Generative downscaling turns coarse climate fields into kilometer-scale maps, but diffusion and flow-matching models routinely break the physics they should respect: winds that diverge, negative rainfall, precipitation totals that drift from the coarse input. PC-RF is a conditional rectified-flow model in PyTorch that bakes three conservation constraints (divergence-free wind, non-negative precipitation, domain mass balance) straight into the training objective and fuses ERA5 atmosphere with Sentinel-2 imagery.

This talk follows one model from torch training to a served stack, and the engineering that broke along the way: exporting a U-Net with attention two ways (torch.onnx and AOTInductor) and why both specialized shapes until coaxed, running the rectified-flow ODE sampler outside the graph, enforcing physics at inference so non-negativity and mass-conservation errors hit exactly zero, and a Ray Train recipe for scaling. The serving path is polyglot: ONNX Runtime in Rust, Temporal in Go, and a vLLM agent that calls the downscaler as a tool. You leave with patterns for shipping scientific generative models, plus three physics-validity metrics worth adding to your eval.