denoising-diffusion-mindspore and MNISTDiffusion

denoising-diffusion-mindspore
50
Established
MNISTDiffusion
46
Emerging
Maintenance 0/25
Adoption 8/25
Maturity 25/25
Community 17/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 20/25
Stars: 46
Forks: 10
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stars: 143
Forks: 28
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stale 6m
Stale 6m No Package No Dependents

About denoising-diffusion-mindspore

lvyufeng/denoising-diffusion-mindspore

Implementation of Denoising Diffusion Probabilistic Model in MindSpore

This project helps machine learning engineers and researchers generate high-quality images from scratch, or perform image denoising. It takes a dataset of existing images as input and trains a model to understand their characteristics. The output is a new set of synthetic images that resemble the training data, allowing for creative content generation or data augmentation. This tool is for professionals working with generative AI and image synthesis.

generative-AI image-synthesis machine-learning-research content-creation data-augmentation

About MNISTDiffusion

bot66/MNISTDiffusion

Implement a MNIST(also minimal) version of denoising diffusion probabilistic model from scratch.The model only has 4.55MB.

This project helps machine learning researchers and students understand and experiment with denoising diffusion probabilistic models on a very small scale. It takes noisy image data and learns to generate clear, new images, providing a hands-on way to see how diffusion models work without requiring significant computational resources. The output is a functional, lightweight model capable of generating digits.

deep-learning-education generative-models image-synthesis model-training computational-efficiency

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