mattroz/diffusion-ddpm
Implementation of "Denoising Diffusion Probabilistic Models", Ho et al., 2020
This project helps machine learning researchers and practitioners understand and implement Denoising Diffusion Probabilistic Models (DDPMs) for generating new images. It takes a collection of existing images and uses them to learn how to create novel, similar images. This is for users who want a clear, direct implementation of the original DDPM paper to study or build upon.
No commits in the last 6 months.
Use this if you are a machine learning researcher or student looking for a transparent and faithful PyTorch implementation of the original DDPM paper to understand the core architecture without complex abstractions.
Not ideal if you are an end-user seeking a ready-to-use application for creative image generation, or if you need the absolute latest, most performant diffusion model architectures.
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Python
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Last pushed
Jan 10, 2024
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