Overview
The project implements and trains different diffusion models from scratch, varying quantization and timestep choices to investigate how those decisions affect the resulting models.
Experiment space
The repository is organized around comparing models under different configurations, with particular attention to quantization and the number of timesteps used in the diffusion process.
Implementation
The project uses PyTorch and includes a dedicated pytorch-diffusion implementation directory. The repository is intentionally kept close to the research/engineering workflow: code, data, and a written project report are all tracked together.
This makes it a useful companion to the other ML projects in the portfolio: rather than treating a model as a black box, the implementation is the subject of the project itself.
Artifacts
The GitHub repository currently includes the project report and supporting experiment data alongside the implementation.
I’m leaving the quantitative conclusions intentionally light here until there is a richer public README to draw from. The next version of this page can incorporate the report's charts and findings directly.