{"total":127,"rows":[{"id":"2511.20645","title":"PixelDiT: Pixel Diffusion Transformers for Image Generation","created":"2025-11-25","cited_by_count":83,"area":"diffusion"},{"id":"2601.16208","title":"Scaling Text-to-Image Diffusion Transformers with Representation Autoencoders","created":"2026-01-22","cited_by_count":53,"area":"diffusion"},{"id":"2603.06507","title":"Self-Supervised Flow Matching for Scalable Multi-Modal Synthesis","created":"2026-03-06","cited_by_count":31,"area":"diffusion"},{"id":"2601.09881","title":"Transition Matching Distillation for Fast Video Generation","created":"2026-01-14","cited_by_count":30,"area":"diffusion"},{"id":"2512.07829","title":"One Layer Is Enough: Adapting Pretrained Visual Encoders for Image Generation","created":"2025-12-08","cited_by_count":28,"area":"diffusion"},{"id":"2510.12586","title":"There is No VAE: End-to-End Pixel-Space Generative Modeling via Self-Supervised Pre-training","created":"2025-10-14","cited_by_count":24,"area":"diffusion"},{"id":"2605.11755","title":"One-Step Generative Modeling via Wasserstein Gradient Flows","created":"2026-05-12","cited_by_count":24,"area":"diffusion"},{"id":"2512.17909","title":"Both Semantics and Reconstruction Matter: Making Representation Encoders Ready for Text-to-Image Generation and Editing","created":"2025-12-19","cited_by_count":22,"area":"diffusion"},{"id":"2602.17270","title":"Unified Latents (UL): How to train your latents","created":"2026-02-19","cited_by_count":20,"area":"diffusion"},{"id":"2512.04926","title":"Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion","created":"2025-12-04","cited_by_count":18,"area":"diffusion"},{"id":"2510.18457","title":"VFM-VAE: Vision Foundation Models Can Be Good Tokenizers for Latent Diffusion Models","created":"2025-10-21","cited_by_count":17,"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":"2603.22275","title":"Repurposing Geometric Foundation Models for Multi-view Diffusion","created":"2026-03-23","cited_by_count":15,"area":"diffusion"},{"id":"2602.02493","title":"PixelGen: Improving Pixel Diffusion with Perceptual Supervision","created":"2026-02-02","cited_by_count":14,"area":"diffusion"},{"id":"2603.14366","title":"Representation Alignment for Just Image Transformers is not Easier than You Think","created":"2026-03-15","cited_by_count":14,"area":"diffusion"},{"id":"2605.06548","title":"Continuous Latent Diffusion Language Model","created":"2026-05-07","cited_by_count":14,"area":"diffusion"},{"id":"2602.10099","title":"Learning on the Manifold: Unlocking Standard Diffusion Transformers with Representation Encoders","created":"2026-02-10","cited_by_count":13,"area":"diffusion"},{"id":"2603.05630","title":"Making Reconstruction FID Predictive of Diffusion Generation FID","created":"2026-03-05","cited_by_count":13,"area":"diffusion"},{"id":"2512.11749","title":"SVG-T2I: Scaling Up Text-to-Image Latent Diffusion Model Without Variational Autoencoder","created":"2025-12-12","cited_by_count":12,"area":"diffusion"},{"id":"2605.07933","title":"How to Train Your Latent Diffusion Language Model Jointly With the Latent Space","created":"2026-05-08","cited_by_count":11,"area":"diffusion"},{"id":"2511.22249","title":"Toward Diffusible High-Dimensional Latent Spaces: A Frequency Perspective","created":"2025-11-27","cited_by_count":10,"area":"diffusion"},{"id":"2602.05951","title":"Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matching","created":"2026-02-05","cited_by_count":9,"area":"diffusion"},{"id":"2605.07915","title":"What Matters for Diffusion-Friendly Latent Manifold? Prior-Aligned Autoencoders for Latent Diffusion","created":"2026-05-08","cited_by_count":9,"area":"diffusion"},{"id":"2605.15741","title":"HyperDiT: Hyper-Connected Transformers for High-Fidelity Pixel-Space Diffusion","created":"2026-05-15","cited_by_count":9,"area":"diffusion"},{"id":"2604.17876","title":"OFlow: Injecting Object-Aware Temporal Flow Matching for Robust Robotic Manipulation","created":"2026-04-20","cited_by_count":8,"area":"diffusion"},{"id":"2606.27760","title":"PixelU: A U-Shaped Transformer for Efficient End-to-End Pixel Diffusion","created":"2026-06-26","cited_by_count":8,"area":"diffusion"},{"id":"2601.22904","title":"Hyperspherical Autoencoder for High-Fidelity Image Reconstruction and Generation","created":"2026-01-30","cited_by_count":7,"area":"diffusion"},{"id":"2603.14645","title":"Spectrum Matching: a Unified Perspective for Superior Diffusability in Latent Diffusion","created":"2026-03-15","cited_by_count":7,"area":"diffusion"},{"id":"2603.19206","title":"RPiAE: A Representation-Pivoted Autoencoder Enhancing Both Image Generation and Editing","created":"2026-03-19","cited_by_count":7,"area":"diffusion"},{"id":"2605.23902","title":"PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion","created":"2026-05-22","cited_by_count":7,"area":"diffusion"},{"id":"2511.13019","title":"MeanFlow Transformers with Representation Autoencoders","created":"2025-11-17","cited_by_count":6,"area":"diffusion"},{"id":"2512.20619","title":"SemanticGen: Video Generation in Semantic Space","created":"2025-12-23","cited_by_count":6,"area":"diffusion"},{"id":"2603.22125","title":"DA-VAE: Plug-in Latent Compression for Diffusion via Detail Alignment","created":"2026-03-23","cited_by_count":6,"area":"diffusion"},{"id":"2605.17991","title":"Stable Audio 3","created":"2026-05-18","cited_by_count":6,"area":"diffusion"},{"id":"2512.16636","title":"REGLUE Your Latents with Global and Local Semantics for Entangled Diffusion","created":"2025-12-18","cited_by_count":5,"area":"diffusion"},{"id":"2602.02092","title":"FSVideo: Fast Speed Video Diffusion Model in a Highly-Compressed Latent Space","created":"2026-02-02","cited_by_count":5,"area":"diffusion"},{"id":"2602.06159","title":"Driving with DINO: Vision Foundation Features as a Unified Bridge for Sim-to-Real Generation in Autonomous Driving","created":"2026-02-05","cited_by_count":5,"area":"diffusion"},{"id":"2603.19222","title":"Spectrally-Guided Diffusion Noise Schedules","created":"2026-03-19","cited_by_count":5,"area":"diffusion"},{"id":"2604.11521","title":"Continuous Adversarial Flow Models","created":"2026-04-13","cited_by_count":5,"area":"diffusion"},{"id":"2606.24888","title":"DiffusionBench: On Holistic Evaluation of Diffusion Transformers","created":"2026-06-23","cited_by_count":5,"area":"diffusion"},{"id":"2512.13421","title":"RecTok: Reconstruction Distillation along Rectified Flow","created":"2025-12-15","cited_by_count":4,"area":"diffusion"},{"id":"2605.16949","title":"Beyond Point-Wise Matching: Structural Representation Alignment for Accelerating Diffusion Transformers","created":"2026-05-16","cited_by_count":4,"area":"diffusion"},{"id":"2605.17759","title":"FrequencyBooster: Full-Frequency Modeling for High-Fidelity Pixel Diffusion","created":"2026-05-18","cited_by_count":4,"area":"diffusion"},{"id":"2605.25195","title":"Baton: Explicit Semantic Blueprints for Joint Video-Audio Generation","created":"2026-05-24","cited_by_count":4,"area":"diffusion"},{"id":"2606.16255","title":"UniDDT: Unifying Multimodal Understanding and Generation with Decoupled Diffusion Transformer","created":"2026-06-15","cited_by_count":4,"area":"diffusion"},{"id":"2607.03524","title":"Perceptual Flow Matching for Few-Step Generative Modeling","created":"2026-07-03","cited_by_count":4,"area":"diffusion"},{"id":"2607.05373","title":"PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space","created":"2026-07-06","cited_by_count":4,"area":"diffusion"},{"id":"2608.11205","title":"AdvFD: Boosting Visual Generation via Adversarial Fr'echet Distance Loss","created":"2026-08-11","cited_by_count":4,"area":"diffusion"},{"id":"2511.14716","title":"Diffusion As Self-Distillation: End-to-End Latent Diffusion In One Model","created":"2025-11-18","cited_by_count":3,"area":"diffusion"},{"id":"2511.17986","title":"Plan-X: Instruct Video Generation via Semantic Planning","created":"2025-11-22","cited_by_count":3,"area":"diffusion"},{"id":"2512.12386","title":"Speedrunning ImageNet Diffusion","created":"2025-12-13","cited_by_count":3,"area":"diffusion"},{"id":"2601.22679","title":"Stabilizing Consistency Training: A Flow Map Analysis and Self-Distillation","created":"2026-01-30","cited_by_count":3,"area":"diffusion"},{"id":"2602.04220","title":"Adaptive 1D Video Diffusion Autoencoder","created":"2026-02-04","cited_by_count":3,"area":"diffusion"},{"id":"2604.11331","title":"Any 3D Scene is Worth 1K Tokens: 3D-Grounded Representation for Scene Generation at Scale","created":"2026-04-13","cited_by_count":3,"area":"diffusion"},{"id":"2604.13030","title":"Generative Refinement Networks for Visual Synthesis","created":"2026-04-14","cited_by_count":3,"area":"diffusion"},{"id":"2607.02220","title":"DetailAnywhere: Fashion Detail Generation via Cross-Modal Feature Alignment Distillation","created":"2026-07-02","cited_by_count":3,"area":"diffusion"},{"id":"2607.28611","title":"Chimera: Designing and Chinchilla-Scaling Hybrid Visual Diffusion Transformers","created":"2026-07-30","cited_by_count":3,"area":"diffusion"},{"id":"2607.28760","title":"WaiT for the Signal: Simple Frequency-Aware Flow-Matching","created":"2026-07-30","cited_by_count":3,"area":"diffusion"},{"id":"2511.20592","title":"Latent Diffusion Inversion Requires Understanding the Latent Space","created":"2025-11-25","cited_by_count":2,"area":"diffusion"},{"id":"2601.22468","title":"Training-Free Representation Guidance for Diffusion Models with a Representation Alignment Projector","created":"2026-01-30","cited_by_count":2,"area":"diffusion"},{"id":"2603.10365","title":"Geometric Autoencoder for Diffusion Models","created":"2026-03-11","cited_by_count":2,"area":"diffusion"},{"id":"2603.16792","title":"V-Co: A Closer Look at Visual Representation Alignment via Co-Denoising","created":"2026-03-17","cited_by_count":2,"area":"diffusion"},{"id":"2605.15193","title":"Aligning Latent Geometry for Spherical Flow Matching in Image Generation","created":"2026-05-14","cited_by_count":2,"area":"diffusion"},{"id":"2605.16147","title":"Registers Matter for Pixel-Space Diffusion Transformers","created":"2026-05-15","cited_by_count":2,"area":"diffusion"},{"id":"2605.17244","title":"Drift Flow Matching","created":"2026-05-17","cited_by_count":2,"area":"diffusion"},{"id":"2605.20708","title":"Rethinking Cross-Layer Information Routing in Diffusion Transformers","created":"2026-05-20","cited_by_count":2,"area":"diffusion"},{"id":"2605.21981","title":"RiT: Vanilla Diffusion Transformers Suffice in Representation Space","created":"2026-05-21","cited_by_count":2,"area":"diffusion"},{"id":"2605.22777","title":"DecQ: Detail-Condensing Queries for Enhanced Reconstruction and Generation in Representation Autoencoders","created":"2026-05-21","cited_by_count":2,"area":"diffusion"},{"id":"2606.15236","title":"Show the Signal, Hide the Noise: Spectral Forcing for Pixel-Space Diffusion","created":"2026-06-13","cited_by_count":2,"area":"diffusion"},{"id":"2606.19970","title":"CrossFlow: One-Step Generation Across Latent and Pixel Spaces","created":"2026-06-18","cited_by_count":2,"area":"diffusion"},{"id":"2609.36014","title":"Persistence Forcing: Exploiting Feature Specialization in Pixel-Space Diffusion","created":"2026-09-28","cited_by_count":2,"area":"diffusion"},{"id":"2603.09819","title":"ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation","created":"2026-03-10","cited_by_count":1,"area":"diffusion"},{"id":"2603.19232","title":"Cubic Discrete Diffusion: Discrete Visual Generation on High-Dimensional Representation Tokens","created":"2026-03-19","cited_by_count":1,"area":"diffusion"},{"id":"2603.20584","title":"Improving Diffusion Generalization with Weak-to-Strong Segmented Guidance","created":"2026-03-21","cited_by_count":1,"area":"diffusion"},{"id":"2603.21085","title":"Taming Sampling Perturbations with Variance Expansion Loss for Latent Diffusion Models","created":"2026-03-22","cited_by_count":1,"area":"diffusion"},{"id":"2604.19902","title":"MMCORE: MultiModal COnnection with Representation Aligned Latent Embeddings","created":"2026-04-21","cited_by_count":1,"area":"diffusion"},{"id":"2605.05206","title":"Taming Outlier Tokens in Diffusion Transformers","created":"2026-05-06","cited_by_count":1,"area":"diffusion"},{"id":"2605.20808","title":"Spatial Gram Alignment for Ultra-High-Resolution Image Synthesis","created":"2026-05-20","cited_by_count":1,"area":"diffusion"},{"id":"2605.27102","title":"JLT: Clean-Latent Prediction in Latent Diffusion Transformers","created":"2026-05-26","cited_by_count":1,"area":"diffusion"},{"id":"2606.06120","title":"Diff-CA: Separating Common and Salient Factors with Diffusion Models","created":"2026-06-04","cited_by_count":1,"area":"diffusion"},{"id":"2606.19162","title":"The Reward Was in Your Data All Along: Correcting Flow Matching with Discriminator-Guided RL","created":"2026-06-17","cited_by_count":1,"area":"diffusion"},{"id":"2607.14898","title":"FlashDecoder: Real-Time Latent-to-Pixel Streaming Decoder with Transformers","created":"2026-07-16","cited_by_count":1,"area":"diffusion"},{"id":"2607.18510","title":"DuSPiT: Dual-Branch Sub-Patch Pixel Diffusion Transformer","created":"2026-07-20","cited_by_count":1,"area":"diffusion"},{"id":"2607.19120","title":"Latent Riemannian Flow Matching for Geometry-Grounded 3D Foundation Models","created":"2026-07-21","cited_by_count":1,"area":"diffusion"},{"id":"2608.01298","title":"UDT: Reconciling U-Nets and Diffusion Transformers with Data-Adaptive Token Reduction","created":"2026-08-02","cited_by_count":1,"area":"diffusion"},{"id":"2608.05798","title":"KVAE: Family of Tokenizers for Multimodal Generative Models","created":"2026-08-06","cited_by_count":1,"area":"diffusion"},{"id":"2608.15705","title":"PixelControl: Fine-Grained Condition Fidelity in Text-to-Image Diffusion","created":"2026-08-16","cited_by_count":1,"area":"diffusion"},{"id":"2608.22111","title":"FlowSep 2: Self-Supervised Flow Matching for Language-Queried Audio Source Separation","created":"2026-08-22","cited_by_count":1,"area":"diffusion"},{"id":"2609.02748","title":"Balancing Frequencies and Pixels in Flow Matching","created":"2026-09-02","cited_by_count":1,"area":"diffusion"},{"id":"2609.37147","title":"Improved Distributional Diffusion Models","created":"2026-09-29","cited_by_count":1,"area":"diffusion"},{"id":"2510.18114","title":"Latent-Augmented Discrete Diffusion Models","created":"2025-10-20","cited_by_count":0,"area":"diffusion"},{"id":"2603.05925","title":"RAC: Rectified Flow Auto Coder","created":"2026-03-06","cited_by_count":0,"area":"diffusion"},{"id":"2603.15932","title":"NAMD: Virtual Follow-up Computed Tomography Synthesis via Nodule-Aligned Multimodal Diffusion Models for Early Lung Cancer Diagnosis","created":"2026-03-16","cited_by_count":0,"area":"diffusion"},{"id":"2604.02751","title":"Understanding Latent Diffusability via Fisher Geometry","created":"2026-04-03","cited_by_count":0,"area":"diffusion"},{"id":"2604.17492","title":"Coevolving Representations in Joint Image-Feature Diffusion","created":"2026-04-19","cited_by_count":0,"area":"diffusion"},{"id":"2604.20902","title":"Frequency-Forcing: From Scaling-as-Time to Soft Frequency Guidance","created":"2026-04-21","cited_by_count":0,"area":"diffusion"},{"id":"2605.10084","title":"PoDAR: Power-Disentangled Audio Representation for Generative Modeling","created":"2026-05-11","cited_by_count":0,"area":"diffusion"},{"id":"2605.10790","title":"Elucidating Representation Degradation Problem in Diffusion Model Training","created":"2026-05-11","cited_by_count":0,"area":"diffusion"},{"id":"2605.18267","title":"SRC-Flow: Compact Semantic Representations Enable Normalizing Flows for Image Generation","created":"2026-05-18","cited_by_count":0,"area":"diffusion"},{"id":"2606.00094","title":"Diffusion Image Generation with Explicit Modeling of Data Manifold Geometry","created":"2026-05-25","cited_by_count":0,"area":"diffusion"}],"next_cursor":"100"}