LRQ-DiT: Log-Rotation Post-Training Quantization of Diffusion Transformers for Image and Video Generation

• URL: http://arxiv.org/abs/2508.03485v3
• Date: Tue, 23 Sep 2025 05:30:13 GMT
• Title: LRQ-DiT: Log-Rotation Post-Training Quantization of Diffusion Transformers for Image and Video Generation
• Authors: Lianwei Yang, Haokun Lin, Tianchen Zhao, Yichen Wu, Hongyu Zhu, Ruiqi Xie, Zhenan Sun, Yu Wang, Qingyi Gu,
• Abstract summary: Diffusion Transformers (DiTs) have achieved impressive performance in text-to-image and text-to-video generation.<n>DiTs' high computational cost and large parameter sizes pose significant challenges for usage in resource-constrained scenarios.<n>We propose LRQ-DiT, an efficient and accurate post-training quantization framework for image and video generation.
• Score: 41.66473889057111
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion Transformers (DiTs) have achieved impressive performance in text-to-image and text-to-video generation. However, their high computational cost and large parameter sizes pose significant challenges for usage in resource-constrained scenarios. Effective compression of models has become a crucial issue that urgently needs to be addressed. Post-training quantization (PTQ) is a promising solution to reduce memory usage and accelerate inference, but existing PTQ methods suffer from severe performance degradation under extreme low-bit settings. After experiments and analysis, we identify two key obstacles to low-bit PTQ for DiTs: (1) the weights of DiT models follow a Gaussian-like distribution with long tails, causing uniform quantization to poorly allocate intervals and leading to significant quantization errors. This issue has been observed in the linear layer weights of different DiT models, which deeply limits the performance. (2) two types of activation outliers in DiT models: (i) Mild Outliers with slightly elevated values, and (ii) Salient Outliers with large magnitudes concentrated in specific channels, which disrupt activation quantization. To address these issues, we propose LRQ-DiT, an efficient and accurate post-training quantization framework for image and video generation. First, we introduce Twin-Log Quantization (TLQ), a log-based method that allocates more quantization intervals to the intermediate dense regions, effectively achieving alignment with the weight distribution and reducing quantization errors. Second, we propose an Adaptive Rotation Scheme (ARS) that dynamically applies Hadamard or outlier-aware rotations based on activation fluctuation, effectively mitigating the impact of both types of outliers. Extensive experiments on various text-to-image and text-to-video DiT models demonstrate that LRQ-DiT preserves high generation quality.

Related papers

• AdaTSQ: Pushing the Pareto Frontier of Diffusion Transformers via Temporal-Sensitivity Quantization [22.45250803905198]
  Diffusion Transformers (DiTs) have emerged as the state-of-the-art backbone for high-fidelity image and video generation.<n>Post-training quantization (PTQ) has proven effective for large language models (LLMs)<n>We propose AdaTSQ, a novel PTQ framework that pushes the frontier of efficiency and quality by exploiting the temporal sensitivity of DiTs.
  arXiv  Detail & Related papers  (2026-02-10T15:23:18Z)
• LSGQuant: Layer-Sensitivity Guided Quantization for One-Step Diffusion Real-World Video Super-Resolution [52.627063566555194]
  We introduce LSGQuant, a layer-sensitivity guided quantizing approach for one-step diffusion-based real-world VSR.<n>Our method incorporates a Dynamic Range Adaptive Quantizer (DRAQ) to fit video token activations.<n>Our method has nearly performance to origin model with full-precision and significantly exceeds existing quantization techniques.
  arXiv  Detail & Related papers  (2026-02-03T06:53:19Z)
• DMQ: Dissecting Outliers of Diffusion Models for Post-Training Quantization [29.066284789131494]
  Recent post-training quantization methods overlook outliers, leading to degraded performance at low bit-widths.<n>We propose a DMQ which combines Learned Equivalent Scaling and channel-wise Power-of-Two Scaling.<n>Our method significantly outperforms existing works, especially at low bit-widths.
  arXiv  Detail & Related papers  (2025-07-17T09:15:29Z)
• MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation [74.34220141721231]
  We present MPQ-DMv2, an improved textbfMixed textbfPrecision textbfQuantization framework for extremely low-bit textbfDiffusion textbfModels.
  arXiv  Detail & Related papers  (2025-07-06T08:16:50Z)
• BASE-Q: Bias and Asymmetric Scaling Enhanced Rotational Quantization for Large Language Models [16.720321201956157]
  BASE-Q is a simple yet powerful approach that combines bias correction and asymmetric scaling to reduce rounding and clipping errors.<n>Experiments demonstrate the effectiveness of BASE-Q, narrowing the accuracy gap to full-precision models by 50.5%, 42.9%, and 29.2% compared to QuaRot, SpinQuant, and OSTQuant, respectively.
  arXiv  Detail & Related papers  (2025-05-26T14:22:21Z)
• RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models [53.571195477043496]
  We propose an algorithm named Rotated Straight-Through-Estimator (RoSTE)<n>RoSTE combines quantization-aware supervised fine-tuning (QA-SFT) with an adaptive rotation strategy to reduce activation outliers.<n>Our findings reveal that the prediction error is directly proportional to the quantization error of the converged weights, which can be effectively managed through an optimized rotation configuration.
  arXiv  Detail & Related papers  (2025-02-13T06:44:33Z)
• MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models [37.061975191553]
  This paper presents MPQ-DM, a Mixed-Precision Quantization method for Diffusion Models.<n>To mitigate the quantization error caused by outlier severe weight channels, we propose an Outlier-Driven Mixed Quantization technique.<n>To robustly learn representations crossing time steps, we construct a Time-Smoothed Relation Distillation scheme.
  arXiv  Detail & Related papers  (2024-12-16T08:31:55Z)
• PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution [95.98801201266099]
  Diffusion-based image super-resolution (SR) models have shown superior performance at the cost of multiple denoising steps.<n>We propose a novel post-training quantization approach with adaptive scale in one-step diffusion (OSD) image SR, PassionSR.<n>Our PassionSR achieves significant advantages over recent leading low-bit quantization methods for image SR.
  arXiv  Detail & Related papers  (2024-11-26T04:49:42Z)
• Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers [45.762142897697366]
  Post-Training Quantization (PTQ) emerges as a promising solution, enabling model compression and accelerated inference for pretrained models. Research on DiT quantization remains sparse, and existing PTQ frameworks tend to suffer from biased quantization, leading to notable performance degradation. We propose Q-DiT, a novel approach that seamlessly integrates two key techniques: automatic quantization granularity allocation to handle the significant variance of weights and activations across input channels, and sample-wise dynamic activation quantization to adaptively capture activation changes across both timesteps and samples.
  arXiv  Detail & Related papers  (2024-06-25T07:57:27Z)
• 2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution [83.09117439860607]
  Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment. It is notorious that low-bit quantization degrades the accuracy of SR models compared to their full-precision (FP) counterparts. We present a dual-stage low-bit post-training quantization (PTQ) method for image super-resolution, namely 2DQuant, which achieves efficient and accurate SR under low-bit quantization.
  arXiv  Detail & Related papers  (2024-06-10T06:06:11Z)
• CBQ: Cross-Block Quantization for Large Language Models [66.82132832702895]
  Post-training quantization (PTQ) has played a key role in compressing large language models (LLMs) with ultra-low costs.<n>We propose CBQ, a cross-block reconstruction-based PTQ method for LLMs.<n> CBQ employs a cross-block dependency using a reconstruction scheme, establishing long-range dependencies across multiple blocks to minimize error accumulation.
  arXiv  Detail & Related papers  (2023-12-13T07:56:27Z)
• Q-Diffusion: Quantizing Diffusion Models [52.978047249670276]
  Post-training quantization (PTQ) is considered a go-to compression method for other tasks. We propose a novel PTQ method specifically tailored towards the unique multi-timestep pipeline and model architecture. We show that our proposed method is able to quantize full-precision unconditional diffusion models into 4-bit while maintaining comparable performance.
  arXiv  Detail & Related papers  (2023-02-08T19:38:59Z)
• Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks [82.18396309806577]
  We propose a novel activation quantizer, referred to as Dynamic Dual Trainable Bounds (DDTB) Our DDTB exhibits significant performance improvements in ultra-low precision. For example, our DDTB achieves a 0.70dB PSNR increase on Urban100 benchmark when quantizing EDSR to 2-bit and scaling up output images to x4.
  arXiv  Detail & Related papers  (2022-03-08T04:26:18Z)
• Fully Quantized Image Super-Resolution Networks [81.75002888152159]
  We propose a Fully Quantized image Super-Resolution framework (FQSR) to jointly optimize efficiency and accuracy. We apply our quantization scheme on multiple mainstream super-resolution architectures, including SRResNet, SRGAN and EDSR. Our FQSR using low bits quantization can achieve on par performance compared with the full-precision counterparts on five benchmark datasets.
  arXiv  Detail & Related papers  (2020-11-29T03:53:49Z)

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.