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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Covariance-aware sampling for Diffusion Models
May 13, 2026Andrea Schioppa, Tim SalimansDiffusion Model Sampling Diffusion Sampling
GDM - Amsterdam · work done at GDM - Amsterdam
Asymmetric Flow Models
May 13, 2026Hansheng Chen, Jan Ackermann, Minseo Kim +2Flow Matching T2I Generation
Stanford University
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
HiDream-O1-Image: A Natively Unified Image Generative Foundation Model with Pixel-level Unified Transformer
May 11, 2026Qi Cai, Jingwen Chen, Chengmin Gao +22Diffusion Transformer Text-Guided Image Editing
FREPix: Frequency-Heterogeneous Flow Matching for Pixel-Space Image Generation
May 7, 2026Mingfeng Lin, Jiakun Chen, Liang Han +1Flow Matching Conditional Image Generation
Harbin Institute of Technology (Shenzhen)
Grokking of Diffusion Models: Case Study on Modular Addition
Apr 20, 2026Joon Hyeok Kim, Yong-Hyun Park, Mattis Dalsætra Østby +1Pixel-Space Diffusion Models Mechanistic Interpretability
Department of Computer and Information Science University of Pennsylvania Philadelphia, PA 19104, USA
V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising
Mar 17, 2026Han Lin, Xichen Pan, Zun Wang +4Diffusion Model Alignment Pixel-Space Diffusion Models
1UNC Chapel Hill · 2NYU · 3Meta +1
WiT: Waypoint Diffusion Transformers for Alleviating Trajectory Conflict in Pixel-Space Image Generation
Mar 16, 2026Hainuo Wang, Mingjia Li, Xiaojie GuoFlow Matching Diffusion Transformer
College of Intelligence and Computing, Tianjin University, Tianjin 300350, China
SSDD: Single-Step Diffusion Decoder for Efficient Image Tokenization
Oct 6, 2025Théophane Vallaeys, Jakob Verbeek, Matthieu CordImage Tokenization Image Reconstruction
Meta Fundamental AI Research · Sorbonne University

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Covariance-aware sampling for Diffusion Models

Andrea Schioppa, Tim Salimans

GDM - Amsterdam · work done at GDM - Amsterdam

Diffusion Model Sampling Diffusion Sampling Pixel-Space Diffusion Models Few-Step Diffusion Sampling Covariance Estimation

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We present a covariance-aware sampler that improves the quality of pixel-space Diffusion Model (DM) sampling in the few-step regime. We hypothesize that in the few-step regime samplers fail because they rely solely on the predicted mean of the reverse distribution, while our solution explicitly models the reverse-process covariance. Our method combines Tweedie's formula to estimate the covariance with an efficient, structured Fourier-space decomposition of the covariance matrix. Implemented as an extension of DDIM, our method requires only a minimal overhead: one extra Jacobian-Vector Product (JVP) per step. We demonstrate that for pixel-based DMs, our method consistently produces superior samples compared to state-of-the-art second order samplers (Heun, DPM-Solver++) and the recent aDDIM sampler, at an identical number of function evaluations (NFE).

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Latest papers 39