---
dataset_info:
  features:
  - name: image
    dtype: image
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 39858266
    num_examples: 140000
  download_size: 37136812
  dataset_size: 39858266
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc
size_categories:
- 100K<n<1M
---


# 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](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image). 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.