---
license: cc-by-4.0
datasets:
- ComputerVisionAnimeProject/AnimeFaceColorization
language:
- en
base_model:
- adityachaubey/Anime_Face_DDPM
new_version: adityachaubey/Anime_Face_DDPM
pipeline_tag: unconditional-image-generation
tags:
- AI
- diffusionmodel
- GenAI
- pytorch
- DDPM
---
# 🎨 AnimeFace-DDPM

![Python](https://img.shields.io/badge/Python-3.12-blue?style=plastic&logo=python)
![PyTorch](https://img.shields.io/badge/PyTorch-EE4C2C?style=plastic&logo=pytorch&logoColor=white)
![DDPM](https://img.shields.io/badge/Model-DDPM-green?style=plastic)
![License](https://img.shields.io/badge/License-cc-by-4.0-green?style=plastic)


A **Denoising Diffusion Probabilistic Model (DDPM)** implemented **from scratch in PyTorch** for unconditional anime face generation.

The model was trained on the **Anime Face Dataset** in a Kaggle Notebook using an **NVIDIA Tesla T4 GPU**. During inference, **Exponential Moving Average (EMA)** shadow weights together with **DDIM sampling** are used to generate higher-quality images with significantly fewer sampling steps.

---

# Model Details

| Property | Value |
|----------|-------|
| Model | DDPM |
| Framework | PyTorch |
| Architecture | Custom U-Net |
| Parameters | 7.52 Million |
| Dataset | Anime Face Dataset |
| Image Resolution | **64X64** |
| Diffusion Timesteps | 1000 |
| Sampling | DDIM |
| EMA | Yes |
| Optimizer | AdamW |

---

# Available Checkpoints

This repository contains two checkpoints.

### `ddpm.pth`

The standard model weights obtained directly after training.

### `ema.pth`

The Exponential Moving Average (EMA) shadow weights.

These weights are recommended for inference because they generally produce sharper and more stable image generations.

---

# Training

The model was trained entirely from scratch using PyTorch.

**Training Configuration**

- Dataset: Anime Face Dataset
- Training Platform: Kaggle
- GPU: NVIDIA Tesla T4
- Optimizer: AdamW
- Diffusion Timesteps: 1000
- EMA enabled during training
- DDIM used during inference

---

# Intended Use

This model is intended for

- Learning diffusion models
- Educational purposes
- Research
- Anime image generation
- Experimenting with DDPMs

---

# Limitations

- Generates only anime-style faces.
- Performance is limited to the distribution of the training dataset.
- This is an unconditional diffusion model and cannot generate images from text prompts.

---

# Citation

If you use this model in your work, please cite the original diffusion papers.

```bibtex
@article{ho2020ddpm,
  title={Denoising Diffusion Probabilistic Models},
  author={Jonathan Ho and others},
  year={2020}
}

@article{song2020ddim,
  title={Denoising Diffusion Implicit Models},
  author={Jiaming Song and others},
  year={2020}
}
```

---

# Acknowledgements

- PyTorch
- Hugging Face
- Kaggle
- DDPM (Ho et al., 2020)
- DDIM (Song et al., 2020)

---

# License

This project is released under the **MIT License**.