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Ddpm Mnist
Lightweight UNet2D model trained on MNIST dataset for unconditional generation of handwritten digit images
Downloads 2,081
Release Time : 10/6/2023
Model Overview
This is an unconditional generation model that cannot specify particular digits to generate, mainly used for producing MNIST-style handwritten digit images.
Model Features
Lightweight model
The model has a small scale and can be trained in about 40 minutes on a Google Colab L4 GPU instance
Unconditional generation
Generates MNIST-style handwritten digit images randomly without relying on any conditional input
Fast training
Short training time, suitable for quick experiments and teaching demonstrations
Model Capabilities
Generate handwritten digit images
Unconditional image generation
Use Cases
Education & Research
Diffusion model teaching
Used to demonstrate the basic principles and working methods of diffusion models
Can generate MNIST-style digit images
Generative model research
Serves as a base model for studying unconditional image generation techniques
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