Latent Diffusion Models

Follow this topic Ask about this topic

Momentum

9 papers in the last four weeks, up 13% on the four weeks before. 0.1% of all new papers.

Jul 13Week of Sep 28

Latest papers 143

All topics

PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion
May 22, 2026Yifan Lu, Qi Wu, Jay Zhangjie Wu +4Latent Diffusion Models Pixel-Space Diffusion Models
NVIDIA
DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders
May 21, 2026Tianhang Wang, Yitong Chen, Wei Song +3Latent Diffusion Models Autoencoders
Zhejiang University · Shanghai Innovation Institute · Fudan University +1
PIU: Proximity-guided Identity Unlearning in ID-Conditioned Diffusion Models
May 21, 2026Jose Edgar Hernandez Cancino Estrada, Mauro Díaz Lupone, Žiga Emeršič +3Latent Diffusion Models Image Generation
University of Ljubljana, Faculty of Computer and Information Science, Ljubljana, Slovenia · University of Ljubljana, Faculty of Electrical Engineering, Ljubljana, Slovenia
Spatial Gram Alignment for Ultra-High-Resolution Image Synthesis
May 20, 2026Jinjin Zhang, Xiefan Guo, Di HuangDiffusion Model Alignment Latent Diffusion Models
Beihang University
EPC-3D-Diff: Equivariant Physics Consistent Conditional 3D Latent Diffusion for CBCT to CT Synthesis
May 19, 2026Alzahra Altalib, Chunhui Li, Haytham Ahmad Alewaidat +3Physics-Informed Diffusion Models Cone-Beam CT
School of Science and Engineering, University of Dundee UK · Faculty of Applied Medical Sciences, Jordan University of Science and Technology · Experia Healthcare, Jordan +1
AirfoilGen: A valid-by-construction and performance-aware latent diffusion model for airfoil generation
May 19, 2026Zhijie Yang, Min Tang, Peng Du +1Latent Diffusion Models Conditional Diffusion Models
State Key Laboratory of CAD&CG, Zhejiang University, Hangzhou, 310027, China
Stable Audio 3
May 18, 2026Zach Evans, Julian D. Parker, Matthew Rice +4Text-to-Audio Generation Latent Diffusion Models
Stability AI
VAMP-Diff: VampPrior Latent Diffusion for Photoplethysmography Modeling
May 17, 2026Fatemeh Ghasemi Balouei, Nathan Willemsen, Mahesh Banavar +1Latent Diffusion Models Photoplethysmography
Department of Electrical and Computer Engineering, Clarkson University, Potsdam, USA · Data Science, Worcester Polytechnic Institute, Worcester, USA · Department of Computer Science, Data Science, and Artificial Intelligence, Worcester Polytechnic Institute, Worcester, USA
The Learnability Gap in Medical Latent Diffusion
May 16, 2026Mischa Dombrowski, Felix Nützel, Bernhard KainzLatent Diffusion Models Medical Image Classification
FAU Erlangen-Nürnberg, Erlangen, DE · Department of Computing, Imperial College London, London, UK
Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine
May 16, 2026Wei Huang, Andi Han, Mingyuan Bai +4Latent Diffusion Models Manifold Learning
RIKEN AIP & The Institute of Statistical Mathematics · University of Sydney · Agency for Science, Technology and Research & The Institute of Statistical Mathematics +4
Face inpainting with Identity Preserving Latent Diffusion Models
May 15, 2026João Santos, Carlos Santiago, Manuel MarquesLatent Diffusion Models Identity-Preserving Image Generation
Institute for Systems and Robotics, Instituto Superior Técnico, Lisboa, Portugal
Efficient Image Synthesis with Sphere Latent Encoder
May 15, 2026Tung Do, Thuan Hoang Nguyen, Hao LiLatent Diffusion Models Few-Step Diffusion Sampling
Mohamed Bin Zayed University of Artificial Intelligence, UAE
Di-BiLPS: Denoising induced Bidirectional Latent-PDE-Solver under Sparse Observations
May 13, 2026Zhonghao Li, Chaoyu Liu, Qian ZhangNeural PDE Solvers Latent Diffusion Models
School of Science, Harbin Institute of Technology, Shenzhen, China · Department of Theoretical Physics and Applied Mathematics, University of Cambridge, Cambridge, United Kingdom
On the Limits of Latent Reuse in Diffusion Models
May 13, 2026Yifeng Yu, Lu YuLatent Diffusion Models Domain Adaptation
Department of Mathematical Sciences, Tsinghua University · Department of Data Science, City University of Hong Kong
Seconds-Aligned PCA-DAC Latent Diffusion for Symbolic-to-Audio Drum Rendering
May 13, 2026Konstantinos Soiledis, Maximos Kaliakatsos Papakostas, Dimos Makris +1Latent Diffusion Models Symbolic Music Generation
1Dept. of Music Technology and Acoustics, Hellenic Mediterranean University, Rethymno & Athens, Greece · 2Athena RC, Athens, Greece
Generative Motion In-betweening by Diffusion over Continuous Implicit Representations
May 12, 2026Shiyu Fan, Paul Henderson, Edmond S. L. HoImplicit Neural Representations Latent Diffusion Models
School of Computing Science, University of Glasgow, United Kingdom.
L2P: Unlocking Latent Potential for Pixel Generation
May 12, 2026Zhennan Chen, Junwei Zhu, Xu Chen +7Latent Diffusion Models Pixel-Space Diffusion Models
Nanjing University · Tencent Youtu Lab · Hainan-biuh +1
LatentHDR: Decoupling Exposure from Diffusion via Conditional Latent-to-Latent Mapping for Text/Image-to-Panoramic HDR
May 11, 2026Pedram Fekri, WenChen Li, William Chen +1Latent Diffusion Models HDR Imaging
Monks AI Research Lab, Toronto, Ontario, Canada
PoDAR: Power-Disentangled Audio Representation for Generative Modeling
May 11, 2026Alejandro Luebs, Mithilesh Vaidya, Ishaan Kumar +5Audio Representation Learning Latent Diffusion Models
Descript
Supersampling Stable Diffusion and Beyond: A Seamless, Training-Free Approach for Scaling Neural Networks Using Common Interpolation Methods
May 9, 2026Md Abu Obaida Zishan, Jannatun Noor, Annajiat Alim RaselLatent Diffusion Models Diffusion-Based Image Generation
School of Data and Sciences BRAC University, Dhaka · Computing for Sustainability and Social Good (C2SG) Research Group Department of Computer Science and Engineering United Internation University, Dhaka
Generative 3D Gaussians with Learned Density Control
May 8, 2026Runjie Yan, Yan-Pei Cao, Peng Wang +23D Gaussian Splatting 3D Asset Generation
Institute for Interdisciplinary Information Sciences, Tsinghua University, China · VAST
How to Train Your Latent Diffusion Language Model Jointly With the Latent Space
May 8, 2026Viacheslav Meshchaninov, Alexander Shabalin, Egor Chimbulatov +4Non-Autoregressive Text Generation Latent Diffusion Models
Constructor University, Bremen, Germany · HSE University, Moscow, Russia · Applied AI Institute, Moscow, Russia +2
What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion
May 8, 2026Zhengrong Yue, Taihang Hu, Mengting Chen +8Latent Diffusion Models Autoencoders
Shanghai Jiao Tong University · Alibaba Group · Beihang University +4
Consistency Regularised Gradient Flows for Inverse Problems
May 8, 2026Alessio Spagnoletti, Tim Y. J. Wang, Marcelo Pereyra +1Latent Diffusion Models Wasserstein Gradient Flows
Laboratoire MAP5, UMR 8145, Université Paris Cité, CNRS · Department of Mathematics, Imperial College London · Heriot-Watt University, MACS & Maxwell Institute
TextLDM: Language Modeling with Continuous Latent Diffusion
May 8, 2026Jiaxiu Jiang, Jingjing Ren, Wenbo Li +10Diffusion Transformer Latent Diffusion Models
Joy Future Academy · HIT · HKUST(GZ)
Continuous Latent Diffusion Language Model
May 7, 2026Hongcan Guo, Qinyu Zhao, Yian Zhao +8Non-Autoregressive Text Generation Diffusion Transformer
ByteDance Seed · The Australian National University · Peking University +2
Reconstruction or Semantics? What Makes a Latent Space Useful for Robotic World Models
May 7, 2026Nilaksh, Saurav Jha, Artem Zholus +1World Model Learning World Models for Robotics
Chandar Research Lab · Mila – Quebec AI Institute · Polytechnique Montréal +1
Fusion in Your Way: Aligning Image Fusion with Heterogeneous Demands via Direct Preference Optimization
May 7, 2026Weijian Su, Songqian Zhang, Yuqi Han +3Diffusion Model Alignment Multi-Source Image Fusion
School of Computer Science and Technology, Dalian University of Technology · Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education · Institute of Image Processing and Understanding, North Minzu University
Deep Dreams Are Made of This: Visualizing Monosemantic Features in Diffusion Models
May 6, 2026Adam Szokalski, Mateusz ModrzejewskiDisentangled Representation Learning Latent Diffusion Models
Institute of Computer Science, Warsaw University of Technology, Warsaw, Poland
MuCALD-SplitFed: Causal-Latent Diffusion for Privacy-Preserving Multi-Task Split-Federated Medical Image Segmentation
May 4, 2026Chamani Shiranthika, Hadi Hadizadeh, Parvaneh SaeediLatent Diffusion Models Split Federated Learning
School of Engineering Science, Simon Fraser University, Burnaby, BC, Canada

May 22, 2026, cs.CVJ/K move · Enter open · S save

PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion

Yifan Lu, Qi Wu, Jay Zhangjie Wu, Zian Wang+3

NVIDIA

Latent Diffusion Models Pixel-Space Diffusion Models Diffusion Model Inference Acceleration Image Super-Resolution Diffusion Models

Open paper AskPDF arXiv

Most practical high-resolution text-to-image systems, including latent diffusion and autoregressive models, perform generation in a compact latent space, and a decoder maps the generated latents back to pixels. Yet the latent-to-pixel decoder is reconstruction-oriented, optimized to invert the encoder rather than synthesize more details, and becomes increasingly costly at megapixel scale. This drawback calls for a more expressive and efficient decoding paradigm. Motivated by recent progress in scalable pixel-space diffusion, we introduce PiD, a Pixel diffusion Decoder that reformulates latent decoding as conditional pixel diffusion, unifying decoding and upsampling into one generative module. By denoising directly in high-resolution pixel space, PiD synthesizes

4\times

and even

8\times

upscaled images with low latency. For latent conditioning, a lightweight sigma-aware adapter injects noise-corrupted latents into the pixel diffusion backbone, enabling PiD to decode partially denoised latents and terminate the latent diffusion process early. To further improve efficiency, we distill the model using DMD2, reducing inference to just 4 steps. PiD applies to both conventional VAE latents and semantic latents (e.g., SigLIP, DINOv2) used in recent RAE-based models. PiD decodes latents of

512 \times 512

images into

2048 \times 2048

pixels in under 1 second with 13 GB peak memory on a consumer RTX 5090, and as fast as 210 ms on a GB200 GPU, about

6\times

faster than cascaded diffusion-based super-resolution pipelines with better visual fidelity.

Momentum

Related topics

Latest papers 143