From Local Mismatch to Global Impact: Optimizing Cache Reuse Policy for Efficient Diffusion

Diffusion models have achieved dominant performance in visual generation but suffer from substantial inference overhead. While cache-based acceleration has emerged as a promising solution, existing policies rely on local similarity heuristics, which we identify as being significantly misaligned with final generation quality. This discrepancy stems from the non-uniform propagation and accumulation of errors along the denoising trajectory. To address this, we propose Global-Impact Cache (GCache).

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ethics.ai (13 August 2026), “From Local Mismatch to Global Impact: Optimizing Cache Reuse Policy for Efficient Diffusion,” evidence record 19184, https://ethics.ai/record/19184 (originally published by arXiv).

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