Diffusion theory

This entrypoint collects the diffusion-theory facts that connect TorchLean's executable sampler specifications to the mathematical language used in diffusion and score-based generative modeling.

This entrypoint contains two stable pieces of the theory surface:

• ForwardGaussian: a mathlib-backed result showing that affine forward noising of a standard Gaussian remains Gaussian.
• Samplers: proved boundary, dynamics-adapter, and Euler-stability facts for DDPM, DDIM, and probability-flow samplers.

Probabilistic claims and executable sampler claims stay separate. The spec layer defines the noising and reverse-update functions; this theory layer records the mathematical facts we can prove cleanly about those definitions.