Single-image diffusion model inference is slowed by kernel launch overhead and attention memory traffic, rather than raw computational power. Optimizing with `torch.compile` in `reduce-overhead` mode, employing a fused attention backend, and batching classifier-free guidance can significantly reduce latency. Only after these optimizations should one consider distillation methods for further speed improvements, while carefully evaluating potential quality degradation. AI
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IMPACT Optimizing diffusion model inference speed can lower operational costs and enable new real-time applications.
RANK_REASON Technical explanation of performance bottlenecks and optimization strategies for diffusion models. [lever_c_demoted from research: ic=1 ai=1.0]