CacheQuant: Comprehensively Accelerated Diffusion Models

• URL: http://arxiv.org/abs/2503.01323v1
• Date: Mon, 03 Mar 2025 09:04:51 GMT
• Title: CacheQuant: Comprehensively Accelerated Diffusion Models
• Authors: Xuewen Liu, Zhikai Li, Qingyi Gu,
• Abstract summary: CacheQuant is a novel training-free paradigm that comprehensively accelerates diffusion models by jointly optimizing model caching and quantization techniques.<n> Experimental results show that CacheQuant achieves a 5.18 speedup and 4 compression for Stable Diffusion on MS-COCO, with only a 0.02 loss in CLIP score.
• Score: 3.78219736760145
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models have gradually gained prominence in the field of image synthesis, showcasing remarkable generative capabilities. Nevertheless, the slow inference and complex networks, resulting from redundancy at both temporal and structural levels, hinder their low-latency applications in real-world scenarios. Current acceleration methods for diffusion models focus separately on temporal and structural levels. However, independent optimization at each level to further push the acceleration limits results in significant performance degradation. On the other hand, integrating optimizations at both levels can compound the acceleration effects. Unfortunately, we find that the optimizations at these two levels are not entirely orthogonal. Performing separate optimizations and then simply integrating them results in unsatisfactory performance. To tackle this issue, we propose CacheQuant, a novel training-free paradigm that comprehensively accelerates diffusion models by jointly optimizing model caching and quantization techniques. Specifically, we employ a dynamic programming approach to determine the optimal cache schedule, in which the properties of caching and quantization are carefully considered to minimize errors. Additionally, we propose decoupled error correction to further mitigate the coupled and accumulated errors step by step. Experimental results show that CacheQuant achieves a 5.18 speedup and 4 compression for Stable Diffusion on MS-COCO, with only a 0.02 loss in CLIP score. Our code are open-sourced: https://github.com/BienLuky/CacheQuant .

Related papers

• AB-Cache: Training-Free Acceleration of Diffusion Models via Adams-Bashforth Cached Feature Reuse [19.13826316844611]
  Diffusion models have demonstrated remarkable success in generative tasks, yet their iterative denoising process results in slow inference. We provide a theoretical understanding by analyzing the denoising process through the second-order Adams-Bashforth method. We propose a novel caching-based acceleration approach for diffusion models, instead of directly reusing cached results.
  arXiv  Detail & Related papers  (2025-04-13T08:29:58Z)
• QuantCache: Adaptive Importance-Guided Quantization with Hierarchical Latent and Layer Caching for Video Generation [84.91431271257437]
  Diffusion Transformers (DiTs) have emerged as a dominant architecture in video generation. DiTs come with significant drawbacks, including increased computational and memory costs. We propose QuantCache, a novel training-free inference acceleration framework.
  arXiv  Detail & Related papers  (2025-03-09T10:31:51Z)
• One-Step Diffusion Model for Image Motion-Deblurring [85.76149042561507]
  We propose a one-step diffusion model for deblurring (OSDD), a novel framework that reduces the denoising process to a single step. To tackle fidelity loss in diffusion models, we introduce an enhanced variational autoencoder (eVAE), which improves structural restoration. Our method achieves strong performance on both full and no-reference metrics.
  arXiv  Detail & Related papers  (2025-03-09T09:39:57Z)
• Q&C: When Quantization Meets Cache in Efficient Image Generation [24.783679431414686]
  We find that the combination of quantization and cache mechanisms for Diffusion Transformers (DiTs) is not straightforward.<n>We propose a hybrid acceleration method by tackling the above challenges.<n>Our method has accelerated DiTs by 12.7x while preserving competitive generation capability.
  arXiv  Detail & Related papers  (2025-03-04T11:19:02Z)
• Learning-to-Cache: Accelerating Diffusion Transformer via Layer Caching [56.286064975443026]
  We make an interesting and somehow surprising observation: the computation of a large proportion of layers in the diffusion transformer, through a caching mechanism, can be readily removed even without updating the model parameters. We introduce a novel scheme, named Learningto-Cache (L2C), that learns to conduct caching in a dynamic manner for diffusion transformers. Experimental results show that L2C largely outperforms samplers such as DDIM and DPM-r, alongside prior cache-based methods at the same inference speed.
  arXiv  Detail & Related papers  (2024-06-03T18:49:57Z)
• QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning [52.157939524815866]
  In this paper, we empirically unravel three properties in quantized diffusion models that compromise the efficacy of current methods. We identify two critical types of quantized layers: those holding vital temporal information and those sensitive to reduced bit-width. Our method is evaluated over three high-resolution image generation tasks and achieves state-of-the-art performance under various bit-width settings.
  arXiv  Detail & Related papers  (2024-02-06T03:39:44Z)
• DeepCache: Accelerating Diffusion Models for Free [65.02607075556742]
  DeepCache is a 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. Under the same throughput, DeepCache effectively achieves comparable or even marginally improved results with DDIM or PLMS.
  arXiv  Detail & Related papers  (2023-12-01T17:01:06Z)
• EfficientDM: Efficient Quantization-Aware Fine-Tuning of Low-Bit Diffusion Models [21.17675493267517]
  Post-training quantization (PTQ) and quantization-aware training (QAT) are two main approaches to compress and accelerate diffusion models. We introduce a data-free and parameter-efficient fine-tuning framework for low-bit diffusion models, dubbed EfficientDM, to achieve QAT-level performance with PTQ-like efficiency. Our method significantly outperforms previous PTQ-based diffusion models while maintaining similar time and data efficiency.
  arXiv  Detail & Related papers  (2023-10-05T02:51:53Z)
• Hessian-Free High-Resolution Nesterov Acceleration for Sampling [55.498092486970364]
  Nesterov's Accelerated Gradient (NAG) for optimization has better performance than its continuous time limit (noiseless kinetic Langevin) when a finite step-size is employed. This work explores the sampling counterpart of this phenonemon and proposes a diffusion process, whose discretizations can yield accelerated gradient-based MCMC methods.
  arXiv  Detail & Related papers  (2020-06-16T15:07:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.