SupraMNiST-IMG-200k
Sumary
Task: Number-To-Image
Dataset: ylecun/mnist
Total training time: ~8 minutes
Inputs: Number (0-9)
Outputs: 32x32 image
Params: ~201k
Framework: PyTorch, diffusers
Author: SupraLabs
Description
MNiST-IMG-200k is an ~200k parameter model trained to generate an image based on an input number (0-9).
Architecture
| Parameter | Value |
|---|---|
image_size |
32 |
in_channels |
1 |
out_channels |
1 |
num_classes |
10 |
block_out_channels |
[12, 16] |
layers_per_block |
8 |
norm_num_groups |
4 |
Training
Hardware
MNiST-IMG was trained on Google Colaboratory (NVIDA Tesla T4) for ~8 minutes with a batch size of 64 for 10 epochs.
Dataset
Training Results
Loss ended at ~0.40.
Note: I can't provide the raw training logs as I loss it somehwere after training. Sorry!
Generation Examples
At 1000 decoding steps:
At 200 decoding steps:
Inference
Use the script in the repo. inference.py
Related Models
Citation
@misc{mnist-img-390k,
title = {MNIST-IMG-390k: a Tiny Diffusion Model for Generating Handwritten Digits},
author = {Paul Courneya; Harley-ml},
year = {2026},
url = {https://huggingface.co/Harley-ml/MNIST-IMG-390k}
}
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