Sigma score estimator

Diffusion samplers depend on how noise is scheduled. This project trains a network to estimate the score with respect to the noise level itself — ∇_σ log p(x,σ) — so the schedule can adapt to the data rather than follow a fixed curve.

The code is a PyTorch training framework around ResNet backbones, aimed at research experiments rather than a packaged product.