In this paper, we propose Tortoise and Hare Guidance (THG), a training-free strategy that accelerates diffusion sampling while maintaining high-fidelity generation. We demonstrate that the noise estimate and the additional guidance term exhibit markedly different sensitivity to numerical error by reformulating the classifier-free guidance (CFG) ODE as a multirate system of ODEs. Our error-bound analysis shows that the additional guidance branch is more robust to approximation, revealing substantial redundancy that conventional solvers fail to exploit. Building on this insight, THG significantly reduces the computation of the additional guidance: the noise estimate is integrated with the tortoise equation on the original, fine-grained timestep grid, while the additional guidance is integrated with the hare equation only on a coarse grid. We also introduce (i) an error-bound-aware timestep sampler that adaptively selects step sizes and (ii) a guidance-scale scheduler that stabilizes large extrapolation spans. THG reduces the number of function evaluations (NFE) by up to 30% with virtually no loss in generation fidelity (∆ImageReward ≤ 0.032) and outperforms state-of-the-art CFG-based training-free accelerators under identical computation budgets. Our findings highlight the potential of multirate formulations for diffusion solvers, paving the way for real-time high-quality image synthesis without any model retraining. The source code is available at https://github.com/yhlee-add/THG.

Custom Memory Allocator (CMA) replaces the standard memory allocator for various purposes, such as improving memory efficiency or enhancing security. However, memory objects allocated by CMA are vulnerable to memory bugs similar to those allocated by the standard memory allocator. Unfortunately, existing memory bug detection approaches, including Address Sanitizer (ASan), do not work properly with these CMAs because existing approaches are mainly designed for the standard memory allocator. This paper presents CMASan, the first CMA-aware address sanitizer designed to effectively detect memory bugs on CMA objects that ASan misses without requiring expert knowledge, manual code modifications, or changing the unique internal logic of CMAs. According to our evaluation, CMASan successfully identifies 19 previously unknown CMA memory bugs undetected by ASan, including some undetected for 9 years. Compared to ASan, CMASan incurs only an additional 9.63% overhead.