| license: mit | |
| tags: | |
| - diffusion | |
| - anime | |
| - image-generation | |
| - pytorch | |
| # Anime Face Diffusion Model | |
| 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 | |
| import torch | |
| from diffusion_model import SimpleUnet, Diffuser # Your model code | |
| # Load the model | |
| checkpoint = torch.load("model.pt") | |
| model = SimpleUnet(in_channels=3) | |
| model.load_state_dict(checkpoint['ema_model_state_dict']) # Use EMA weights | |
| model.eval() | |
| # Generate samples | |
| # (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} | |
| } | |
| ``` | |