{"total":69,"rows":[{"id":"2510.11690","title":"Diffusion Transformers with Representation Autoencoders","created":"2025-10-13","cited_by_count":295,"area":"diffusion"},{"id":"2511.22699","title":"Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformer","created":"2025-11-27","cited_by_count":277,"area":"diffusion"},{"id":"2506.15564","title":"Show-o2: Improved Native Unified Multimodal Models","created":"2025-06-18","cited_by_count":250,"area":"diffusion"},{"id":"2510.06308","title":"Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understanding","created":"2025-10-07","cited_by_count":88,"area":"diffusion"},{"id":"2507.23268","title":"PixNerd: Pixel Neural Field Diffusion","created":"2025-07-31","cited_by_count":82,"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":"2508.10711","title":"NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scale","created":"2025-08-14","cited_by_count":57,"area":"diffusion"},{"id":"2505.23606","title":"Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model","created":"2025-05-29","cited_by_count":55,"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":"2509.04394","title":"Transition Models: Rethinking the Generative Learning Objective","created":"2025-09-04","cited_by_count":34,"area":"diffusion"},{"id":"2507.08801","title":"Lumos-1: On Autoregressive Video Generation with Discrete Diffusion from a Unified Model Perspective","created":"2025-07-11","cited_by_count":22,"area":"diffusion"},{"id":"2510.13721","title":"NExT-OMNI: Towards Any-to-Any Omnimodal Foundation Models with Discrete Flow Matching","created":"2025-10-15","cited_by_count":21,"area":"diffusion"},{"id":"2602.08820","title":"Omni-Video 2: Scaling MLLM-Conditioned Diffusion for Unified Video Generation and Editing","created":"2026-02-09","cited_by_count":19,"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":"2509.19244","title":"Lavida-O: Elastic Large Masked Diffusion Models for Unified Multimodal Understanding and Generation","created":"2025-09-23","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":"2511.20520","title":"HBridge: H-Shape Bridging of Heterogeneous Experts for Unified Multimodal Understanding and Generation","created":"2025-11-25","cited_by_count":11,"area":"diffusion"},{"id":"2508.08098","title":"TBAC-UniImage: Unified Understanding and Generation by Ladder-Side Diffusion Tuning","created":"2025-08-11","cited_by_count":10,"area":"diffusion"},{"id":"2512.17951","title":"SuperFlow: Training Flow Matching Models with RL on the Fly","created":"2025-12-17","cited_by_count":10,"area":"diffusion"},{"id":"2603.01068","title":"LLaDA-o: An Effective and Length-Adaptive Omni Diffusion Model","created":"2026-03-01","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":"2602.13993","title":"Elastic Diffusion Transformer","created":"2026-02-15","cited_by_count":8,"area":"diffusion"},{"id":"2601.09255","title":"PhyRPR: Training-Free Physics-Constrained Video Generation","created":"2026-01-14","cited_by_count":7,"area":"diffusion"},{"id":"2512.14008","title":"Sparse-LaViDa: Sparse Multimodal Discrete Diffusion Language Models","created":"2025-12-16","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":"2604.12163","title":"Nucleus-Image: Sparse MoE for Image Generation","created":"2026-04-14","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":"2506.09482","title":"Marrying Autoregressive Transformer and Diffusion with Multi-Reference Autoregression","created":"2025-06-11","cited_by_count":4,"area":"diffusion"},{"id":"2506.23543","title":"Pyramidal Patchification Flow for Visual Generation","created":"2025-06-30","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":"2607.05373","title":"PixWorld: Unifying 3D Scene Generation and Reconstruction in Pixel Space","created":"2026-07-06","cited_by_count":4,"area":"diffusion"},{"id":"2509.26644","title":"Stitch: Training-Free Position Control in Multimodal Diffusion Transformers","created":"2025-09-30","cited_by_count":3,"area":"diffusion"},{"id":"2511.16917","title":"UniModel: A Visual-Only Framework for Unified Multimodal Understanding and Generation","created":"2025-11-21","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":"2602.04220","title":"Adaptive 1D Video Diffusion Autoencoder","created":"2026-02-04","cited_by_count":3,"area":"diffusion"},{"id":"2607.14681","title":"ReBind: Multi-Reference Video Editing via Structured Instructions with Explicit Reference Relationships","created":"2026-07-16","cited_by_count":3,"area":"diffusion"},{"id":"2609.03796","title":"LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes","created":"2026-09-03","cited_by_count":3,"area":"diffusion"},{"id":"2511.12207","title":"Mixture of States: Routing Token-Level Dynamics for Multimodal Generation","created":"2025-11-15","cited_by_count":2,"area":"diffusion"},{"id":"2604.06966","title":"MAR-GRPO: Stabilized GRPO for AR-diffusion Hybrid Image Generation","created":"2026-04-08","cited_by_count":2,"area":"diffusion"},{"id":"2604.08121","title":"Uni-ViGU: Towards Unified Video Generation and Understanding via A Diffusion-Based Video Generator","created":"2026-04-09","cited_by_count":2,"area":"diffusion"},{"id":"2606.11289","title":"i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models","created":"2026-06-09","cited_by_count":2,"area":"diffusion"},{"id":"2505.16875","title":"T2I-ConBench: Text-to-Image Benchmark for Continual Post-training","created":"2025-05-22","cited_by_count":1,"area":"diffusion"},{"id":"2509.23760","title":"UniAlignment: Semantic Alignment for Unified Image Generation, Understanding, Manipulation and Perception","created":"2025-09-28","cited_by_count":1,"area":"diffusion"},{"id":"2602.03448","title":"Hierarchical Concept-to-Appearance Guidance for Multi-Subject Image Generation","created":"2026-02-03","cited_by_count":1,"area":"diffusion"},{"id":"2602.06886","title":"Prompt Reinjection: Alleviating Prompt Forgetting in Multimodal Diffusion Transformers for Text-to-Image Generation","created":"2026-02-06","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":"2603.20828","title":"EruDiff: Refactoring Knowledge in Diffusion Models for Advanced Text-to-Image Synthesis","created":"2026-03-21","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":"2605.16122","title":"GenShield: Unified Detection and Artifact Correction for AI-Generated Images","created":"2026-05-15","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":"2608.15705","title":"PixelControl: Fine-Grained Condition Fidelity in Text-to-Image Diffusion","created":"2026-08-16","cited_by_count":1,"area":"diffusion"},{"id":"2606.08833","title":"CSFlow: Aligning Flow Matching with Human Contrast Sensitivity","created":"2026-06-07","cited_by_count":0,"area":"diffusion"},{"id":"2606.29814","title":"Nemotron-Labs-Diffusion-Image: Advancing Masked Discrete Diffusion for High-Resolution Image Synthesis","created":"2026-06-29","cited_by_count":0,"area":"diffusion"},{"id":"2608.05811","title":"Energy-Guided Flow Matching","created":"2026-08-06","cited_by_count":0,"area":"diffusion"},{"id":"2608.24068","title":"Representation Learning in Diffusion and Flow-based Model: An Application Aspect","created":"2026-08-25","cited_by_count":0,"area":"diffusion"},{"id":"2609.28473","title":"On the Diffusibility of High-Dimensional Latents","created":"2026-09-23","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":"2609.38156","title":"DMA$^2$: Pixel-space Distribution Matching with Adversarial and Anchor Losses","created":"2026-09-29","cited_by_count":0,"area":"diffusion"},{"id":"2609.38170","title":"Adversarial Training for Pixel Diffusion","created":"2026-09-29","cited_by_count":0,"area":"diffusion"},{"id":"2609.40305","title":"Looped Diffusion Transformer","created":"2026-09-30","cited_by_count":0,"area":"diffusion"},{"id":"2610.00483","title":"PixelDense: Dense Prediction as Representation Alignment for Pixel Diffusion","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"},{"id":"2610.08070","title":"Two Halves are More than One: Phase-wise Velocity Distillation for Fast and High-Quality Image Generation","created":"2026-10-06","cited_by_count":0,"area":"diffusion"},{"id":"2610.12978","title":"Learning Representations to Guide Pixel Diffusion Transformers","created":"2026-10-09","cited_by_count":0,"area":"diffusion"},{"id":"2610.14136","title":"Correction Training for Diffusion Models","created":"2026-10-09","cited_by_count":0,"area":"diffusion"},{"id":"2610.20608","title":"Representation Conditioning for Generation","created":"2026-10-14","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}