研究团队测试了三种视频生成模型，发现硬件故障可能导致视频内容发生语义变化，这对实际应用有重要警示意义。
研究团队首次系统评估了文本生成视频(T2V)扩散模型在随机硬件故障下的表现。研究覆盖了三种T2V模型和一个代表性基准，发现单个故障可导致性能下降3.7%，语义正确性比感知质量更易受影响。内存故障比计算故障危害更大，bfloat16格式比其他格式更易受影响。7-28%的故障会导致可见伪影，包括语义变化。
On the Resilience of Text-to-Video Diffusion Models to Hardware Faults
We present the first systematic study of the resilience of text-to-video (T2V) diffusion models under random hardware-level faults. While T2V models are widely used for automated video generation due to their ability to produce high-quality, temporally coherent, and realistic videos, their iterative denoising process and spatiotemporal dependencies introduce unique failure modes. We perform an extensive fault-injection study covering both computational and memory faults across three T2V models and a representative benchmark. Our results show that (1) a single fault can degrade overall performance by up to 3.7\%, with semantic correctness more affected than perceptual quality; (2) memory faults are more damaging than computational faults, high-order exponent bits are particularly vulnerable, and the widely-used bfloat16 is more susceptible than alternative formats; and (3) 7-28\% of faults cause visible artifacts, including semantic changes such as added objects, suggesting that single faults are sufficient to alter output semantics. Our findings reveal reliability risks in deployed T2V systems and motivate further research on improving fault resilience. Code: \href{https://github.com/ztcoalson/T2V-Resilience}{https://github.com/ztcoalson/T2V-Resilience}.