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• Importance-Aware OBS Pruning for Diffusion Models

  Ba-Thinh Lam, Srijan Das, Hieu Le · 23. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  We propose importance-aware pruning for diffusion models, a training-free framework that prioritizes preserving parameters critical to semantically salient image regions. To do so, we incorporate spatial importance maps -- derived from conditioning signals or model attention -- into the pruning obje…

• OSVE: One Step Video Editing with One Step Diffusion Models

  Habin Lim, Gyeong-Moon Park · 23. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Text-guided video editing with diffusion models is impractically slow, hindered by costly multi-step sampling and inversion. We present OSVE, the first framework to successfully adapt one-step Text-to-Image (T2I) models for high-quality video editing, addressing the core challenges of inversion, edi…

• Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines

  Guillaume Payeur, Laurence Perreault-Levasseur, Gabriel Missael Barco, Yashar Hezaveh · 23. Juli 2026 · Galaxies: Formation, Evolution, Phenomena

  Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground galaxy is computationally challenging, particularly for high-resolution, high signal-to-noise ratio observations. In this regime, high-dimensional representatio…

• DiFA: Inference-Time Forward-Process Alignment for Diffusion Models

  Shigui Li, Delu Zeng · 21. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  The prevailing inference framework for diffusion models formulates generation fundamentally as a problem of numerical integration. This perspective casts the model as an exact estimator, neglecting the inherent statistical uncertainty of the denoising process. In this work, we propose Forward-Proces…

• AGG: Jacobian-Aggregated Group Gradient for Efficient GRPO Training of Diffusion Models

  Ruiyi Ding, Jie Li, He Kang, Ziyan Liu, Chengru Song, Yuan chen · 21. Juli 2026 · Reinforcement Learning in Robotics

  Group Relative Policy Optimization (GRPO) is a powerful reinforcement learning algorithm for aligning generative models with human preferences. While successful in large language models~\cite{shao2024deepseekmathpushinglimitsmathematical}, its extension to diffusion and flow matching models introduc…

• Diffusion models recover accurate mixture weights despite score function insensitivity

  Andrew Dennehy, Ramchandran Muthukumar, Rebecca Willett, Nisha Chandramoorthy · 20. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Score-based generative models exhibit a puzzling behavior: they often appear to cover all modes of a target multimodal distribution and yet may fail to learn the correct relative mode amplitudes, which can be interpreted as mixture weights. We resolve this apparent paradox by relating the diffusion …

• Toward a mechanistic understanding of inference in visual cortex and diffusion models

  Zeyu Yun, Alexander Belsten, Dasheng Bi, Zahra Kadkhodaie, Yubei Chen, Bruno A. Olshausen · 20. Juli 2026 · Visual perception and processing mechanisms

  We describe a model of perceptual inference in primary visual cortex (V1) equivalent to a minimal diffusion model whose function can be readily understood from its parameters. The model is based on sparse coding with a non-factorial prior over latent variables in the form of an unconstrained, pairwi…

• Rare Concept Generation via Counterfactual Inference in Diffusion Models

  Zhengyuan Jiang, Haipeng Liu, Meng Wang, Yang Wang · 17. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Rare concept generation focuses on synthesizing customized images conditioned on text prompts that describe objects with unusual attributes. Previous works failed to align the generated images with rare concepts, resulting in incorrect attribute rendering or inconsistent composition of concepts. Suc…

• A Continuous-Time Reinforcement Learning Framework for Fine-Tuning Discrete Diffusion Models

  Zikun Zhang, Jiayuan Sheng, David D. Yao, Wenpin Tang · 17. Juli 2026 · Reinforcement Learning in Robotics

  We formulate reinforcement learning (RL) in continuous time with discrete state spaces and possibly arbitrary action spaces via a stochastic control approach, where the state dynamics are modeled as a controlled continuous-time Markov chain (CTMC). We consider policy optimization problems and derive…

• Integration Matters: Rollout-Based Training for Constrained Diffusion Models

  Xiaoxuan Liang, Saeid Naderiparizi, Berend Zwartsenberg, Frank Wood · 17. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Constrained generative models aim to produce samples that satisfy complex feasibility constraints while remaining faithful to the data distribution. Existing constrained generation methods typically enforce constraints either through training-time optimization or sampling-time correction. Training-t…

• Cyclone: Diffusion Model for Cycle-Consistent Weather Editing from Unpaired Driving Data

  Thang-Anh-Quan Nguyen, Moussab Bennehar, Luis Guillermo Roldao Jimenez, Nathan Piasco, Dzmitry Tsishkou, Laurent Caraffa, Jean-Philippe Tarel, Roland Brémond · 16. Juli 2026 · Image Enhancement Techniques

  Reliable perception under diverse weather conditions remains a major challenge for autonomous driving systems. A common strategy to improve robustness is either to synthesize adverse weather conditions for training perception models or to apply weather-removal techniques to recover clean inputs. How…

• TCAM-Diff: Triplane-Aware Cross-Attention Medical Diffusion Model

  Zhenkai Zhang, Krista A. Ehinger, Tom Drummond · 16. Juli 2026 · Advanced Neuroimaging Techniques and Applications

  We introduce TCAM-Diff, a novel 3D medical image generation model that reduces the memory requirements to encode and generate high-resolution 3D data. This model utilizes a decoder-only autoencoder method to learn triplane representation from dense volume and leverages generalization operations to p…

• PersGuard: Preventing Malicious Personalization in Text-to-Image Diffusion Models via Model Backdoors

  Xinwei Liu, Xiaojun Jia, Yuan Xun, Hua Zhang, Xiaochun Cao · 16. Juli 2026 · Machine Learning in Healthcare

  Diffusion models (DMs) have advanced text-to-image (T2I) synthesis, yet their personalization capabilities raise serious privacy and copyright concerns. Malicious actors can misuse these models to generate unauthorized portraits or artistic style replicas. Existing proactive defenses primarily rely …

• Discrete Diffusion Models: A Unified Framework from Tokenization to Generation

  Ye Yuan, Weien Li, Rui Song, Zeyu Li, Haochen Liu, Xiangyu Kong, Zixuan Dong, Linfeng Du, Zipeng Sun, Weixu Zhang, Jiaxin Huang, Changjiang Han, Yonghan Yang, Zichen Zhao, Xiuyuan Hu, Haolun Wu, Yankai Chen, Fengran Mo, Jikun Kang, Bowei He, Philip S. Yu, Xue Liu · 16. Juli 2026 · Model Reduction and Neural Networks

  Discrete denoising diffusion models (DDMs) have recently emerged as a compelling alternative to autoregressive (AR) modeling for discrete data, offering parallel generation and iterative global refinement capabilities. Unlike continuous diffusion, where the state space is fixed, DDMs are fundamental…

• Beyond Perceptual Distance: Discrepancy Assessment on Deep Representation for Out-of-Distribution Detection with Diffusion Model

  Kun Fang, Zuopeng Yang, Haibo Hu, Xiaolin Huang, Jie Yang, Qinghua Tao · 15. Juli 2026 · Anomaly Detection Techniques and Applications

  Out-of-Distribution (OoD) detection aims to justify whether a given sample is from the training distribution of the classifier-under-protection, i.e., In-Distribution (InD), or from an unknown out distribution. Recent researches have leveraged Diffusion Models (DMs) for OoD detection due to their po…

• Steering Diffusion Models via Class-Contrastive Influence for Few-Shot Medical Classification

  Jeeyung Kim, Erfan Esmaeili, Qiang Qiu · 15. Juli 2026 · Domain Adaptation and Few-Shot Learning

  When labeled data are scarce, off-the-shelf diffusion models can augment training sets for few-shot medical image classification, but not all generated samples are equally useful for the downstream task. Existing approaches largely improve synthetic data by increasing realism, diversity, or domain a…

• FlashDiff: Efficient Regional Execution and Scheduling for Diffusion Model Serving

  Yaqi Qiao, Ping He, Songrun Xie, Ayush Barik, Chensong Zhang, Zhengzhong Tu, Fan Lai · 15. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models have become the central backbone for modern image, video, and audio generation, but their efficient service remains a challenge. Unlike autoregressive decoding, diffusion inference repeatedly updates high-dimensional spatial or temporal latents over many denoising steps. This all-re…

• TraceSynth: Generating Production-Quality Kernel Traces with Constraint-Guided Diffusion Models

  Yuvraj Sehgal, Sneh Patel, Mahsa Panahandeh, Naser Ezzati-Jivan, Francois Tetreault · 15. Juli 2026 · Software System Performance and Reliability

  Machine learning models for system diagnostics rely on kernel execution traces to capture fine-grained system behavior, but collecting production traces in industrial systems is costly due to runtime overhead, storage demands, and privacy constraints. We present TraceSynth, a diffusion-based framewo…

• GenDiff: A Dose and Anatomy Aware Diffusion Model with Structural Prior Refinement for Low-Dose CT Reconstruction and Generalization

  Md Imam Ahasan, Guangchao Yang, A F M Abdun Noor, Kah Ong Michael Goh, S. M. Hasan Mahmud, Md Mahfuzur Rahman · 15. Juli 2026 · Medical Imaging Techniques and Applications

  Computed tomography (CT) is a critical imaging modality for clinical diagnosis, but reducing radiation dose inevitably introduces severe noise and structured artifacts that degrade image quality. Existing deep learning-based low-dose CT (LDCT) reconstruction methods are typically optimized for fixed…

• The Seriality Gap in Video Diffusion Models

  Jorge Diaz Chao, Konpat Preechakul, Yuxi Liu, Yutong Bai · 15. Juli 2026 · Neural dynamics and brain function

  When one ball strikes another, then another, video models should predict the consequences of each bounce. In controlled experiments on multi-ball hard-sphere dynamics, we find that the performance of standard bidirectional video diffusion degrades as the causal chain lengthens, even when provided mo…

• Low-dimensional adaptation of diffusion models: Convergence in total variation

  Jiadong Liang, Zhihan Huang, Yuxin Chen · 14. Juli 2026 · Advanced Mathematical Modeling in Engineering

  This paper investigates how diffusion generative models leverage (unknown) low-dimensional structure to accelerate sampling. Focusing on two mainstream samplers -- the denoising diffusion implicit model (DDIM) and the denoising diffusion probabilistic model (DDPM), we prove that their iteration comp…

• From Global to Factor-Wise Expert Composition in Discrete Diffusion Models

  Haozhe Huang, Yudong Xu, Abhijoy Mandal, Al\'an Aspuru-Guzik · 14. Juli 2026 · Opinion Dynamics and Social Influence

  Discrete diffusion models offer a powerful framework for solving complex reasoning tasks, particularly through compositional generation, which combines multiple pre-trained experts to generalize beyond their individual training data. Recent theoretical corrections introduce time-dependent mixing wei…

• Conservation Laws for Diffusion Models

  Ziv Aharoni, Henry D. Pfister · 14. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  While autoregressive models optimize the exact data likelihood via the chain rule, diffusion models are typically trained with denoising objectives. We develop conservation laws based on generalized extrinsic information transfer (GEXIT) functions for a broad class of memoryless noise processes, sho…

• DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations

  Abdulkader Helwan, Lina Abou-Abbas, Hussein El Amouri, Belkacem Chikhaoui, Khadidja Henni · 14. Juli 2026 · EEG and Brain-Computer Interfaces

  Deep learning for EEG-based seizure detection faces critical challenges: severe annotation scarcity and extreme class imbalance, where ictal events comprise less than 10\% of clinical recordings. We present DiffEEG, a 9.6M-parameter self-supervised foundation model that addresses both limitations th…

• Unified Backbone Refinement for Diffusion Models via Internal-Latent Analysis

  Haksoo Lim, Myeongjin Lee, Wonjoon Chang, Jaesik Choi · 14. Juli 2026 · Generative Adversarial Networks and Image Synthesis

  Diffusion models have achieved remarkable success across diverse domains, with performance closely related to the denoising backbones that parameterize the score function. In this paper, we present a systematic, phase-aware analysis of diffusion components and show that abrupt, early-stage fluctuati…