This is the Linux app named Denoising Diffusion Probabilistic Model whose latest release can be downloaded as 1.9.4sourcecode.zip. It can be run online in the free hosting provider OnWorks for workstations.
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SCREENSHOTS:
Denoising Diffusion Probabilistic Model
DESCRIPTION:
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
Programming Language
Python
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/denoising-diff-probabil.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.