SDXL inference optimizations with diffusers: FP16, SDPA, and CPU offload

AI Impact Summary

SDXL's 3.5B UNet drives high memory and latency. The article demonstrates concrete speed/memory tradeoffs using diffusers and PyTorch 2.0: FP16 reduces memory to ~21.7GB with 14.8s latency; adding SDPA yields ~11.4s with the same memory footprint; torch.compile then pushes to ~10.2s, though the first compile incurs overhead. For memory-constrained deployments, CPU offloading variants drop GPU memory to about 20.2GB or 19.9GB but can dramatically increase per-batch latency (up to ~67s). In production, engineers can tune precision, attention, compilation, and offloading to fit SDXL on mid-range GPUs, but require careful benchmarking across batch sizes and hardware to balance throughput and latency.