{"total":79,"rows":[{"id":"2209.00796","title":"Diffusion Models: A Comprehensive Survey of Methods and Applications","created":"2022-09-02","cited_by_count":1114,"area":"diffusion"},{"id":"2209.12152","title":"All are Worth Words: A ViT Backbone for Diffusion Models","created":"2022-09-25","cited_by_count":502,"area":"diffusion"},{"id":"2310.02279","title":"Consistency Trajectory Models: Learning Probability Flow ODE Trajectory of Diffusion","created":"2023-10-01","cited_by_count":459,"area":"diffusion"},{"id":"2208.05314","title":"Convergence of denoising diffusion models under the manifold hypothesis","created":"2022-08-10","cited_by_count":242,"area":"diffusion"},{"id":"2302.07194","title":"Score Approximation, Estimation and Distribution Recovery of Diffusion Models on Low-Dimensional Data","created":"2023-02-14","cited_by_count":174,"area":"diffusion"},{"id":"2307.00619","title":"Solving Linear Inverse Problems Provably via Posterior Sampling with Latent Diffusion Models","created":"2023-07-02","cited_by_count":157,"area":"diffusion"},{"id":"2304.12526","title":"Patch Diffusion: Faster and More Data-Efficient Training of Diffusion Models","created":"2023-04-25","cited_by_count":133,"area":"diffusion"},{"id":"2303.05754","title":"Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems","created":"2023-03-10","cited_by_count":132,"area":"diffusion"},{"id":"2206.01018","title":"Score-Based Generative Models Detect Manifolds","created":"2022-06-02","cited_by_count":122,"area":"diffusion"},{"id":"2305.13122","title":"Policy Representation via Diffusion Probability Model for Reinforcement Learning","created":"2023-05-22","cited_by_count":112,"area":"diffusion"},{"id":"2209.05442","title":"Soft Diffusion: Score Matching for General Corruptions","created":"2022-09-12","cited_by_count":102,"area":"diffusion"},{"id":"2211.17091","title":"Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models","created":"2022-11-28","cited_by_count":100,"area":"diffusion"},{"id":"2304.04740","title":"Reflected Diffusion Models","created":"2023-04-10","cited_by_count":87,"area":"diffusion"},{"id":"2302.07400","title":"Score-based Diffusion Models in Function Space","created":"2023-02-14","cited_by_count":82,"area":"diffusion"},{"id":"2208.13753","title":"Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis","created":"2022-08-29","cited_by_count":75,"area":"diffusion"},{"id":"2311.18257","title":"Diffusion Models Without Attention","created":"2023-11-30","cited_by_count":75,"area":"diffusion"},{"id":"2303.03384","title":"Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers","created":"2023-03-06","cited_by_count":74,"area":"diffusion"},{"id":"2402.17376","title":"Accelerating Diffusion Sampling with Optimized Time Steps","created":"2024-02-27","cited_by_count":67,"area":"diffusion"},{"id":"2308.07037","title":"Bayesian Flow Networks","created":"2023-08-14","cited_by_count":63,"area":"diffusion"},{"id":"2306.04542","title":"On the Design Fundamentals of Diffusion Models: A Survey","created":"2023-06-07","cited_by_count":60,"area":"diffusion"},{"id":"2302.09057","title":"Consistent Diffusion Models: Mitigating Sampling Drift by Learning to be Consistent","created":"2023-02-17","cited_by_count":58,"area":"diffusion"},{"id":"2305.03935","title":"Improved Techniques for Maximum Likelihood Estimation for Diffusion ODEs","created":"2023-05-06","cited_by_count":54,"area":"diffusion"},{"id":"2404.02954","title":"Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections","created":"2024-04-03","cited_by_count":47,"area":"diffusion"},{"id":"2306.08860","title":"OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models","created":"2023-06-15","cited_by_count":45,"area":"diffusion"},{"id":"2308.01316","title":"Patched Denoising Diffusion Models For High-Resolution Image Synthesis","created":"2023-08-02","cited_by_count":45,"area":"diffusion"},{"id":"2402.05608","title":"Scalable Diffusion Models with State Space Backbone","created":"2024-02-08","cited_by_count":45,"area":"diffusion"},{"id":"2205.13699","title":"Maximum Likelihood Training of Implicit Nonlinear Diffusion Models","created":"2022-05-27","cited_by_count":42,"area":"diffusion"},{"id":"2404.04478","title":"Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models","created":"2024-04-06","cited_by_count":41,"area":"diffusion"},{"id":"2303.00800","title":"Continuous-Time Functional Diffusion Processes","created":"2023-03-01","cited_by_count":39,"area":"diffusion"},{"id":"2305.19147","title":"Conditional score-based diffusion models for Bayesian inference in infinite dimensions","created":"2023-05-28","cited_by_count":38,"area":"diffusion"},{"id":"2302.01217","title":"A Theoretical Justification for Image Inpainting using Denoising Diffusion Probabilistic Models","created":"2023-02-02","cited_by_count":37,"area":"diffusion"},{"id":"2505.16792","title":"REPA Works Until It Doesn't: Early-Stopped, Holistic Alignment Supercharges Diffusion Training","created":"2025-05-22","cited_by_count":37,"area":"diffusion"},{"id":"2406.18459","title":"DiffuseHigh: Training-free Progressive High-Resolution Image Synthesis through Structure Guidance","created":"2024-06-26","cited_by_count":34,"area":"diffusion"},{"id":"2510.01329","title":"Continuously Augmented Discrete Diffusion model for Categorical Generative Modeling","created":"2025-10-01","cited_by_count":34,"area":"diffusion"},{"id":"2403.10348","title":"Denoising Task Difficulty-based Curriculum for Training Diffusion Models","created":"2024-03-15","cited_by_count":27,"area":"diffusion"},{"id":"2504.10612","title":"Energy Matching: Unifying Flow Matching and Energy-Based Models for Generative Modeling","created":"2025-04-14","cited_by_count":27,"area":"diffusion"},{"id":"2310.13545","title":"ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection","created":"2023-10-20","cited_by_count":25,"area":"diffusion"},{"id":"2403.01189","title":"Training Unbiased Diffusion Models From Biased Dataset","created":"2024-03-02","cited_by_count":25,"area":"diffusion"},{"id":"2506.17139","title":"Consistent Sampling and Simulation: Molecular Dynamics with Energy-Based Diffusion Models","created":"2025-06-20","cited_by_count":23,"area":"diffusion"},{"id":"2306.11251","title":"Lipschitz Singularities in Diffusion Models","created":"2023-06-20","cited_by_count":21,"area":"diffusion"},{"id":"2407.10641","title":"Deep Diffusion Image Prior for Efficient OOD Adaptation in 3D Inverse Problems","created":"2024-07-15","cited_by_count":21,"area":"diffusion"},{"id":"2502.08011","title":"Training-Free Safe Denoisers for Safe Use of Diffusion Models","created":"2025-02-11","cited_by_count":21,"area":"diffusion"},{"id":"2407.01014","title":"An Expectation-Maximization Algorithm for Training Clean Diffusion Models from Corrupted Observations","created":"2024-07-01","cited_by_count":20,"area":"diffusion"},{"id":"2505.18495","title":"Beyond Masked and Unmasked: Discrete Diffusion Models via Partial Masking","created":"2025-05-24","cited_by_count":20,"area":"diffusion"},{"id":"2309.05153","title":"Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood","created":"2023-09-10","cited_by_count":19,"area":"diffusion"},{"id":"2407.11555","title":"Self-Guided Generation of Minority Samples Using Diffusion Models","created":"2024-07-16","cited_by_count":19,"area":"diffusion"},{"id":"2302.10907","title":"Diffusion Models in Bioinformatics: A New Wave of Deep Learning Revolution in Action","created":"2023-02-13","cited_by_count":15,"area":"diffusion"},{"id":"2309.17074","title":"AdaDiff: Accelerating Diffusion Models through Step-Wise Adaptive Computation","created":"2023-09-29","cited_by_count":14,"area":"diffusion"},{"id":"2502.01441","title":"Improved Training Technique for Latent Consistency Models","created":"2025-02-03","cited_by_count":13,"area":"diffusion"},{"id":"2402.17563","title":"Structure-Guided Adversarial Training of Diffusion Models","created":"2024-02-27","cited_by_count":11,"area":"diffusion"},{"id":"2403.16379","title":"FlashEval: Towards Fast and Accurate Evaluation of Text-to-image Diffusion Generative Models","created":"2024-03-25","cited_by_count":11,"area":"diffusion"},{"id":"2405.17880","title":"Diffusion Rejection Sampling","created":"2024-05-28","cited_by_count":11,"area":"diffusion"},{"id":"2212.13771","title":"Exploring Vision Transformers as Diffusion Learners","created":"2022-12-28","cited_by_count":10,"area":"diffusion"},{"id":"2504.09454","title":"D$^2$iT: Dynamic Diffusion Transformer for Accurate Image Generation","created":"2025-04-13","cited_by_count":9,"area":"diffusion"},{"id":"2306.11363","title":"Masked Diffusion Models Are Fast Distribution Learners","created":"2023-06-20","cited_by_count":7,"area":"diffusion"},{"id":"2405.15676","title":"Taming Score-Based Diffusion Priors for Infinite-Dimensional Nonlinear Inverse Problems","created":"2024-05-24","cited_by_count":7,"area":"diffusion"},{"id":"2409.03817","title":"Neural Entropy","created":"2024-09-05","cited_by_count":7,"area":"diffusion"},{"id":"2506.20641","title":"Telegrapher's Generative Model via Kac Flows","created":"2025-06-25","cited_by_count":7,"area":"diffusion"},{"id":"2603.20645","title":"Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity","created":"2026-03-21","cited_by_count":7,"area":"diffusion"},{"id":"2303.05456","title":"Restoration based Generative Models","created":"2023-02-20","cited_by_count":6,"area":"diffusion"},{"id":"2308.11941","title":"Boosting Diffusion Models with an Adaptive Momentum Sampler","created":"2023-08-23","cited_by_count":6,"area":"diffusion"},{"id":"2405.16104","title":"Global Well-posedness and Convergence Analysis of Score-based Generative Models via Sharp Lipschitz Estimates","created":"2024-05-25","cited_by_count":6,"area":"diffusion"},{"id":"2501.00384","title":"S-Diff: An Anisotropic Diffusion Model for Collaborative Filtering in Spectral Domain","created":"2024-12-31","cited_by_count":6,"area":"diffusion"},{"id":"2504.11457","title":"Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception","created":"2025-04-15","cited_by_count":4,"area":"diffusion"},{"id":"2506.10177","title":"Geometric Regularity in Deterministic Sampling Dynamics of Diffusion-based Generative Models","created":"2025-06-11","cited_by_count":4,"area":"diffusion"},{"id":"2602.16601","title":"Quantifying Error Propagation and Model Collapse in Diffusion Models","created":"2026-02-18","cited_by_count":4,"area":"diffusion"},{"id":"2508.08735","title":"Elucidating Rectified Flow with Deterministic Sampler: Polynomial Discretization Complexity for Multi and One-step Models","created":"2025-08-12","cited_by_count":3,"area":"diffusion"},{"id":"2311.11164","title":"Mitigating Exposure Bias in Discriminator Guided Diffusion Models","created":"2023-11-18","cited_by_count":2,"area":"diffusion"},{"id":"2404.09016","title":"Theoretical research on generative diffusion models: an overview","created":"2024-04-13","cited_by_count":2,"area":"diffusion"},{"id":"2501.06999","title":"Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps","created":"2025-01-13","cited_by_count":2,"area":"diffusion"},{"id":"2601.19368","title":"Divergence-Free Diffusion Models for Incompressible Fluid Flows","created":"2026-01-27","cited_by_count":2,"area":"diffusion"},{"id":"2605.21489","title":"Variance Reduction for Expectations with Diffusion Teachers","created":"2026-05-20","cited_by_count":2,"area":"diffusion"},{"id":"2312.06285","title":"Compensation Sampling for Improved Convergence in Diffusion Models","created":"2023-12-11","cited_by_count":1,"area":"diffusion"},{"id":"2411.06119","title":"Hardware-Friendly Diffusion Models with Fixed-Size Reusable Structures for On-Device Image Generation","created":"2024-11-09","cited_by_count":0,"area":"diffusion"},{"id":"2507.15216","title":"Improving Joint Embedding Predictive Architecture with Diffusion Noise","created":"2025-07-21","cited_by_count":0,"area":"diffusion"},{"id":"2511.19434","title":"Breaking the Likelihood-Quality Trade-off in Diffusion Models by Merging Pretrained Experts","created":"2025-11-24","cited_by_count":0,"area":"diffusion"},{"id":"2602.02928","title":"Distance Marching for Generative Modeling","created":"2026-02-03","cited_by_count":0,"area":"diffusion"},{"id":"2606.13796","title":"Recursively Trained Diffusion Models: Limiting Collapse Distribution and Spectral Characterization","created":"2026-06-11","cited_by_count":0,"area":"diffusion"},{"id":"2610.14409","title":"PotentialEP: Backprop-Free Training of CIFAR-Scale Generative Models via Equilibrium Propagation","created":"2026-10-10","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}