Diffusion models have shown tremendous results in image generation. However,
due to the iterative nature of the diffusion process and its reliance on
classifier-free guidance, inference times are slow. In this paper, we propose a
new distillation approach for guided diffusion models in which an external
lightweight guide model is trained while the original text-to-image model
remains frozen. We show that our method reduces the inference computation of
classifier-free guided latent-space diffusion models by almost half, and only
requires 1\% trainable parameters of the base model. Furthermore, once trained,
our guide model can be applied to various fine-tuned, domain-specific versions
of the base diffusion model without the need for additional training: this
"plug-and-play" functionality drastically improves inference computation while
maintaining the visual fidelity of generated images. Empirically, we show that
our approach is able to produce visually appealing results and achieve a
comparable FID score to the teacher with as few as 8 to 16 steps.

我们提出了一种新的蒸馏方法，可以减少迭代计算过程中无需分类器指导的扩散模型的推理计算时间，并且只需要基础模型的 1％的可训练参数，同时还能维持生成图像的视觉逼真度。

即插即用扩散蒸馏

Plug-and-Play Diffusion Distillation

Evaluating the performance of autonomous vehicle planning algorithms
necessitates simulating long-tail traffic scenarios. Traditional methods for
generating safety-critical scenarios often fall short in realism and
controllability. Furthermore, these techniques generally neglect the dynamics
of agent interactions. To mitigate these limitations, we introduce a novel
closed-loop simulation framework rooted in guided diffusion models. Our
approach yields two distinct advantages: 1) the generation of realistic
long-tail scenarios that closely emulate real-world conditions, and 2) enhanced
controllability, enabling more comprehensive and interactive evaluations. We
achieve this through novel guidance objectives that enhance road progress while
lowering collision and off-road rates. We develop a novel approach to simulate
safety-critical scenarios through an adversarial term in the denoising process,
which allows the adversarial agent to challenge a planner with plausible
maneuvers, while all agents in the scene exhibit reactive and realistic
behaviors. We validate our framework empirically using the NuScenes dataset,
demonstrating improvements in both realism and controllability. These findings
affirm that guided diffusion models provide a robust and versatile foundation
for safety-critical, interactive traffic simulation, extending their utility
across the broader landscape of autonomous driving. For additional resources
and demonstrations, visit our project page at this https URL

评估自动驾驶车辆规划算法的性能需要模拟长尾交通场景。通过引入基于引导扩散模型的闭环模拟框架，我们提出一种新的方法来生成现实世界情景的安全关键场景，并通过增强的可控性使评估更全面和互动性更强。通过相关的引导目标，我们改善了道路进展，同时降低了碰撞和离道率，从而实现了这一目标。通过去噪过程中的对抗项，我们开发了一种模拟安全关键场景的新方法，使对抗性代理能够通过可行的操纵手段挑战规划器，同时场景中的所有代理均表现出反应灵敏和逼真的行为。我们通过使用 NuScenes 数据集的实证验证了我们的框架，证明了在现实性和可控性方面的改进。这些发现肯定了引导扩散模型为安全关键、互动式交通模拟提供了强大而通用的基础，进一步扩展了其在自动驾驶领域的实用性。请访问我们的项目页面（https URL）获取更多资源和演示。