Why your diffusion model is slow at batch size 1 (and what actually helps)
The article discusses the inefficiencies of single-image diffusion models at batch size 1. It highlights that the primary bottlenecks are kernel launch overhead and memory traffic rather than raw computational power. Several optimization strategies are suggested to improve performance, including using specific compilation modes and batching techniques.
- ▪Single-image diffusion inference is limited by kernel launch overhead and attention memory traffic.
- ▪Using torch.compile with mode='reduce-overhead' can significantly reduce latency without changing model architecture.
- ▪Batching classifier-free guidance can nearly halve per-step latency by utilizing the GPU more effectively.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3864909) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Elise Moreau Posted on May 19 Why your diffusion model is slow at batch size 1 (and what actually helps) #pytorch #machinelearning #computervision #mlops TL;DR: Single-image diffusion inference is bottlenecked by kernel launch overhead and attention memory traffic, not raw FLOPs. torch.compile with mode="reduce-overhead", a fused attention backend, and CFG batching get you most of the way before you reach for distillation.
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