LRQ-DiT: Log-Rotation Post-Training Quantization of Diffusion Transformers for Image and Video Generation
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；School of Engineering and Applied Sciences, Harvard University（哈佛大学工程与应用科学学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
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.
FP4DiT: Towards Effective Floating Point Quantization for Diffusion Transformers
Department of Electrical and Computer Engineering, Faculty of Engineering University of Alberta（电气与计算机工程系，工程学院，阿尔伯塔大学）；Division of Computer Science and Engineering, College of Engineering Louisiana State University（计算机科学与工程系，工程学院，路易斯安那州立大学）
Q-Drift: Quantization-Aware Drift Correction for Diffusion Model Sampling
Department of Computer Science and Engineering, Seoul National University, Seoul, South Korea（计算机科学与工程系，首尔国立大学，韩国首尔）；School of Computer and Communication Sciences, EPFL, Lausanne, Switzerland（计算机与通信科学学院，瑞士洛桑联邦理工学院）；Meta Reality Labs, Redmond, USA（Meta现实实验室，美国西雅图）
Physical Autoregressive Model for Robotic Manipulation without Action Pretraining
Sun Yat-sen University（中山大学）；Guangdong Key Laboratory of Big Data Analysis and Processing（广东大数据分析与处理重点实验室）；X-Era AI Lab（X-Era人工智能实验室）；Guangdong University of Technology（广东工业大学）
Timestep-Aware SVDQuant-GPTQ for W4A4 Quantization of Wan2.2-I2V
National Engineering Research Center for Big Data Technology and System（大数据技术与系统国家工程研究中心）；Services Computing Technology and System Lab（服务计算技术与系统实验室）；Cluster and Grid Computing Lab（集群与网格计算实验室）；School of Computer Science and Technology（计算机科学与技术学院）；Huazhong University of Science and Technology（华中科技大学）
Group-of-Latents: Perceptual Video Compression at Extreme Bitrates via Masked Latent Generative Modeling
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China（多媒体信息处理国家重点实验室，计算机科学学院，北京大学，北京，中国）；State Key Discipline Laboratory of Wide Band-Gap Semiconductor Technology, School of Microelectronics, Xidian University, Xi'an, China（宽带隙半导体技术重点学科实验室，微电子学院，西安电子科技大学，西安，中国）
