Stable Diffusion Comparison in C, Rust and Ruby
a year ago
- #diffusion-models
- #low-level-programming
- #machine-learning
- Introduction to implementing Stable Diffusion from scratch in low-level languages (C, Ruby, Rust).
- Focus on the forward process of diffusion models, specifically adding Gaussian noise to images.
- Implementation of Gaussian noise generators using the Box-Muller transform in C, Ruby, and Rust.
- Generation of the Swiss-Roll dataset for testing diffusion models.
- Explanation and implementation of different beta schedules (Linear, Quadratic, Cosine, Sigmoid).
- Detailed explanation of the reparameterization trick to enable gradient backpropagation in diffusion models.
- Step-by-step guide to computing diffusion parameters and adding noise to images.
- Conclusion with a teaser for Part 2, which will cover building a neural network to reverse the noise process.
- Citations and references to influential papers and resources in the field of diffusion models.