metadata
license: apache-2.0
datasets:
- huggan/anime-faces
pipeline_tag: unconditional-image-generation
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
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.