adityachaubey/Anime_Face_DDPM

🎨 AnimeFace-DDPM

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.

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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

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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.

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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

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Intended Use

This model is intended for

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

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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.

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Citation

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

@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}
}

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Acknowledgements

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

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License

This project is released under the MIT License.