Pixel-Space Diffusion Models

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9 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

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Latest papers 39

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BudgetPix: Compute-Adaptive Tokenization for Pixel-Space Image Diffusion
Oct 8, 2026Ozgur Kara, Yujia Chen, Daniel Watson +4Image Tokenization Diffusion Model Inference Acceleration
University of Illinois Urbana-Champaign · Google
CRISP: Fixing Flying Pixels in Latent LiDAR Generation via Diffusion Decoding
Oct 8, 2026Andrea Ceron, Michael Schmidt, Alvaro Marcos-Ramiro +2Pixel-Space Diffusion Models Latent Diffusion Models
TU München · BMW AG
Think Before You Paint: Recursive Latent Reasoning for Diffusion Models
Oct 7, 2026Paweł Skierś, Małgorzata Grzanka, Wojciech Masarczyk +2Latent Visual Reasoning Diffusion Models
Warsaw University of Technology · IDEAS Research Institute · University of Amsterdam
Iris-3B: Going Beyond the Latent with Pixel-Space Diffusion Training, Conversion and Fine-Tuning
Oct 7, 2026Hanqiu Li Cai, Chema GarabitoT2I Diffusion Models Pixel-Space Diffusion Models
SperidLabs
Just Align $\bm{x}$ : Aligning Predictions, Not Representations
Sep 30, 2026Yuyao Zhang, Yuwei Hu, Ziyang Mai +1Diffusion Model Alignment Diffusion Transformer
Dartmouth College
PixelDense: Dense Prediction as Representation Alignment for Pixel Diffusion
Sep 30, 2026Lehan Yang, Daiqing Qi, Wenhao Zhang +9Diffusion Model Alignment Diffusion Transformer
University of Virginia · Michigan State University · Adobe +1
ExpandDiff: Dynamic Range Expanding Diffusion for Single-Image HDR Reconstruction
Sep 30, 2026Mehmet Emre andıran, Zhuoqian Yang, Liying Lu +2HDR Imaging Pixel-Space Diffusion Models
School of Computer and Communication Sciences, EPFL, Switzerland
Adversarial Training for Pixel Diffusion
Sep 29, 2026Xin Lin, Zhifei Zhang, Yuqian Zhou +4Adversarial Training Pixel-Space Diffusion Models
UC San Diego · Adobe Research · Work done during an internship at Adobe Research +1
DMA $^2$ : Pixel-space Distribution Matching with Adversarial and Anchor Losses
Sep 29, 2026Xin Lin, Zhifei Zhang, Yuqian Zhou +6Diffusion Model Distillation Pixel-Space Diffusion Models
UC San Diego · Adobe Research · University of Virginia +1
Persistence Forcing: Exploiting Feature Specialization in Pixel-Space Diffusion
Sep 28, 2026Chong Wang, Zixuan Fu, Shiqi Huang +3Diffusion Transformer Diffusion Guidance
Nanyang Technological University · KTH Royal Institute of Technology · Hebei University of Technology
CLIMB-flow: Coupled Linear Inverse posterior sampling via Multiscale-Based flow
Sep 27, 2026Zeqiu Yu, Ruizhi Yuan, Mathews JacobDiffusion Models for Image Restoration Diffusion Sampling
Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, USA · Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, USA
PixelDiT2: Representation-Grounded Pixel Diffusion Transformers
Sep 21, 2026Yongsheng Yu, Wei Xiong, Yichen Sheng +2Diffusion Model Guidance Diffusion Transformer
NVIDIA · University of Rochester
Beyond Random Couplings: Contrastive Noise Alignment in Generative Flows
Sep 16, 2026Lennart Wittke, Vinicius AzevedoFlow Matching Cross-Modal Alignment
ETH Zürich · Disney Research | Studios
Advanced Pixel Diffusion Model with Guided Sparse Global Refinement
Sep 1, 2026Weiyi You, Jinhua Zhang, Xingyu Zhou +3Pixel-Space Diffusion Models Diffusion-Based Image Generation
University of Electronic Science and Technology of China
Efficient and High-Quality Depth Estimation via Pixel-Space Diffusion with Linear Attention
Aug 31, 2026Bingde Liu, Wu Ran, Jinglei Zhang +2Monocular Depth Estimation Pixel-Space Diffusion Models
MoE Key Lab of Artificial Intelligence, Institute of AI, Shanghai Jiao Tong University, Shanghai, China · Zhiyuan College, Shanghai Jiao Tong University, Shanghai, China
MOSAIK: Multi-Patch Content-Aware Spatial Allocation of Image Tokens for Efficient Generation
Aug 5, 2026Mohammadreza Hami, Mohammadreza Samadi, Chao Gao +1Pixel-Space Diffusion Models Diffusion Model Inference Acceleration
Huawei Technologies Canada
A Frozen Pixel-Space Diffusion Model Can Guide Itself with Its Own Samples
Jul 31, 2026Zixuan Fu, Chong Wang, Lanqing Guo +3Diffusion Model Guidance Diffusion Model Sampling
Nanyang Technological University
Generative Video Compression with Adaptive Score Distillation
Jul 24, 2026Naifu Xue, Zhaoyang Jia, Haosen Li +7Video Diffusion Models Diffusion Model Distillation
Communication University of China, Beijing, China · Microsoft Research Asia · University of Science and Technology of China, Hefei, China +1
Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines
Jul 21, 2026Guillaume Payeur, Laurence Perreault-Levasseur, Gabriel Missael Barco +1Pixel-Space Diffusion Models Diffusion Posterior Sampling
Department of Physics, Universit´e de Montr´eal, Montreal QC, Canada · 2Mila - Quebec AI Institute, Montreal QC, Canada · 3Ciela Institute, Institute for Astrophysics and Machine Learning, Montreal QC, Canada +3
DuSPiT: Dual-Branch Sub-Patch Pixel Diffusion Transformer
Jul 20, 2026Yunpeng Bai, Yossi Gandelsman, Michaël GharbiDiffusion Transformer Pixel-Space Diffusion Models
The University of Texas at Austin · Reve
Pixel-Space Diffusion Transformers
Jul 20, 2026Renye Yan, Jikang Cheng, You Wu +8Diffusion Transformer Pixel-Space Diffusion Models
Peking University, China · Nanjing University, China · Stanford University, USA +3
PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space
Jul 6, 2026Sensen Gao, Zhaoqing Wang, Qihang Cao +33D Reconstruction Pixel-Space Diffusion Models
Nanyang Technological University · AISphere
PointDiT: Pixel-Space Diffusion for Monocular Geometry Estimation
Jul 2, 2026Haofei Xu, Rundi Wu, Philipp Henzler +7Diffusion Transformer Monocular 3D Reconstruction
Google · ETH Zurich · University of Tübingen, Tübingen AI Center +3
PixGS: Pixel-Space Diffusion for Direct 3D Gaussian Splat Generation
Jul 2, 2026Cao Duy, Phong Nguyen-Ha3D Gaussian Splatting 3D Asset Generation
Qualcomm AI Research⋆
PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion
Jun 26, 2026Zipeng Guo, Lichen Ma, Yu He +4Diffusion Transformer Pixel-Space Diffusion Models
Xi’an Jiaotong University
Show the Signal, Hide the Noise: Spectral Forcing for Pixel-Space Diffusion
Jun 13, 2026Weichen Fan, Haiwen Diao, Penghao Wu +1Pixel-Space Diffusion Models Diffusion-Based Image Generation
1S-Lab, Nanyang Technological University
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
FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion
May 18, 2026Lichen Ma, Zipeng Guo, Yu He +5Pixel-Space Diffusion Models Diffusion-Based Image Generation
Registers Matter for Pixel-Space Diffusion Transformers
May 15, 2026Nikita Starodubcev, Ilia Sudakov, Ilya Drobyshevskiy +2Diffusion Transformer Diffusion Guidance
Yandex Research
HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion
May 15, 2026Yu He, Lichen Ma, Zipeng Guo +5Diffusion Transformer Image Generation

Oct 8, 2026, cs.CVJ/K move · Enter open · S save

BudgetPix: Compute-Adaptive Tokenization for Pixel-Space Image Diffusion

Ozgur Kara, Yujia Chen, Daniel Watson, David Forsyth+3

University of Illinois Urbana-Champaign · Google

Image Tokenization Diffusion Model Inference Acceleration Pixel-Space Diffusion Models T2I Generation

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Most image generation models rely on uniform tokenization, allocating the exact same computational budget to equally-sized image patches. This static paradigm cannot adapt to different resource constraints at inference time, and yields suboptimal quality-cost tradeoff by devoting the same effort to both plain backgrounds and intricate details. We propose BudgetPix, an adaptive tokenization framework that dynamically allocates compute based on visual complexity and spatial layout, enabling flexible computational budgeting at inference time. BudgetPix comprises three key components: (1) an adaptive encoder that maps a fixed-size image to a variable-length token sequence using an entropy-guided quadtree alongside a multi-scale patch embedder; (2) a scale-aware decoder reconstructs fixed-resolution images from multi-scale token sets; and (3) a flexible training and sampling schedule that enables pixel-space denoisers to operate across variable token counts. BudgetPix seamlessly integrates with existing pixel-space diffusion architectures, enabling a single checkpoint to be operated at a wide range of compute budgets. Evaluated on text-to-image generation, BudgetPix matches the fidelity of MiniT2I-L at

512^2

and PixelDiT at

1024^2

using just 25% of the original compute budget. In class-conditional generation using a MeanFlow backbone, BudgetPix requires merely 60% of the full compute budget to produce images with near-zero quality degradation, observing a marginal 0.8-point increase in FID. Comprehensive assessments by human and VLM judges confirm that BudgetPix establishes a significantly improved quality-efficiency tradeoff over prior budget-adaptive baselines. More details are available at our project page: https://karaozgur.com/BudgetPix

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