Shypd.aiThe largest open index of AI toolsdiffusion is an open-source tool that implements Denoising Diffusion Probabilistic Models. It supports machine learning experiments and requires TensorFlow 1.15 and Python 3.5.
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diffusion is an open-source implementation of Denoising Diffusion Probabilistic Models, developed by Jonathan Ho, Ajay Jain, and Pieter Abbeel. This tool is designed for researchers and developers working on machine learning experiments, particularly in the field of generative models. It requires TensorFlow 1.15 and Python 3.5, along with specific dependencies like fire, scipy, pillow, tensorflow-probability, tensorflow-gan, and tensorflow-datasets. The repository includes scripts for training and evaluation, with data storage configured for Google Cloud Storage (GCS) buckets. Models and samples are available via Dropbox, making it a valuable resource for those looking to replicate or build upon the research presented in their paper.
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