{"total":104,"rows":[{"id":"2501.01423","title":"Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models","created":"2025-01-02","cited_by_count":393,"area":"diffusion"},{"id":"2510.11690","title":"Diffusion Transformers with Representation Autoencoders","created":"2025-10-13","cited_by_count":295,"area":"diffusion"},{"id":"2506.09113","title":"Seedance 1.0: Exploring the Boundaries of Video Generation Models","created":"2025-06-10","cited_by_count":259,"area":"diffusion"},{"id":"2502.01776","title":"Sparse VideoGen: Accelerating Video Diffusion Transformers with Spatial-Temporal Sparsity","created":"2025-02-03","cited_by_count":217,"area":"diffusion"},{"id":"2411.05007","title":"SVDQuant: Absorbing Outliers by Low-Rank Components for 4-Bit Diffusion Models","created":"2024-11-07","cited_by_count":170,"area":"diffusion"},{"id":"2505.18875","title":"Sparse VideoGen2: Accelerate Video Generation with Sparse Attention via Semantic-Aware Permutation","created":"2025-05-24","cited_by_count":106,"area":"diffusion"},{"id":"2412.17808","title":"Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders","created":"2024-12-23","cited_by_count":98,"area":"diffusion"},{"id":"2502.03444","title":"Masked Autoencoders Are Effective Tokenizers for Diffusion Models","created":"2025-02-05","cited_by_count":96,"area":"diffusion"},{"id":"2509.24695","title":"SANA-Video: Efficient Video Generation with Block Linear Diffusion Transformer","created":"2025-09-29","cited_by_count":94,"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":"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":"2412.15205","title":"FlowAR: Scale-wise Autoregressive Image Generation Meets Flow Matching","created":"2024-12-19","cited_by_count":80,"area":"diffusion"},{"id":"2502.14831","title":"Improving the Diffusability of Autoencoders","created":"2025-02-20","cited_by_count":80,"area":"diffusion"},{"id":"2503.18352","title":"Diffusion-4K: Ultra-High-Resolution Image Synthesis with Latent Diffusion Models","created":"2025-03-24","cited_by_count":65,"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":"2506.00045","title":"ACE-Step: A Step Towards Music Generation Foundation Model","created":"2025-05-28","cited_by_count":56,"area":"diffusion"},{"id":"2510.15857","title":"BLIP3o-NEXT: Next Frontier of Native Image Generation","created":"2025-10-17","cited_by_count":54,"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":"2511.18822","title":"DiP: Taming Diffusion Models in Pixel Space","created":"2025-11-24","cited_by_count":48,"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":"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":"2602.11401","title":"Latent Forcing: Reordering the Diffusion Trajectory for Pixel-Space Image Generation","created":"2026-02-11","cited_by_count":36,"area":"diffusion"},{"id":"2509.04394","title":"Transition Models: Rethinking the Generative Learning Objective","created":"2025-09-04","cited_by_count":34,"area":"diffusion"},{"id":"2511.07399","title":"StreamDiffusionV2: A Streaming System for Dynamic and Interactive Video Generation","created":"2025-11-10","cited_by_count":30,"area":"diffusion"},{"id":"2503.03206","title":"An Analytical Theory of Spectral Bias in the Learning Dynamics of Diffusion Models","created":"2025-03-05","cited_by_count":26,"area":"diffusion"},{"id":"2503.16302","title":"Unleashing Vecset Diffusion Model for Fast Shape Generation","created":"2025-03-20","cited_by_count":26,"area":"diffusion"},{"id":"2505.14708","title":"DraftAttention: Fast Video Diffusion via Low-Resolution Attention Guidance","created":"2025-05-17","cited_by_count":22,"area":"diffusion"},{"id":"2509.25079","title":"UniLat3D: Geometry-Appearance Unified Latents for Single-Stage 3D Generation","created":"2025-09-29","cited_by_count":20,"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":"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":"2503.19462","title":"AccVideo: Accelerating Video Diffusion Model with Synthetic Dataset","created":"2025-03-25","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":"2503.07197","title":"Effective and Efficient Masked Image Generation Models","created":"2025-03-10","cited_by_count":13,"area":"diffusion"},{"id":"2504.10567","title":"H3AE: High Compression, High Speed, and High Quality AutoEncoder for Video Diffusion Models","created":"2025-04-14","cited_by_count":13,"area":"diffusion"},{"id":"2602.05435","title":"Stable Velocity: A Variance Perspective on Flow Matching","created":"2026-02-05","cited_by_count":12,"area":"diffusion"},{"id":"2507.09984","title":"Latent Diffusion Models with Masked AutoEncoders","created":"2025-07-14","cited_by_count":11,"area":"diffusion"},{"id":"2512.05394","title":"Delving into Latent Spectral Biasing of Video VAEs for Superior Diffusability","created":"2025-12-05","cited_by_count":11,"area":"diffusion"},{"id":"2501.12976","title":"LiT: Delving into a Simple Linear Diffusion Transformer for Image Generation","created":"2025-01-22","cited_by_count":10,"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":"2601.17830","title":"SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Training","created":"2026-01-25","cited_by_count":9,"area":"diffusion"},{"id":"2602.08620","title":"Improving Reconstruction of Representation Autoencoder","created":"2026-02-09","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":"2605.12013","title":"L2P: Unlocking Latent Potential for Pixel Generation","created":"2026-05-12","cited_by_count":9,"area":"diffusion"},{"id":"2508.04663","title":"HierarchicalPrune: Position-Aware Compression for Large-Scale Diffusion Models","created":"2025-08-06","cited_by_count":8,"area":"diffusion"},{"id":"2509.25180","title":"DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space","created":"2025-09-29","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":"2504.12259","title":"VGDFR: Diffusion-based Video Generation with Dynamic Latent Frame Rate","created":"2025-04-16","cited_by_count":7,"area":"diffusion"},{"id":"2505.19151","title":"SRDiffusion: Accelerate Video Diffusion Inference via Sketching-Rendering Cooperation","created":"2025-05-25","cited_by_count":7,"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":"2605.23902","title":"PiD: Fast and High-Resolution Latent Decoding with Pixel Diffusion","created":"2026-05-22","cited_by_count":7,"area":"diffusion"},{"id":"2503.08737","title":"Representing 3D Shapes With 64 Latent Vectors for 3D Diffusion Models","created":"2025-03-11","cited_by_count":6,"area":"diffusion"},{"id":"2506.09193","title":"LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting","created":"2025-06-10","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":"2608.16887","title":"An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models","created":"2026-08-17","cited_by_count":6,"area":"diffusion"},{"id":"2506.01331","title":"Ultra-High-Resolution Image Synthesis: Data, Method and Evaluation","created":"2025-06-02","cited_by_count":5,"area":"diffusion"},{"id":"2512.18184","title":"Is There a Better Source Distribution than Gaussian? Exploring Source Distributions for Image Flow Matching","created":"2025-12-20","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":"2608.26794","title":"Ring Forcing: Towards Precise Long-Term Memory for Autoregressive Video Diffusion","created":"2026-08-27","cited_by_count":5,"area":"diffusion"},{"id":"2501.05442","title":"Progressive Growing of Video Tokenizers for Temporally Compact Latent Spaces","created":"2025-01-09","cited_by_count":4,"area":"diffusion"},{"id":"2503.09242","title":"NAMI: Efficient Image Generation via Bridged Progressive Rectified Flow Transformers","created":"2025-03-12","cited_by_count":4,"area":"diffusion"},{"id":"2510.27135","title":"E-MMDiT: Revisiting Multimodal Diffusion Transformer Design for Fast Image Synthesis under Limited Resources","created":"2025-10-31","cited_by_count":4,"area":"diffusion"},{"id":"2511.21475","title":"MobileI2V: Fast and High-Resolution Image-to-Video on Mobile Devices","created":"2025-11-26","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":"2507.09103","title":"CoVAE: Consistency Training of Variational Autoencoders","created":"2025-07-12","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":"2506.23618","title":"TurboVSR: Fantastic Video Upscalers and Where to Find Them","created":"2025-06-30","cited_by_count":2,"area":"diffusion"},{"id":"2603.26357","title":"MPDiT: Multi-Patch Global-to-Local Transformer Architecture For Efficient Flow Matching and Diffusion Model","created":"2026-03-27","cited_by_count":2,"area":"diffusion"},{"id":"2604.16479","title":"Latent-Compressed Variational Autoencoder for Video Diffusion Models","created":"2026-04-12","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":"2605.25294","title":"Geometry-Aware Image Flow Matching","created":"2026-05-24","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":"2609.15810","title":"VC-Attention: Value Smoothing and Softmax Casting for Low-bit Attention","created":"2026-09-14","cited_by_count":2,"area":"diffusion"},{"id":"2511.16117","title":"Decoupling Complexity from Scale in Latent Diffusion Model","created":"2025-11-20","cited_by_count":1,"area":"diffusion"},{"id":"2601.01475","title":"Multi-Subspace Multi-Modal Modeling for Diffusion Models: Estimation, Convergence and Mixture of Experts","created":"2026-01-04","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":"2605.20808","title":"Spatial Gram Alignment for Ultra-High-Resolution Image Synthesis","created":"2026-05-20","cited_by_count":1,"area":"diffusion"},{"id":"2607.03012","title":"HyperVAttention: Efficient Sparse Attention with Spatio-Temporal Clustering for Video Diffusion","created":"2026-07-03","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":"2607.22531","title":"Twins: Learn to Predict Unified Representations with Focal Loss","created":"2026-07-24","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":"2507.00447","title":"Latent Posterior-Mean Rectified Flow for Higher-Fidelity Perceptual Face Restoration","created":"2025-07-01","cited_by_count":0,"area":"diffusion"},{"id":"2509.06068","title":"Home-made Diffusion Model from Scratch to Hatch","created":"2025-09-07","cited_by_count":0,"area":"diffusion"},{"id":"2602.11807","title":"PuYun-LDM: A Latent Diffusion Model for High-Resolution Ensemble Weather Forecasts","created":"2026-02-12","cited_by_count":0,"area":"diffusion"},{"id":"2603.24428","title":"Marchuk: Efficient Global Weather Forecasting from Mid-Range to Sub-Seasonal Scales via Flow Matching","created":"2026-03-25","cited_by_count":0,"area":"diffusion"},{"id":"2604.00298","title":"SANA I2I: A Text Free Flow Matching Framework for Paired Image to Image Translation with a Case Study in Fetal MRI Artifact Reduction","created":"2026-03-31","cited_by_count":0,"area":"diffusion"},{"id":"2605.14531","title":"Language Generation as Optimal Control: Closed-Loop Diffusion in Latent Control Space","created":"2026-05-14","cited_by_count":0,"area":"diffusion"},{"id":"2605.15684","title":"ElasticDiT: Efficient Diffusion Transformers via Elastic Architecture and Sparse Attention for High-Resolution Image Generation on Mobile Devices","created":"2026-05-15","cited_by_count":0,"area":"diffusion"},{"id":"2606.01620","title":"Real-Time Generation of Streamable Talking Portrait Video with Reference-Guided Deep Compression VAEs","created":"2026-06-01","cited_by_count":0,"area":"diffusion"},{"id":"2606.25545","title":"TensorLDM: A Component-Wise Latent Diffusion Model for Volumetric DTI Reconstruction from Sparse DWIs","created":"2026-06-24","cited_by_count":0,"area":"diffusion"},{"id":"2608.05450","title":"MOSAIK: Multi-Patch Content-Aware Spatial Allocation of Image Tokens for Efficient Generation","created":"2026-08-05","cited_by_count":0,"area":"diffusion"},{"id":"2608.24293","title":"Keep-or-Drop? Adaptive Tokenizer for Compact Video Representation","created":"2026-08-25","cited_by_count":0,"area":"diffusion"},{"id":"2609.00798","title":"Advanced Pixel Diffusion Model with Guided Sparse Global Refinement","created":"2026-09-01","cited_by_count":0,"area":"diffusion"},{"id":"2609.06665","title":"TransNormal-2: Geometry-Grounded Rectified Flow with Edge-Aware Decoding for Precise Normal Estimation","created":"2026-09-06","cited_by_count":0,"area":"diffusion"},{"id":"2609.33895","title":"Residual-Stream Burden Shapes Representation Learning in Diffusion Transformers","created":"2026-09-27","cited_by_count":0,"area":"diffusion"},{"id":"2609.37080","title":"LDM-is-AE: Latent Diffusion Model is an Auto-Encoder for End-to-End Image Generation","created":"2026-09-29","cited_by_count":0,"area":"diffusion"},{"id":"2609.39222","title":"DC-SAE: Deep Compression Semantic Autoencoder for Faster Diffusion Convergence","created":"2026-09-30","cited_by_count":0,"area":"diffusion"},{"id":"2610.02203","title":"Embedding Prediction Helps Image Generation","created":"2026-10-01","cited_by_count":0,"area":"diffusion"}],"next_cursor":"100"}