NVIDIA compares its PhysicsNeMo diffusion module with pure PyTorch: 41% to 54% less code and 1.4x the training throughput at 512x512

NVIDIA engineers Charlelie Laurent and Peter Harrington publish a comparison of `physicsnemo.diffusion` with equivalent pure-PyTorch diffusion pipelines. User-facing code falls to 59% of the baseline for training, 59% for inference and 46% for guided inference with diffusion posterior sampling, each a mean over 10 independent implementations. Training an 81M-parameter diffusion U-Net on 16-channel data on four GB200 GPUs, the module with compilation, bf16 mixed precision and Apex layers reaches 1.4x the baseline's throughput at 512x512; beyond that size the baseline runs out of memory, the optimised module reaches 1024x1024, and only multi-diffusion goes further, at lower throughput. Guided inference on one GB200 gains about 20% to 30% on domains under 256x256.

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