{"total":22,"rows":[{"id":"2410.08261","title":"Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis","created":"2024-10-10","cited_by_count":56,"area":"diffusion"},{"id":"2503.20853","title":"Unified Multimodal Discrete Diffusion","created":"2025-03-26","cited_by_count":49,"area":"diffusion"},{"id":"2412.09619","title":"SnapGen: Taming High-Resolution Text-to-Image Models for Mobile Devices with Efficient Architectures and Training","created":"2024-12-12","cited_by_count":27,"area":"diffusion"},{"id":"2510.24711","title":"Routing Matters in MoE: Scaling Diffusion Transformers with Explicit Routing Guidance","created":"2025-10-28","cited_by_count":26,"area":"diffusion"},{"id":"2503.14487","title":"DiffMoE: Dynamic Token Selection for Scalable Diffusion Transformers","created":"2025-03-18","cited_by_count":24,"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":"2505.10046","title":"Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis","created":"2025-05-15","cited_by_count":16,"area":"diffusion"},{"id":"2501.05450","title":"Decentralized Diffusion Models","created":"2025-01-09","cited_by_count":13,"area":"diffusion"},{"id":"2503.16726","title":"EDiT: Efficient Diffusion Transformers with Linear Compressed Attention","created":"2025-03-20","cited_by_count":8,"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":"2510.21986","title":"Sprint: Sparse-Dense Residual Fusion for Efficient Diffusion Transformers","created":"2025-10-24","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":"2412.03791","title":"INRFlow: Flow Matching for INRs in Ambient Space","created":"2024-12-05","cited_by_count":2,"area":"diffusion"},{"id":"2412.06248","title":"Rendering-Refined Stable Diffusion for Privacy Compliant Synthetic Data","created":"2024-12-09","cited_by_count":2,"area":"diffusion"},{"id":"2601.15417","title":"Ambient Dataloops: Generative Models for Dataset Refinement","created":"2026-01-21","cited_by_count":2,"area":"diffusion"},{"id":"2606.08788","title":"MaskAlign: Token-Subset Representation Alignment for Efficient Diffusion Training","created":"2026-06-07","cited_by_count":2,"area":"diffusion"},{"id":"2601.01608","title":"Guiding Token-Sparse Diffusion Models","created":"2026-01-04","cited_by_count":1,"area":"diffusion"},{"id":"2604.24885","title":"VibeToken: Scaling 1D Image Tokenizers and Autoregressive Models for Dynamic Resolution Generations","created":"2026-04-27","cited_by_count":1,"area":"diffusion"},{"id":"2605.07253","title":"LENS: Low-Frequency Eigen Noise Shaping for Efficient Diffusion Sampling","created":"2026-05-08","cited_by_count":1,"area":"diffusion"},{"id":"2501.07741","title":"Gaussian Universality for Diffusion Models","created":"2025-01-13","cited_by_count":0,"area":"diffusion"},{"id":"2606.14756","title":"Divide-and-Denoise: A Game-Theoretic Method for Fairly Composing Diffusion Models","created":"2026-06-08","cited_by_count":0,"area":"diffusion"},{"id":"2610.01296","title":"ITC-MoE: Importance-guided Token-aware Compression for MoE Diffusion Language Models","created":"2026-10-01","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}