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nitesh501/
tinydit

unconditional-image-generation

huggan/anime-faces

apache-2.0

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TinyDiT

TinyDiT is an 85 million parameter unconditional image generation model trained on 21,000+ anime face images.

Model Details

Model Name: TinyDiT
Architecture: Diffusion Transformer (DiT-inspired)
Parameters: 85M
Task: Unconditional Image Generation
Dataset Size: 21,000+ anime face images
VAE: Lightweight 13M parameter VAE
Generation Type: Anime face generation from random noise (no text conditioning)
Image Size: 64x64px
Github Repo: https://github.com/Nitesh1405/TinyDiT/tree/main

Dataset

TinyDiT was trained on a curated anime face dataset containing over 21k images.

Dataset Repository: huggan/anime-faces

VAE

The model uses a compact 13M parameter Variational Autoencoder (VAE) for latent-space encoding and decoding.

Example Generated Images

Below is a sample images generated by TinyDiT:

Usage

HuggingFace Space: https://huggingface.co/spaces/nitesh501/TinyDiT

bash

git clone https://github.com/Nitesh1405/TinyDiT.git && cd TinyDiT

pip install -r requirements.txt

python app.py
#the model will automatically download on first run if you have wget, if not you can download the model from https://huggingface.co/nitesh501/tinydit and place it in TinyDit Folder.

Limitations

Trained only on anime face data
Unconditional generation only
Limited diversity compared to larger diffusion models
May occasionally generate blurry or distorted outputs

Acknowledgements

Inspired by DiT architectures, latent diffusion models, and the open-source generative AI community.