| --- |
| 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: |
|
|
| <p align="center" style="display: flex;"> |
| <img src="images/sample.webp" width="64"/> |
| <img src="images/sample2.webp" width="64"/> |
| <img src="images/sample3.webp" width="64"/> |
| <img src="images/sample4.webp" width="64"/> |
| <img src="images/sample5.webp" width="64"/> |
| <img src="images/sample6.webp" width="64"/> |
| <img src="images/sample7.webp" width="64"/> |
| <img src="images/sample8.webp" width="64"/> |
| <img src="images/sample9.webp" width="64"/> |
| <img src="images/sample10.webp" width="64"/> |
| <img src="images/sample11.webp" width="64"/> |
| </p> |
|
|
| <p align="center" style="display: flex;"> |
| <img src="images/sample22.webp" width="64"/> |
| <img src="images/sample12.webp" width="64"/> |
| <img src="images/sample13.webp" width="64"/> |
| <img src="images/sample14.webp" width="64"/> |
| <img src="images/sample15.webp" width="64"/> |
| <img src="images/sample16.webp" width="64"/> |
| <img src="images/sample17.webp" width="64"/> |
| <img src="images/sample18.webp" width="64"/> |
| <img src="images/sample19.webp" width="64"/> |
| <img src="images/sample20.webp" width="64"/> |
| <img src="images/sample21.webp" width="64"/> |
| </p> |
|
|
| ## 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. |
