Download
Implementation of Denoising Diffusion Probabilistic Model in Pytorch. It is a new approach to generative modeling that may have the potential to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution. If you simply want to pass in a folder name and the desired image dimensions, you can use the Trainer class to easily train a model.
Features
- Annotated code by Research Scientists
- This implementation was transcribed from the official Tensorflow version
- Samples and model checkpoints will be logged to ./results periodically
- The Trainer class is now equipped with Accelerator
- You can easily do multi-gpu training in two steps
- A new approach to generative modeling
Project Samples
Categories
Machine LearningLicense
MIT LicenseFollow Denoising Diffusion Probabilistic Model
Other Useful Business Software
Build Securely on Azure with Proven Frameworks
Moving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
Rate This Project
Login To Rate This Project
User Reviews
Be the first to post a review of Denoising Diffusion Probabilistic Model!
