19 positive cases, which can be used for data augmentation in training diagnostic models.
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