Haotong Qin

SPHQuant: Efficient extreme low bit weight quantization for Vision-Language Models

Kewei Zhang, Zheng Chen, Haotong Qin +1

Recent foundation models are moving toward native multimodal Vision-Language Models (VLMs), making VLMs a central form of next-generation foundation models. However, their large la…

QuantSR+: Pushing the Limit of Quantized Image Super-Resolution Networks

Haotong Qin, Xudong Ma, Xianglong Liu +4

Low-bit quantization is widely used to compress super-resolution (SR) models and reduce storage and computation costs for deployment on resource-limited devices. However, when SR m…

Breaking Modality Heterogeneity in Low-Bit Quantization for Large Vision-Language Models

Yi Zhong, Haotong Qin, Xindong Zhang +2

Low-bit post-training quantization (PTQ) is a pivotal technique for deploying Vision-Language Models (VLMs) on resource-constrained devices. However, existing PTQ methods often deg…

Fast-SAM3D: 3Dfy Anything in Images but Faster

Weilun Feng, Mingqiang Wu, Zhiliang Chen +10

SAM3D enables scalable, open-world 3D reconstruction from complex scenes, yet its deployment is hindered by prohibitive inference latency. In this work, we conduct the \textbf{firs…

Q-DiT4SR: Exploration of Detail-Preserving Diffusion Transformer Quantization for Real-World Image Super-Resolution

Xun Zhang, Kaicheng Yang, Hongliang Lu +3

Recently, Diffusion Transformers (DiTs) have emerged in Real-World Image Super-Resolution (Real-ISR) to generate high-quality textures, yet their heavy inference burden hinders rea…

TreeQ: Pushing the Quantization Boundary of Diffusion Transformer via Tree-Structured Mixed-Precision Search

Kaicheng Yang, Kaisen Yang, Baiting Wu +5

Diffusion Transformers (DiTs) have emerged as a highly scalable and effective backbone for image generation, outperforming U-Net architectures in both scalability and performance.…

PT $^{2}$ -LLM: Post-Training Ternarization for Large Language Models

Xianglong Yan, Chengzhu Bao, Zhiteng Li +6

Large Language Models (LLMs) have shown impressive capabilities across diverse tasks, but their large memory and compute demands hinder deployment. Ternarization has gained attenti…

Quant-dLLM: Post-Training Extreme Low-Bit Quantization for Diffusion Large Language Models

Tianao Zhang, Zhiteng Li, Xianglong Yan +3

Diffusion large language models (dLLMs), which offer bidirectional context and flexible masked-denoising generation, are emerging as a compelling alternative to autoregressive (AR)…

QuantSparse: Comprehensively Compressing Video Diffusion Transformer with Model Quantization and Attention Sparsification

Weilun Feng, Chuanguang Yang, Haotong Qin +8

Diffusion transformers exhibit remarkable video generation capability, yet their prohibitive computational and memory costs hinder practical deployment. Model quantization and atte…

RobuQ: Pushing DiTs to W1.58A2 via Robust Activation Quantization

Kaicheng Yang, Xun Zhang, Haotong Qin +4

Diffusion Transformers (DiTs) have recently emerged as a powerful backbone for image generation, demonstrating superior scalability and performance over U-Net architectures. Howeve…

FlashEdit: Decoupling Speed, Structure, and Semantics for Precise Image Editing

Junyi Wu, Zhiteng Li, Haotong Qin +2

Text-guided image editing with diffusion models has achieved remarkable quality but often suffers from prohibitive latency. We introduce \textbf{FlashEdit}, a real-time localized i…

QuantFace: Efficient Quantization for Face Restoration

Jiatong Li, Libo Zhu, Haotong Qin +5

Diffusion models have been achieving remarkable performance in face restoration. However, the heavy computations hamper the widespread adoption of these models. In this work, we pr…

Low-bit Model Quantization for Deep Neural Networks: A Survey

Kai Liu, Qian Zheng, Kaiwen Tao +9

With unprecedented rapid development, deep neural networks (DNNs) have deeply influenced almost all fields. However, their heavy computation costs and model sizes are usually unacc…

cs.CV2025★ 1 cited

QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution

Libo Zhu, Haotong Qin, Kaicheng Yang +5

One-step diffusion-based image super-resolution (OSDSR) models are showing increasingly superior performance nowadays. However, although their denoising steps are reduced to one an…