Introduction to Diffusion Models from Scratch
The "Diffusion Models from Scratch" project is a comprehensive repository focused on implementing different types of diffusion models, including DDPM, DDIM, and Classifier-Free guidance models. These models have been trained using the ImageNet dataset at a resolution of 64x64 pixels. The goal of the project is to provide the community with tools and resources to understand and effectively use diffusion models for image recognition and generation tasks. For more in-depth details about the algorithms behind these models, readers are encouraged to refer to the author's detailed Medium article.
Current Features
The repository includes several advanced features related to diffusion models. Key highlights include:
- Standard DDPM: The basic diffusion probabilistic model.
- Improved DDPM: An enhanced version incorporating a cosine scheduler and variance prediction to improve model performance.
- DDIM: A variant allowing faster inference speeds.
- Classifier-Free Guidance: This improves the quality of the generated images by fine-tuning the conditioning process.
These implementations make the repository a valuable resource for both researchers and practitioners interested in image generation using diffusion models.
Getting Started
Setting up your environment is straightforward with the provided instructions. The repository can be cloned via Git and it's recommended to use a virtual environment to avoid conflicts with other Python projects. This setup ensures that all required packages, especially PyTorch with CUDA support if necessary, are correctly installed.
Utilizing Pre-Trained Models
Pre-trained models are available for quick experimentation and integration. These models differ based on their architectural structures and training parameters. Users can choose from different types of models such as Res-Conv, Res, Res-Res, Res-Res-Atn, and a larger variant Res Large tailored to specific needs.
To use these models, understanding their training configuration is crucial. Each model is optimized to ensure high-quality image generation, tuned through numerous parameters like the number of timesteps, batch size, and learning rate. Files related to model checkpoints and parameters for both training & inference use cases are provided.
Training New Models
While pre-trained models offer immediate uses, the project also supports custom model training. Detailed instructions are given for setting up training configurations, including GPU usage, model architecture parameters, and training processes like gradient accumulation which helps manage memory usage during training.
Image Generation and Evaluation
For generating new images, scripts are provided to facilitate the use of pre-trained models. Parameters for the inference process allow users to tune aspects like generation step size and guidance scale to control the quality and diversity of the resulting images.
Additionally, the project offers tools to calculate the FID (Fréchet Inception Distance) score—a method for evaluating the accuracy and diversity of generated images against a real dataset, ensuring users can assess the quality of their models effectively.
Conclusion
The "Diffusion Models from Scratch" project is not only a repository of sophisticated models but a full-fledged toolkit for anyone eager to explore the fascinating field of image diffusion models. Through well-documented procedures and robust model implementations, users have a ready foundation for advancing their work or simply for educational purposes in the realm of machine learning and image processing.