addtt/variational-diffusion-models
PyTorch implementation of Variational Diffusion Models.
This project offers a clear implementation of Variational Diffusion Models, primarily focused on accurately estimating the likelihood of images rather than just their visual quality. It takes raw image data (like the CIFAR10 dataset) and provides a model that can generate new images, along with metrics like the variational lower bound (VLB) to quantify how well the model understands the underlying data distribution. This is intended for researchers and machine learning practitioners who need to rigorously evaluate generative models based on their probabilistic fidelity.
104 stars. No commits in the last 6 months.
Use this if you are a researcher or practitioner interested in understanding and implementing Variational Diffusion Models with an emphasis on likelihood estimation and educational clarity, especially for image generation tasks.
Not ideal if your primary goal is to generate visually stunning, high-fidelity images without a deep concern for the underlying probabilistic modeling or if you need advanced features like variance minimization with the γη network.
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104
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10
Language
Python
License
MIT
Category
Last pushed
Apr 23, 2024
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