DiffusionFastForward provides a free course and an experimental framework for understanding and researching diffusion-based generative models, particularly for image generation. It's designed for researchers and students looking for a starting point to train diffusion models on new data types, offering a simplified structure and runnable notebooks for various diffusion techniques.

How It Works

The framework leverages PyTorch Lightning for streamlined training and offers examples for both pixel-space diffusion (suitable for low-resolution images) and latent-space diffusion (for high-resolution images). It includes conditional diffusion for image translation tasks. The project's design prioritizes simplicity and customizability, making it an accessible entry point into diffusion model research without requiring extensive prior experience or specialized hardware.

Quick Start & Requirements

• Install via pip: pip install pytorch-lightning==1.9.3 diffusers einops
• Requires PyTorch and Torchvision.
• All experiments can be run on Google Colab, eliminating the need for a personal GPU.
• Official documentation and course videos are available on YouTube.

Highlighted Details

• Provides runnable notebooks for diffusion sandbox, pixel diffusion, conditional pixel diffusion, latent diffusion, and conditional latent diffusion.
• Includes short summary notes semantically aligned with the notebooks.
• Offers examples for both low-resolution (e.g., 64x64, ~10 hrs training) and high-resolution (e.g., 256x256, ~20 hrs training) data.
• Demonstrates image translation capabilities using diffusion models.

Maintenance & Community

The project is associated with a YouTube video course that expands on the repository's content, covering seminal papers and ongoing research. Links to community channels or active development are not explicitly mentioned in the README.

Licensing & Compatibility

The repository does not specify a license. It states that it does not provide model weights, and its purpose is to train new weights on unexplored data.

Limitations & Caveats

The repository does not provide pre-trained model weights, requiring users to train their own. The code is presented as a starting point and may require customization for more robust solutions.