A diffusion model trained to generate 64x64 anime-style faces.
Model Details
Model type: Denoising Diffusion Probabilistic Model (DDPM)
Training data: Anime faces dataset
Image size: 64x64 RGB
Timesteps: 1000
Architecture: U-Net with sinusoidal time embeddings
Usage
python
1import torch
2from diffusion_model import SimpleUnet, Diffuser # Your model code34# Load the model5checkpoint = torch.load("model.pt")6model = SimpleUnet(in_channels=3)7model.load_state_dict(checkpoint['ema_model_state_dict'])# Use EMA weights8model.eval()910# Generate samples11# (Add your sampling code here)
Training Details
Trained for X epochs
Batch size: 64
Learning rate: 1e-4
EMA decay: 0.9999
Noise schedule: Cosine
Samples
(Add generated sample images here)
Citation
If you use this model, please cite:
@misc{your-anime-diffusion,
author = {Your Name},
title = {Anime Face Diffusion Model},
year = {2024},
publisher = {HuggingFace},
url = {https://huggingface.co/your-username/anime-diffusion}
}
