Importance-aware pruning for diffusion models uses spatial importance maps to retain parameters critical to semantically salient regions, preserving subject fidelity and structural correctness at high compression ratios.
LoRIF exploits low-rank gradient structure to reduce storage and query I/O to O(c√D) and inverse Hessian memory to O(Dr), achieving up to 20× speedups over LoGRA at scale.