Diffusion Model Inference Acceleration

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CoBit: Language Modeling with Bitstream Diffusion
May 7, 2026Georgios Batzolis, Mark Girolami, Luca AmbrogioniDiffusion Language Model Decoding Diffusion Language Models
Department of Engineering, University of Cambridge. · Devotion AI Labs Ltd. · Donders Institute for Brain, Cognition, and Behaviour, Radboud University.
Not All Tokens Need 40 Steps: Heterogeneous Step Allocation in Diffusion Transformers for Efficient Video Generation
May 7, 2026Ernie Chu, Vishal M. PatelVideo Diffusion Models Diffusion Transformer
Johns Hopkins University Baltimore, MD 21218
P-Guide: Parameter-Efficient Prior Steering for Single-Pass CFG Inference
May 7, 2026Xin Peng, Ang GaoFlow Matching Classifier-Free Guidance
School of Physical Science and Technology, Beijing University of Posts and Telecommunications, Beijing, China
DBMSolver: A Training-free Diffusion Bridge Sampler for High-Quality Image-to-Image Translation
May 7, 2026Sankarshana Venugopal, Mohammad Mostafavi, Jonghyun ChoiImage-to-Image Translation Diffusion Sampling
Seoul National University · ECE, IPAI, ASRI in SNU
Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation
May 5, 2026Bin Wu, Mengqi Huang, Shaojin Wu +4Video Diffusion Models Diffusion Model Distillation
University of Science and Technology of China · Independent Researcher
Tempered Guided Diffusion
May 5, 2026Andreas Makris, Paul Fearnhead, Chris NemethDiffusion Model Sampling Diffusion-Based Inverse Problems
Department of Mathematics and Statistics Lancaster University, UK · Department of Mathematics and Statistics2026 Lancaster University, UK
Performance Optimization and Comparative Analysis of Generative AI Models on Advanced Accelerators
May 5, 2026Amitash Nanda, Javier Hernandez Nicolau, Madhusudan Gujral +3Mixed-Precision Quantization AI Accelerator Inference
University of California San Diego, San Diego, CA, USA · San Diego Supercomputer Center, San Diego, CA, USA
Linearizing Vision Transformer with Test-Time Training
May 4, 2026Yining Li, Dongchen Han, Zeyu Liu +3Efficient Transformer Inference Self-Attention
Tsinghua University, Beijing, China
Mamoda2.5: Enhancing Unified Multimodal Model with DiT-MoE
May 4, 2026Yangming Shi, Shixiang Zhu, Tao Shen +14Diffusion Transformer Unified Multimodal Models
Mamoda Team, ByteDance
SlimDiffSR: Toward Lightweight and Efficient Remote Sensing Image Super-Resolution via Diffusion Model Distillation
May 4, 2026Ce Wang, Zhenyu Hu, Wanjie SunDiffusion Model Distillation Remote Sensing Image Super-Resolution
School of Remote Sensing and Information Engineering, Wuhan University; Wuhan 430079, China
SpecEdit: Training-Free Acceleration for Diffusion based Image Editing via Semantic Locking
May 4, 2026Zhengan Yan, Shikang Zheng, Haoran Qin +9Diffusion Sampling Diffusion-Based Image Editing
EPIC Lab , Shanghai Jiao Tong University · Shandong University · Alibaba Group +2
Motion-Aware Caching for Efficient Autoregressive Video Generation
May 3, 2026Jing Xu, Yuexiao Ma, Xuzhe Zheng +7Video Diffusion Models KV-Cache Management
1Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China · 2ByteDance · 3Max Planck Institute for Intelligent Systems, 4ELLIS Institute Tbingen, 5Tbingen AI Center
Focus on the Core: Empowering Diffusion Large Language Models by Self-Contrast
May 2, 2026Jinyuan Feng, Xin Yu, Yiqun Chen +5Diffusion Language Model Inference Diffusion Language Model Decoding
Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · Renmin University of China +1
DARE: Diffusion Language Model Activation Reuse for Efficient Inference
May 1, 2026Natalia Frumkin, Bokun Wang, Hung-Yueh Chiang +3Self-Attention Diffusion Language Model Inference
Chandra Family Department of Electrical and Computer Engineering The University of Texas at Austin · Cornell University
GD4: Graph-based Discrete Denoising Diffusion for MIMO Detection
May 1, 2026Qincheng Lu, Sitao Luan, Xiao-Wen ChangDiffusion Model Inference Acceleration Diffusion Models
School of Computer Science McGill University Montreal, Canada · University of Montreal Mila - Quebec AI Institute Montreal, Canada
YOSE: You Only Select Essential Tokens for Efficient DiT-based Video Object Removal
Apr 30, 2026Chenyang Wu, Lina Lei, Fan Li +6Diffusion Transformer Video Editing
1VCIP, CS, Nankai University · 3NKIARI, Shenzhen Futian · 2Huawei Noah’s Ark Lab
Beyond Fixed Formulas: Data-Driven Linear Predictor for Efficient Diffusion Models
Apr 29, 2026Zhirong Shen, Rui Huang, Jiacheng Liu +6Diffusion Transformer Diffusion Model Caching
Shanghai Jiao Tong University · University of Electronic Science and Technology of China · Shandong University +2
Characterizing Vision-Language-Action Models across XPUs: Constraints and Acceleration for On-Robot Deployment
Apr 27, 2026Kaijun Zhou, Qiwei Chen, Da Peng +3AI Accelerator Inference Edge Inference
School of Computer Science, Shanghai Jiao Tong University
Latent Inter-Frame Pruning: A Training-Free Method Bridging Traditional Video Compression and Modern Diffusion Transformers for Efficient Generation
Apr 26, 2026Dennis Menn, Chih-Hsien ChouVideo Diffusion Models Token Pruning
The University of Texas at Austin · Futurewei Technologies, Inc.
$Z^2$ -Sampling: Zero-Cost Zigzag Trajectories for Semantic Alignment in Diffusion Models
Apr 26, 2026Haosen Li, Wenshuo Chen, Shaofeng Liang +3Diffusion Model Alignment Diffusion Sampling
1The Hong Kong University of Science and Technology (Guangzhou) · 2Griffith University & Data61/CSIRO
VS-DDPM: Efficient Low-Cost Diffusion Model for Medical Modality Translation
Apr 24, 2026Nikoo Moradi, Gijs Luijten, Behrus Hinrichs-Puladi +4Image-to-Image Translation 3D Medical Imaging
Institute for Artificial Intelligence in Medicine (IKIM), Essen University Hospital (AöR), University of Duisburg-Essen, Essen, Germany · Institute of Computer Graphics and Vision, Graz University of Technology, Inffeldgasse 16, Graz, 8010, Austria · Center for Virtual and Extended Reality in Medicine (ZvRM), University Hospital Essen (AöR), Essen, Germany +7
Accelerating Frequency Domain Diffusion Models with Error-Feedback Event-Driven Caching
Apr 24, 2026Dong Liu, Haisheng Wang, Yanxuan YuTime Series Generation Diffusion Model Caching
University of California, Los Angeles · Yale University · East China Normal University +1
MISTY: High-Throughput Motion Planning via Mixer-based Single-step Drifting
Apr 23, 2026Yining Xing, Zehong Ke, Yiqian Tu +3Trajectory Generation Diffusion-Based Planning
School of Vehicle and Mobility, Tsinghua University, Beijing, China · State Key Laboratory of Intelligent Green Vehicle and Mobility, Tsinghua University, Beijing, China
Sparse Forcing: Native Trainable Sparse Attention for Real-time Autoregressive Diffusion Video Generation
Apr 23, 2026Boxun Xu, Yuming Du, Zichang Liu +7Autoregressive Diffusion Self-Attention
Meta Superintelligence Labs · University of California, Santa Barbara · Work done at Meta
DynamicRad: Content-Adaptive Sparse Attention for Long Video Diffusion
Apr 22, 2026Yongji Long, Shijun Liang, Jintao Li +1Video Diffusion Models Long-Horizon Video Generation
University of Electronic Science and Technology of China · Shenzhen Institute for Advanced Study, UESTC · Computational Mathematics, Science, & Engineering at Michigan State University (MSU)
X-Cache: Cross-Chunk Block Caching for Few-Step Autoregressive World Models Inference
Apr 22, 2026Yixiao Zeng, Jianlei Zheng, Chaoda Zheng +10Video Diffusion Models Diffusion Model Caching
AI Infra Team, XPeng Inc.
FASTER: Value-Guided Sampling for Fast RL
Apr 21, 2026Perry Dong, Alexander Swerdlow, Dorsa Sadigh +1Diffusion Sampling Diffusion Policy
Stanford University
Denoising, Fast and Slow: Difficulty-Aware Adaptive Sampling for Image Generation
Apr 21, 2026Johannes Schusterbauer, Ming Gui, Yusong Li +3Diffusion Model Sampling Diffusion Sampling
NI Sampling: Accelerating Discrete Diffusion Sampling by Token Order Optimization
Apr 20, 2026Enshu Liu, Xuefei Ning, Yu Wang +1Few-Step Diffusion Sampling Diffusion Language Model Decoding
Department of EE, Tsinghua University · Microsoft Research
One-Step Diffusion with Inverse Residual Fields for Unsupervised Industrial Anomaly Detection
Apr 20, 2026Boan Zhang, Wen Li, Guanhua Yu +3Industrial Anomaly Detection Unsupervised Anomaly Detection
Department of Computer Science and Technology Xidian University Shaanxi, China 710126 · Department of Electronic Engineering Xidian University Shaanxi, China 710126

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CoBit: Language Modeling with Bitstream Diffusion

Georgios Batzolis, Mark Girolami, Luca Ambrogioni

Department of Engineering, University of Cambridge. · Devotion AI Labs Ltd. · Donders Institute for Brain, Cognition, and Behaviour, Radboud University.

Diffusion Language Model Decoding Diffusion Language Models Continuous Diffusion Language Models Diffusion Model Inference Acceleration Diffusion Models

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Diffusion language models (DLMs) promise parallel, order-agnostic generation, but on standard benchmarks they have historically lagged behind autoregressive models in sample quality and diversity. Recent continuous flow and diffusion approaches have narrowed this gap. In this work, we further close the autoregressive gap by modeling text as a continuous diffusion process over fixed-width binary bitstreams. We refer to the resulting model as CoBit (Continuous Bitstream Diffusion). Our approach represents semantic tokens as analog bit sequences and uses a matched-filter residual parameterization to isolate contextual learning from analytic independent-bit posteriors. Crucially, we adopt a stochastic sampler that applies Langevin-type corrections gated by the entropy-rate profile, concentrating stochasticity in high-information regions while remaining nearly deterministic elsewhere. On LM1B, our 130M-parameter model reaches a generative perplexity (GenPPL) of 59.76 at matched real-data entropy (4.31) using 256 neural function evaluations (NFEs), outperforming prior DLM baselines and reaching the autoregressive reference. On OpenWebText (OWT), our sampler establishes a new continuous-DLM Pareto frontier, achieving GenPPL 27.06 at entropy 5.26 using 4x fewer steps than previous 1024-NFE baselines. Scaling the same recipe to a 462M-parameter model (CoBit-M) further improves the OWT GenPPL-entropy frontier over the 130M model (CoBit-S) and over medium-scale continuous and discrete DLM baselines, reaching GenPPL 19.5 at entropy 5.40, near real-data entropy (5.44), and approaching pretrained GPT-2 Medium over the high-quality region. As an additional benefit, bitstream diffusion removes the O(V) vocabulary scaling bottleneck of standard DLMs: by predicting O(log V) bitwise logits via semantic bit-patching, it lowers memory and raises throughput, a scalable paradigm as vocabulary sizes grow.

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