{"total":263,"rows":[{"id":"2403.03206","title":"Scaling Rectified Flow Transformers for High-Resolution Image Synthesis","created":"2024-03-05","cited_by_count":4181,"area":"diffusion"},{"id":"2410.06940","title":"Representation Alignment for Generation: Training Diffusion Transformers Is Easier Than You Think","created":"2024-10-09","cited_by_count":703,"area":"diffusion"},{"id":"2412.07772","title":"From Slow Bidirectional to Fast Autoregressive Video Diffusion Models","created":"2024-12-10","cited_by_count":433,"area":"diffusion"},{"id":"2410.06885","title":"F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow Matching","created":"2024-10-09","cited_by_count":361,"area":"diffusion"},{"id":"2406.02507","title":"Guiding a Diffusion Model with a Bad Version of Itself","created":"2024-06-04","cited_by_count":299,"area":"diffusion"},{"id":"2510.11690","title":"Diffusion Transformers with Representation Autoencoders","created":"2025-10-13","cited_by_count":295,"area":"diffusion"},{"id":"2410.11081","title":"Simplifying, Stabilizing and Scaling Continuous-Time Consistency Models","created":"2024-10-14","cited_by_count":283,"area":"diffusion"},{"id":"2404.07724","title":"Applying Guidance in a Limited Interval Improves Sample and Distribution Quality in Diffusion Models","created":"2024-04-11","cited_by_count":254,"area":"diffusion"},{"id":"2410.10733","title":"Deep Compression Autoencoder for Efficient High-Resolution Diffusion Models","created":"2024-10-14","cited_by_count":217,"area":"diffusion"},{"id":"2502.06764","title":"History-Guided Video Diffusion","created":"2025-02-10","cited_by_count":171,"area":"diffusion"},{"id":"2410.10792","title":"Semantic Image Inversion and Editing using Rectified Stochastic Differential Equations","created":"2024-10-14","cited_by_count":165,"area":"diffusion"},{"id":"2407.01521","title":"Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing","created":"2024-07-01","cited_by_count":163,"area":"diffusion"},{"id":"2411.18673","title":"AC3D: Analyzing and Improving 3D Camera Control in Video Diffusion Transformers","created":"2024-11-27","cited_by_count":154,"area":"diffusion"},{"id":"2406.14548","title":"Consistency Models Made Easy","created":"2024-06-20","cited_by_count":144,"area":"diffusion"},{"id":"2401.11605","title":"Scalable High-Resolution Pixel-Space Image Synthesis with Hourglass Diffusion Transformers","created":"2024-01-21","cited_by_count":118,"area":"diffusion"},{"id":"2503.07565","title":"Inductive Moment Matching","created":"2025-03-10","cited_by_count":113,"area":"diffusion"},{"id":"2505.18825","title":"How to build a consistency model: Learning flow maps via self-distillation","created":"2025-05-24","cited_by_count":103,"area":"diffusion"},{"id":"2503.02819","title":"Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts","created":"2025-03-04","cited_by_count":96,"area":"diffusion"},{"id":"2503.09641","title":"SANA-Sprint: One-Step Diffusion with Continuous-Time Consistency Distillation","created":"2025-03-12","cited_by_count":87,"area":"diffusion"},{"id":"2506.14603","title":"Align Your Flow: Scaling Continuous-Time Flow Map Distillation","created":"2025-06-17","cited_by_count":87,"area":"diffusion"},{"id":"2510.08431","title":"Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency","created":"2025-10-09","cited_by_count":86,"area":"diffusion"},{"id":"2408.09000","title":"Classifier-Free Guidance is a Predictor-Corrector","created":"2024-08-16","cited_by_count":84,"area":"diffusion"},{"id":"2511.20645","title":"PixelDiT: Pixel Diffusion Transformers for Image Generation","created":"2025-11-25","cited_by_count":83,"area":"diffusion"},{"id":"2507.23268","title":"PixNerd: Pixel Neural Field Diffusion","created":"2025-07-31","cited_by_count":82,"area":"diffusion"},{"id":"2502.14831","title":"Improving the Diffusability of Autoencoders","created":"2025-02-20","cited_by_count":80,"area":"diffusion"},{"id":"2405.05967","title":"Distilling Diffusion Models into Conditional GANs","created":"2024-05-09","cited_by_count":78,"area":"diffusion"},{"id":"2410.19324","title":"Simpler Diffusion (SiD2): 1.5 FID on ImageNet512 with pixel-space diffusion","created":"2024-10-25","cited_by_count":75,"area":"diffusion"},{"id":"2511.19365","title":"DeCo: Frequency-Decoupled Pixel Diffusion for End-to-End Image Generation","created":"2025-11-24","cited_by_count":60,"area":"diffusion"},{"id":"2505.02831","title":"No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselves","created":"2025-05-05","cited_by_count":56,"area":"diffusion"},{"id":"2407.02687","title":"No Training, No Problem: Rethinking Classifier-Free Guidance for Diffusion Models","created":"2024-07-02","cited_by_count":53,"area":"diffusion"},{"id":"2410.14919","title":"Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step","created":"2024-10-19","cited_by_count":47,"area":"diffusion"},{"id":"2508.00413","title":"DC-AE 1.5: Accelerating Diffusion Model Convergence with Structured Latent Space","created":"2025-08-01","cited_by_count":47,"area":"diffusion"},{"id":"2602.12233","title":"Categorical Flow Maps","created":"2026-02-12","cited_by_count":44,"area":"diffusion"},{"id":"2510.20771","title":"AlphaFlow: Understanding and Improving MeanFlow Models","created":"2025-10-23","cited_by_count":41,"area":"diffusion"},{"id":"2407.03297","title":"Improved Noise Schedule for Diffusion Training","created":"2024-07-03","cited_by_count":39,"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":"2503.11056","title":"Flow to the Mode: Mode-Seeking Diffusion Autoencoders for State-of-the-Art Image Tokenization","created":"2025-03-14","cited_by_count":36,"area":"diffusion"},{"id":"2509.25162","title":"AlignTok: Aligning Visual Foundation Encoders to Tokenizers for Diffusion Models","created":"2025-09-29","cited_by_count":36,"area":"diffusion"},{"id":"2408.16333","title":"Self-Improving Diffusion Models with Synthetic Data","created":"2024-08-29","cited_by_count":35,"area":"diffusion"},{"id":"2512.10858","title":"Scaling Behavior of Discrete Diffusion Language Models","created":"2025-12-11","cited_by_count":35,"area":"diffusion"},{"id":"2410.07815","title":"Simple ReFlow: Improved Techniques for Fast Flow Models","created":"2024-10-10","cited_by_count":34,"area":"diffusion"},{"id":"2412.15032","title":"DCTdiff: Intriguing Properties of Image Generative Modeling in the DCT Space","created":"2024-12-19","cited_by_count":34,"area":"diffusion"},{"id":"2509.04394","title":"Transition Models: Rethinking the Generative Learning Objective","created":"2025-09-04","cited_by_count":34,"area":"diffusion"},{"id":"2402.04930","title":"Blue noise for diffusion models","created":"2024-02-07","cited_by_count":29,"area":"diffusion"},{"id":"2503.18512","title":"Uncertainty-guided Perturbation for Image Super-Resolution Diffusion Model","created":"2025-03-24","cited_by_count":29,"area":"diffusion"},{"id":"2602.05993","title":"Diamond Maps: Efficient Reward Alignment via Stochastic Flow Maps","created":"2026-02-05","cited_by_count":29,"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":"2405.14822","title":"PaGoDA: Progressive Growing of a One-Step Generator from a Low-Resolution Diffusion Teacher","created":"2024-05-23","cited_by_count":27,"area":"diffusion"},{"id":"2406.12839","title":"Evaluating the design space of diffusion-based generative models","created":"2024-06-18","cited_by_count":26,"area":"diffusion"},{"id":"2501.09756","title":"SynthLight: Portrait Relighting with Diffusion Model by Learning to Re-render Synthetic Faces","created":"2025-01-16","cited_by_count":24,"area":"diffusion"},{"id":"2412.05827","title":"Self-Guidance: Boosting Flow and Diffusion Generation on Their Own","created":"2024-12-08","cited_by_count":23,"area":"diffusion"},{"id":"2405.14477","title":"LiteVAE: Lightweight and Efficient Variational Autoencoders for Latent Diffusion Models","created":"2024-05-23","cited_by_count":22,"area":"diffusion"},{"id":"2410.06025","title":"Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency","created":"2024-10-08","cited_by_count":22,"area":"diffusion"},{"id":"2410.14895","title":"Truncated Consistency Models","created":"2024-10-18","cited_by_count":22,"area":"diffusion"},{"id":"2605.05204","title":"D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Models","created":"2026-05-06","cited_by_count":22,"area":"diffusion"},{"id":"2605.11125","title":"Language Modeling with Hyperspherical Flows","created":"2026-05-11","cited_by_count":22,"area":"diffusion"},{"id":"2401.00110","title":"Diffusion Model with Perceptual Loss","created":"2023-12-30","cited_by_count":21,"area":"diffusion"},{"id":"2411.01293","title":"Diffusion Models as Cartoonists: The Curious Case of High Density Regions","created":"2024-11-02","cited_by_count":21,"area":"diffusion"},{"id":"2506.07903","title":"Diffuse Everything: Multimodal Diffusion Models on Arbitrary State Spaces","created":"2025-06-09","cited_by_count":21,"area":"diffusion"},{"id":"2409.18336","title":"DeBaRA: Denoising-Based 3D Room Arrangement Generation","created":"2024-09-26","cited_by_count":20,"area":"diffusion"},{"id":"2511.19797","title":"Terminal Velocity Matching","created":"2025-11-24","cited_by_count":20,"area":"diffusion"},{"id":"2507.02608","title":"Lost in Latent Space: An Empirical Study of Latent Diffusion Models for Physics Emulation","created":"2025-07-03","cited_by_count":19,"area":"diffusion"},{"id":"2403.18035","title":"Bidirectional Consistency Models","created":"2024-03-26","cited_by_count":18,"area":"diffusion"},{"id":"2409.00587","title":"FLUX that Plays Music","created":"2024-09-01","cited_by_count":18,"area":"diffusion"},{"id":"2410.18737","title":"Rectified Diffusion Guidance for Conditional Generation","created":"2024-10-24","cited_by_count":18,"area":"diffusion"},{"id":"2503.01161","title":"Split Gibbs Discrete Diffusion Posterior Sampling","created":"2025-03-03","cited_by_count":18,"area":"diffusion"},{"id":"2506.10038","title":"Ambient Diffusion Omni: Training Good Models with Bad Data","created":"2025-06-10","cited_by_count":18,"area":"diffusion"},{"id":"2509.24526","title":"CMT: Mid-Training for Efficient Learning of Consistency, Mean Flow, and Flow Map Models","created":"2025-09-29","cited_by_count":18,"area":"diffusion"},{"id":"2510.01378","title":"Selective Underfitting in Diffusion Models","created":"2025-10-01","cited_by_count":18,"area":"diffusion"},{"id":"2312.10431","title":"Continuous Diffusion for Mixed-Type Tabular Data","created":"2023-12-16","cited_by_count":17,"area":"diffusion"},{"id":"2410.05167","title":"Presto! Distilling Steps and Layers for Accelerating Music Generation","created":"2024-10-07","cited_by_count":16,"area":"diffusion"},{"id":"2411.07765","title":"Novel View Synthesis with Pixel-Space Diffusion Models","created":"2024-11-12","cited_by_count":16,"area":"diffusion"},{"id":"2504.13612","title":"Entropic Time Schedulers for Generative Diffusion Models","created":"2025-04-18","cited_by_count":16,"area":"diffusion"},{"id":"2505.06166","title":"DiffLocks: Generating 3D Hair from a Single Image using Diffusion Models","created":"2025-05-09","cited_by_count":16,"area":"diffusion"},{"id":"2506.06085","title":"Feedback Guidance of Diffusion Models","created":"2025-06-06","cited_by_count":16,"area":"diffusion"},{"id":"2512.24176","title":"Guiding a Diffusion Transformer with the Internal Dynamics of Itself","created":"2025-12-30","cited_by_count":16,"area":"diffusion"},{"id":"2502.18197","title":"VCT: Training Consistency Models with Variational Noise Coupling","created":"2025-02-25","cited_by_count":15,"area":"diffusion"},{"id":"2406.12303","title":"Immiscible Diffusion: Accelerating Diffusion Training with Noise Assignment","created":"2024-06-18","cited_by_count":14,"area":"diffusion"},{"id":"2501.00603","title":"DiC: Rethinking Conv3x3 Designs in Diffusion Models","created":"2024-12-31","cited_by_count":14,"area":"diffusion"},{"id":"2502.04549","title":"Mechanisms of Projective Composition of Diffusion Models","created":"2025-02-06","cited_by_count":14,"area":"diffusion"},{"id":"2602.02493","title":"PixelGen: Improving Pixel Diffusion with Perceptual Supervision","created":"2026-02-02","cited_by_count":14,"area":"diffusion"},{"id":"2402.15170","title":"The Surprising Effectiveness of Skip-Tuning in Diffusion Sampling","created":"2024-02-23","cited_by_count":13,"area":"diffusion"},{"id":"2404.12333","title":"Customizing Text-to-Image Diffusion with Object Viewpoint Control","created":"2024-04-18","cited_by_count":13,"area":"diffusion"},{"id":"2409.00230","title":"Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observations","created":"2024-08-30","cited_by_count":13,"area":"diffusion"},{"id":"2503.07197","title":"Effective and Efficient Masked Image Generation Models","created":"2025-03-10","cited_by_count":13,"area":"diffusion"},{"id":"2411.03177","title":"On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models","created":"2024-11-05","cited_by_count":12,"area":"diffusion"},{"id":"2502.08150","title":"Force Matching with Relativistic Constraints: A Physics-Inspired Approach to Stable and Efficient Generative Modeling","created":"2025-02-12","cited_by_count":12,"area":"diffusion"},{"id":"2510.21608","title":"Generalised Flow Maps for Few-Step Generative Modelling on Riemannian Manifolds","created":"2025-10-24","cited_by_count":12,"area":"diffusion"},{"id":"2503.16397","title":"Scale-wise Distillation of Diffusion Models","created":"2025-03-20","cited_by_count":11,"area":"diffusion"},{"id":"2503.20644","title":"MMGen: Unified Multi-modal Image Generation and Understanding in One Go","created":"2025-03-26","cited_by_count":11,"area":"diffusion"},{"id":"2509.23265","title":"CREPE: Controlling Diffusion with Replica Exchange","created":"2025-09-27","cited_by_count":11,"area":"diffusion"},{"id":"2504.01689","title":"InvFusion: Bridging Supervised and Zero-shot Diffusion for Inverse Problems","created":"2025-04-02","cited_by_count":10,"area":"diffusion"},{"id":"2505.19367","title":"Adaptive Diffusion Guidance via Stochastic Optimal Control","created":"2025-05-25","cited_by_count":10,"area":"diffusion"},{"id":"2509.26096","title":"EVODiff: Entropy-aware Variance Optimized Diffusion Inference","created":"2025-09-30","cited_by_count":10,"area":"diffusion"},{"id":"2602.10314","title":"Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training","created":"2026-02-10","cited_by_count":10,"area":"diffusion"},{"id":"2405.18503","title":"SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound Generation","created":"2024-05-28","cited_by_count":9,"area":"diffusion"},{"id":"2502.08005","title":"DiffRatio: Training One-Step Diffusion Models Without Teacher Supervision","created":"2025-02-11","cited_by_count":9,"area":"diffusion"},{"id":"2602.09651","title":"The Entropic Signature of Class Speciation in Diffusion Models","created":"2026-02-10","cited_by_count":9,"area":"diffusion"},{"id":"2406.10513","title":"Lift Your Molecules: Molecular Graph Generation in Latent Euclidean Space","created":"2024-06-15","cited_by_count":8,"area":"diffusion"},{"id":"2408.08822","title":"PFDiff: Training-Free Acceleration of Diffusion Models Combining Past and Future Scores","created":"2024-08-16","cited_by_count":8,"area":"diffusion"}],"next_cursor":"100"}