DeepCache: Accelerating Diffusion Models for Free
DeepCache accelerates diffusion models by caching features, achieving 2.3× speedup on Stable Diffusion v1.5 with minimal quality loss without retraining.
Key Findings
Methodology
DeepCache leverages the temporal redundancy inherent in diffusion models by caching and reusing high-level features across adjacent denoising steps. It exploits the U-Net architecture, reusing high-level features while efficiently updating low-level features with minimal computation. This approach eliminates the need for retraining, integrating seamlessly with existing sampling techniques like DDIM and PLMS. The process involves: • Caching high-level features at each denoising step; • Updating low-level features with low-cost operations; • Reusing cached features across steps to reduce redundancy. Experiments show 2.3× acceleration on Stable Diffusion v1.5 with only a 0.05 drop in CLIP score, and 4.1× on LDM-4-G with a 0.22 FID increase.
Key Results
- On Stable Diffusion v1.5, DeepCache achieves 2.3× faster inference with only a 0.05 decrease in CLIP score, maintaining high-quality image synthesis while significantly improving efficiency.
- On LDM-4-G, it attains 4.1× speedup with a slight FID increase of 0.22, outperforming traditional pruning and distillation methods that require retraining.
- Under the same throughput, DeepCache performs comparably or slightly better than DDIM and PLMS sampling, demonstrating broad applicability.
Significance
This work addresses the computational bottleneck of diffusion models, offering a novel architecture-level solution that does not rely on retraining. It significantly reduces deployment costs and enhances practicality for real-world applications. The approach advances both academic research in model compression and industry efforts toward efficient generative AI, enabling high-quality image synthesis on resource-constrained devices while maintaining model flexibility.
Technical Contribution
DeepCache introduces a training-free acceleration framework based on feature caching within the U-Net architecture. By exploiting the temporal redundancy of the denoising process, it reuses high-level features across steps, only updating low-level features with minimal computation. This design eliminates the need for retraining, integrates with existing sampling algorithms, and provides a new paradigm for efficient diffusion model deployment. The method's core innovations include: • A feature caching mechanism; • Selective low-cost feature updates; • Compatibility with multiple sampling techniques. Extensive experiments validate its effectiveness across models.
Novelty
DeepCache is the first to utilize feature caching for training-free acceleration of diffusion models. Unlike traditional pruning or distillation requiring retraining, it leverages the intrinsic temporal redundancy of the denoising process. Its architecture-level optimization offers a new direction for efficient inference, filling a gap in current research on model acceleration without additional training.
Limitations
- The caching mechanism may face challenges in highly complex or dynamic scenarios where feature consistency deteriorates, potentially affecting quality.
- Some minor quality degradation may occur due to feature reuse, requiring careful trade-offs between speed and fidelity.
- The current implementation is tailored to U-Net-based models; extending to other architectures needs further validation.
Future Work
Future research will explore adaptive caching strategies to handle diverse tasks and model architectures, aiming to improve robustness. Combining hardware acceleration and dynamic cache management could further boost real-time performance. Additionally, investigating cache consistency and memory management in large-scale deployment will be crucial for broader adoption.
AI Executive Summary
Diffusion models have revolutionized image synthesis, delivering high-quality results across diverse applications. However, their computational demands—stemming from the sequential denoising process—pose significant barriers to widespread deployment. Traditional approaches like pruning and distillation can reduce model size but often require extensive retraining, which is costly and inflexible. To address this, the authors propose DeepCache, a novel architecture-level, training-free framework that accelerates diffusion models by exploiting the temporal redundancy inherent in the denoising process.
DeepCache leverages the structure of U-Net, caching high-level features during each denoising step and reusing them across adjacent steps. This approach minimizes redundant computations, significantly speeding up inference. The core idea is simple yet powerful: high-level features are stable across steps and can be reused, while low-level features are cheap to update. This method does not require retraining or fine-tuning, making it highly practical.
Experimental results demonstrate the effectiveness of DeepCache. On the popular Stable Diffusion v1.5, it achieves a 2.3× speedup with only a 0.05 drop in CLIP score, indicating minimal impact on image quality. Similarly, on the LDM-4-G model, it attains a 4.1× acceleration with a slight increase of 0.22 in FID. These improvements outperform existing pruning and distillation techniques, which often involve retraining and can degrade quality.
The significance of this work lies in its simplicity and broad applicability. By harnessing the intrinsic temporal redundancy of diffusion models, DeepCache provides a practical solution for deploying high-quality generative models efficiently. Its compatibility with current sampling methods ensures easy integration into existing workflows. Looking ahead, combining this approach with hardware acceleration and adaptive caching strategies could further revolutionize real-time image synthesis, making advanced generative AI accessible on resource-constrained devices and in latency-sensitive applications.
Deep Analysis
Background
Diffusion模型近年来在图像生成领域取得巨大突破，代表性工作包括DDPM、Score-based Models和Stable Diffusion。这些模型通过逐步去噪实现高质量生成，但计算成本极高，限制了其实际应用。传统压缩技术如剪枝、蒸馏和量化虽然能减轻模型负担，但都依赖大量再训练，成本高且难以灵活调整。随着模型规模不断扩大，效率瓶颈愈发突出，推动架构优化成为研究重点。近年来，研究者开始关注模型推理的冗余特性，试图通过架构设计减少重复计算，但尚未实现训练无关的高效加速方案。
Core Problem
扩散模型的核心瓶颈在于逐步去噪的序列性，导致每次采样都需重复大量计算，尤其在高分辨率图像生成中尤为明显。现有压缩技术多依赖再训练，成本高且不易快速部署。此外，如何在保证生成质量的同时实现高效推理，是当前的技术难题。解决这一问题对于推广扩散模型的实际应用具有重要意义，尤其是在资源有限的设备上实现实时生成成为亟待突破的瓶颈。
Innovation
本文提出DeepCache，利用模型中的时间冗余特性，通过缓存邻近去噪阶段的高层次特征，减少重复计算。创新点包括：• 设计特征缓存机制，存储高层次信息；• 仅用低成本操作更新低层特征；• 结合邻近阶段的特征重用，充分利用时间冗余。这一架构优化突破了传统依赖再训练的限制，为模型加速提供了新思路。其核心在于：在不改变模型参数的情况下，通过架构设计实现推理速度的显著提升。
Methodology
- �� 在每个去噪步骤中，提取并缓存U-Net中的高层特征，存储在缓存中；• 只对低层特征进行低成本的更新操作，避免重复高成本计算；• 利用邻近阶段的时间冗余，将缓存的高层特征直接复用，减少冗余；• 结合现有采样技术（如DDIM、PLMS），在不影响生成质量的前提下实现加速；• 通过多模型、多参数设置，验证不同场景下的性能提升。整个流程无需模型微调，直接在原模型基础上实现加速。
Experiments
采用ImageNet数据集，比较Stable Diffusion v1.5和LDM-4-G模型的性能。指标包括推理速度、FID、CLIP得分。设置不同采样步数，进行AB测试，验证不同缓存策略的效果。对比剪枝、蒸馏等方法，评估模型质量与效率。参数调优确保在最大化速度提升的同时，保持生成质量。实验还测试了不同缓存大小和更新频率对性能的影响，确保方案的鲁棒性。
Results
DeepCache在Stable Diffusion v1.5上实现2.3倍加速，FID仅下降0.22，CLIP得分下降0.05，显示其在保持生成质量的同时显著提升推理速度。在LDM-4-G模型上，达成4.1倍加速，FID指标仅增加0.22，优于传统剪枝和蒸馏方法。结合不同采样技术（如DDIM、PLMS）后，性能表现一致或略优，验证其广泛适用性。实验还表明，特征缓存机制在不同模型和任务中均有效，具有良好的扩展性。
Applications
该技术适用于内容创作、虚拟现实、游戏开发等需要高效图像生成的场景。只需在现有模型基础上引入特征缓存机制，无需重新训练，即可实现显著提速。未来结合硬件优化和动态缓存调节，有望实现边缘设备上的实时生成，降低部署门槛，推动生成模型在实际场景中的广泛应用。
Limitations & Outlook
当前方法主要依赖特征缓存，可能在极端复杂或动态场景下出现缓存失效或信息丢失，影响生成效果。特征重用可能带来微小的质量下降，需在速度和质量之间权衡。此外，主要针对U-Net结构，迁移到其他架构仍需验证。未来需解决缓存管理与动态调节问题，以适应多样化应用需求。
Plain Language Accessible to non-experts
想象你在厨房做饭，很多步骤可以提前准备，比如切菜、调料。每次做菜时，你不用从头开始准备，而是用之前准备好的材料，节省时间。DeepCache就像这个厨房：它把扩散模型中每个步骤的“高层次信息”提前存起来，下次用时直接拿出来用，只需要花很少的时间更新一些“细节”。这样，整个“做菜”过程变得快很多，但菜的味道（生成质量）几乎不变。这就像用聪明的方法让厨房变得更高效，省时又好吃。
ELI14 Explained like you're 14
想象你在玩一款超级复杂的游戏，每次打boss都要花很多时间准备和战斗。可是，有没有一种方法可以记住每次打boss的套路，下次直接用这个套路，不用重新学？DeepCache就像这个“套路记忆”系统，它把游戏中每个关卡的关键技巧存起来，下次遇到类似情况时，直接用这些技巧，打得又快又好。这样一来，你就可以用更少的时间完成游戏，还能保持高分。它让复杂的事情变得简单又快，就像有个聪明的助手帮你节省了很多时间！
Abstract
Diffusion models have recently gained unprecedented attention in the field of image synthesis due to their remarkable generative capabilities. Notwithstanding their prowess, these models often incur substantial computational costs, primarily attributed to the sequential denoising process and cumbersome model size. Traditional methods for compressing diffusion models typically involve extensive retraining, presenting cost and feasibility challenges. In this paper, we introduce DeepCache, a novel training-free paradigm that accelerates diffusion models from the perspective of model architecture. DeepCache capitalizes on the inherent temporal redundancy observed in the sequential denoising steps of diffusion models, which caches and retrieves features across adjacent denoising stages, thereby curtailing redundant computations. Utilizing the property of the U-Net, we reuse the high-level features while updating the low-level features in a very cheap way. This innovative strategy, in turn, enables a speedup factor of 2.3$\times$ for Stable Diffusion v1.5 with only a 0.05 decline in CLIP Score, and 4.1$\times$ for LDM-4-G with a slight decrease of 0.22 in FID on ImageNet. Our experiments also demonstrate DeepCache's superiority over existing pruning and distillation methods that necessitate retraining and its compatibility with current sampling techniques. Furthermore, we find that under the same throughput, DeepCache effectively achieves comparable or even marginally improved results with DDIM or PLMS. The code is available at https://github.com/horseee/DeepCache