Diffusion Model Quantization

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4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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Latest papers 29

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SoloQ: Calibration-Free Quantization for Diffusion Language Models
Oct 5, 2026Donghyun Lee, Arkapravo Ghosh, Varun Manjunath +5Diffusion Model Quantization KV-Cache Quantization
University of Southern California · Yale University · Korea University
CentriQ: Calibration-Free Quantization of Diffusion Transformers via Exact Mean Centering
Oct 5, 2026Nataša Jovanović, Mathieu Salzmann, Saqib JavedDiffusion Transformer Diffusion Model Quantization
Tenstorrent · EPFL
Joint Branch-Space Transform Coding for Diffusion Activation Quantization with Classifier-Free Guidance
Oct 1, 2026Mingrun Jiang, Yuejia Liu, Zishan Shao +11Classifier-Free Guidance Diffusion Model Quantization
Duke University · Carnegie Mellon University · University of Florida +2
JustQuant: You Don't Need Smoothing, SVD, or Rotation for 4-Bit Activation Quantization
Sep 27, 2026Kaicheng Yang, Kaisen Yang, Chunyu Liu +8Diffusion Model Quantization Quantization-Aware Training
Shanghai Jiao Tong University · Tsinghua University · The Hong Kong Polytechnic University
Chameleon: Dynamic Format Adapter for Efficient Diffusion
Sep 27, 2026Arnab Sanyal, Sandeep ChinchaliDiffusion Model Quantization Post-Training Quantization
UT SWARM Lab, Department of Electrical & Computer Engineering The University of Texas, Austin TX 78712
PulseQuant: Propagation-Guided Subspace Correction for 4-Bit Video Diffusion Transformers
Sep 27, 2026Yutong Wang, Xingtong Ge, Enhuai Liu +4Video Diffusion Models Diffusion Transformer
The University of Sydney · The Hong Kong University of Science and Technology · The Chinese University of Hong Kong +1
DSAQuant: Denoising-Stage-Aligned Quantization-Aware Training for Video Generation
Sep 3, 2026Shuaiting Li, Zelin Gao, Haibin Shen +3Diffusion Model Guidance Video Diffusion Models
1Robbyant · 2ZJU · 4HKUST +1
TASQ: Temporal-Adaptive Bit Sparsification Quantization for Diffusion Models
Aug 4, 2026Seokho Han, Dongwei Wang, Jinhee Kim +4Mixed-Precision Quantization Diffusion Model Quantization
Department of Electrical and Computer Engineering, Sungkyunkwan University, Korea · Department of Electrical and Computer Engineering, University of Arizona, USA · Department of Electrical and Computer Engineering, Duke University, USA +1
MXAttention: Data-Free Optimal Scaling and Pre-Normalization Quantization for MXFP4 Attention
Jul 27, 2026Jianlin Yu, Jing Lin, Linghui Kong +13Video Diffusion Models Diffusion Model Quantization
Huawei Technologies Co., Ltd.
KroQuant: Kronecker-Structured Block Transforms for Efficient Post-Training Quantization of Diffusion Transformers
Jul 23, 2026Yann Bouquet, Alireza Khodamoradi, Kristof Denolf +1Diffusion Transformer Diffusion Model Quantization
EPFL, Lausanne, Switzerland. · Advanced Micro Devices, Inc., Longmont, CO, USA.
Closing the Null Space: Guidance-Aware Quantization for Classifier-Free Diffusion
Jul 9, 2026Abdullah Al Shafi, Sumaiya Rahim SumaMixed-Precision Quantization Classifier-Free Guidance
Department of Computer Science and Engineering Khulna University of Engineering & Technology Khulna-9203, Bangladesh
FourTune: Towards Fully 4-Bit Efficient Post-Training for Diffusion Models
Jul 7, 2026Bowen Xue, Zihan Min, Xingyang Li +8Diffusion Model Quantization Quantization-Aware Training
Nunchux AI · MIT · Stanford +2
OrbitQuant: Data-Agnostic Quantization for Image and Video Diffusion Transformers
Jul 2, 2026Donghyun Lee, Jitesh Chavan, Duy Nguyen +5Video Diffusion Models Diffusion Transformer
Cantina Labs · University of Southern California · *Work done during an internship at Cantina Labs. +1
W4A4 Quantization for Inference on Wan2.2-I2V-A14B
Jun 28, 2026Yidong Chen, Chengyu Shi, Jiahao LiuDiffusion Model Quantization Post-Training Quantization
Tsinghua University
HyperQuant: A Rate-Distortion-Optimal Quantization Pipeline for Large Language and Diffusion Models
Jun 22, 2026Yuval Domb, Hadar Sackstein, Tomer SolbergLLM Quantization Diffusion Model Quantization
Realizing Native INT8 Compute for Diffusion Transformers on Consumer GPUs: A Fused INT8 GEMM Kernel for Ideogram 4.0
Jun 12, 2026Ali Asaria, Tony Salomone, Deep GandhiGPU Kernel Optimization Diffusion Transformer
Transformer Lab
Holding the FP8 Quality Ceiling at 8-Bit Weights and Activations: INT8 and GGUF Post-Training Quantization of Ideogram 4.0 for Consumer GPUs
Jun 10, 2026Deep Gandhi, Ali Asaria, Tony SalomoneMixed-Precision Quantization Diffusion Transformer
Transformer Lab
FAIR-Calib: Frontier-Aware Instability-Reweighted Calibration for Post-Training Quantization of Diffusion Large Language Models
Jun 4, 2026Haoyu Huang, Linlin Yang, Sheng Xu +5LLM Quantization Diffusion Model Quantization
National College for Excellent Engineers, Beihang University, Beijing, China · State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing, China · Independent researcher +4
STaR-Quant: State-Time Consistent Post-Training Quantization for Diffusion Large Language Models
Jun 3, 2026Xin Yan, Aqiang Wang, Zhenglin Wan +2LLM Quantization Diffusion Model Quantization
School of Artificial Intelligence, Beijing Normal University, Beijing, China · Department of Computer Science, National University of Singapore, Singapore · Centre for Frontier AI Research, Agency for Science, Technology and Research (A*STAR), Singapore
Boundary-Protection W8A8 HiFloat8 Quantization for Large-Scale Text-to-Video Diffusion Transformers
May 31, 2026Yiming ZhaoVideo Diffusion Models Diffusion Model Quantization
University of Chinese Academy of Sciences Beijing, China
Collaborative Few-Step Distillation and Low-Bit Quantization for Wan2.2 Dual-Expert Video Diffusion Models
May 30, 2026Jinyang Du, Shenghao Jin, Ziqian Xu +5Video Diffusion Models Diffusion Model Quantization
SenseTime
Timestep-Aware SVDQuant-GPTQ for W4A4 Quantization of Wan2.2-I2V
May 26, 2026Junhao Wu, Dezhong Yao, Hai JinVideo Diffusion Models Diffusion Transformer
School of Computer Science and Technology, Huazhong University of Science and Technology Wuhan, Hubei, China 430074
Quantized Keys Steal Attention: Bias Correction for KV-Cache Compression in Video Diffusion
May 25, 2026Tuna Tuncer, Felix Becker, Thomas PfeilVideo Diffusion Models Self-Attention
1Technical University of Munich · 2Tensordyne
Q-ARVD: Quantizing Autoregressive Video Diffusion Models
May 20, 2026Siao Tang, Xinyin Ma, Gongfan Fang +2Video Diffusion Models Diffusion Model Quantization
National University of Singapore · The Hong Kong Polytechnic University
DiRotQ: Rotation-Aware Quantization for 4-bit Diffusion Transformers
May 16, 2026Sayeh Sharify, Mahsa Salmani, Hesham MostafaDiffusion Transformer Diffusion Model Quantization
d-Matrix Santa Clara, CA 95054
PermuQuant: Lowering Per-Group Quantization Error by Reordering Channels for Diffusion Models
May 10, 2026Yongsen Cheng, Kai Liu, Kaiwen Tao +5Diffusion Model Quantization Post-Training Quantization
Shanghai Jiao Tong University · Huawei Noah’s Ark Lab
On the Quantization Robustness of Diffusion Language Models in Coding Benchmarks
Apr 22, 2026Aarav Gupta, Gururaj Deshpande, Chandreyi ChakrabortyMixed-Precision Quantization Diffusion Model Quantization
College of Computing · Georgia Institute of Technology
LoRaQ: Optimized Low Rank Approximation for 4-bit Quantization
Apr 20, 2026Yann Bouquet, Alireza Khodamoradi, Sophie Yáng Shen +2Mixed-Precision Quantization Diffusion Transformer
Research and Advancement Development Team, Advanced Micro Devices, Inc, USA · Computer Vision Laboratory, Ecole Polytechnique F´ed´erale de Lausanne, Lausanne, Switzerland.
Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling
Mar 18, 2026Sooyoung Ryu, Mathieu Salzmann, Saqib JavedDiffusion Model Quantization Diffusion Model Sampling
Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea · School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland · Meta Reality Labs, Redmond, USA

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SoloQ: Calibration-Free Quantization for Diffusion Language Models

Donghyun Lee, Arkapravo Ghosh, Varun Manjunath, Bumjoon Kyle Rhee+4

University of Southern California · Yale University · Korea University

Diffusion Model Quantization KV-Cache Quantization Post-Training Quantization Neural Network Quantization

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Diffusion large language models dLLMs) have emerged as a promising alternative to autoregressive language models through bidirectional diffusion-based token generation. However, their growing model sizes and high inference costs make efficient deployment challenging: full-sequence denoising repeatedly invokes compute-intensive forward passes, while block-diffusion models additionally introduce a memory-intensive KV-cache. Low-bit weight-activation quantization is therefore attractive, yet existing dLLM post-training quantization methods rely on calibration data despite activation distributions shifting across masking states and denoising steps. We present SoloQ, a calibration-free quantization framework that maps weights and activations into a normalized rotated basis with a predictable marginal distribution, enabling data-independent quantization. SoloQ combines a structured K-RPBH rotation with a lightweight rescaling correction for calibration-free quantization. Its predictable post-rotation distribution supports both distribution-matched codebooks and hardware-native NVFP4. For block-diffusion models, SoloQ further applies commit-time KV-cache quantization to compress persistent states without perturbing the actively denoised block. Across full-sequence dLLMs (LLaDA and Dream) and block-diffusion dLLMs(Fast-dLLM v2 and Nemotron-Labs-Diffusion), SoloQ retains accuracy under 4-bit quantization and outperforms calibration-based baselines on knowledge- and reasoning-intensive benchmarks. With NVFP4, SoloQ reduces peak memory by up to 2.61X and accelerates end-to-end inference by up to 2.24X.

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