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MNIST for Diffusion
Training a diffusion model from scratch is pretty cool, why not do so with the canonical "hello world" dataset of computer vision? This dataset matches the sample dataset from this text_to_image.py diffusion tutorial. Specifying ckg/mnist-for-diffusion ought get you off to the races.
This dataset contains two copies of the original MNIST train & test sets. The first half of the dataset contains MNIST images with the string-ified class id (i.e: "1") and the second half has the class id mapped to a natural language name (i.e: "one"). This little data augmentation doubles the number of samples and should result in interesting behavior if you train a U-Net from scratch whilst using a frozen, pre-trained text-encoder!
Thank you LeCun & Cortes for making this dataset available.
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