arXiv:2606.31026v1 Announce Type: new Abstract: We propose OTCache, a training-free framework for accelerating diffusion sampling via caching schedule prediction. Existing graph-based caching methods reduce redundant computation by optimizing shortest-path objectives, but rely on an additive independence assumption, which often breaks down in the low NFE regime. To address this issue, OTCache models caching schedules across inference budgets as a smooth evolution in policy space, inspired by Optimal Transport (OT). The framework consists of three stages: (1) obtaining a high-fidelity \textbf{r
The continuous drive to optimize computational efficiency in AI models, particularly diffusion models, motivates new methods for acceleration and resource management.
This development offers a training-free approach to significantly speed up diffusion sampling, which is critical for the practical deployment and scalability of generative AI applications.
Diffusion model inference can become substantially faster and potentially more resource-efficient without requiring extensive retraining, making these models more accessible and cost-effective.
- · AI developers
- · Cloud providers
- · Generative AI companies
- · Consumers of generative AI
Faster diffusion sampling leads to quicker iteration and development cycles for AI models.
Reduced computational costs could democratize access to advanced generative AI capabilities.
The increased efficiency might push the boundaries of what is feasible with real-time generative AI applications, potentially leading to new product categories.
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Read at arXiv cs.LG