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Life sciences · Preprint
arXiv · August 13, 2026
Posted before peer review. The findings may change or fail to hold.
This preprint proposes Global-Impact Cache (GCache), a bilevel optimization framework for selecting which computations to cache during diffusion model inference, with the goal of maintaining generation speed while improving visual quality. On a state-of-the-art video model (Waifu2.1), the method reports 2.17× speedup paired with improved LPIPS (reduced from 0.1095 to 0.0316), but the work has not been peer reviewed and lacks detail on experimental scale and statistical rigor.
Preprint. Diffusion models for visual generation (image and video); specifically tested on Waifu2.1 video diffusion model and unspecified image generation benchmarks.. Intervention: Global-Impact Cache (GCache) – bilevel optimization framework for cache reuse policy in diffusion model inference. Compared with: Prior cache-based acceleration strategies using local similarity heuristics.
GCache achieves 2.17× speedup on Waifu2.1 video diffusion model while reducing LPIPS from 0.1095 to 0.0316 Method outperforms prior caching strategies on both video and image generation tasks Proposes error propagation upper bound characterization with reparameterization via Bernstein form
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This is an unrefereed arXiv preprint proposing a novel algorithmic optimization for diffusion model inference; it reports experimental comparisons but has not undergone peer review.
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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). We first establish a rigorous theoretical characterization of the error propagation upper bound. Recognizing that this bound can be overly conservative for complex, highly non-convex diffusion models, we further reparameterize the propagation exponent with a Bernstein form and reformulate cache policy search as a bilevel optimization problem. In detail, GCache identifies an optimal reuse policy in the inner objective while aligning the error-weighting function with generation quality loss in the outer objective. This framework effectively reconciles theoretical rigor with empirical performance, learning to prioritize computation where it most impacts visual fidelity. Extensive experiments demonstrate that GCache consistently outperforms prior caching strategies on both video and image generation. Notably, on the state-of-the-art Wan2.1 video diffusion model, GCache maintains a 2.17x speedup while significantly enhancing generation quality, reducing LPIPS from 0.1095 to 0.0316.
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