🌟 Awesome Unified Multimodal Models

Curated papers on unifying multimodal understanding and generation from my regular reading.

📬 Have a new paper or collaboration idea? Reach out to me at: niuyuwei04@gmail.com.
🤝 Seeking Opportunities: I'm eager to discuss and collaborate, especially for industry internships in unified multimodal model!

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📑 Table of Contents

• 2025
• 2024
• 2023

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📄 Papers

2025

• [2026-01-07] UniCorn: Towards Self-Improving Unified Multimodal Models through Self-Generated Supervision
• [2025-11-28] Does Understanding Inform Generation in Unified Multimodal Models? From Analysis to Path Forward
• [2025-10-15] SRUM: Fine-Grained Self-Rewarding for Unified Multimodal Models
  📧 You can contact us by mail, authorized!
• [2025-09-19] MANZANO: A Simple and Scalable Unified Multimodal Model with a Hybrid Vision Tokenizer
  ⭐ Highly recommended!
• [2025-09-17] GenExam: A Multidisciplinary Text-to-Image Exam
  
• [2025-09-10] Reconstruction Alignment Improves Unified Multimodal Models
  ⭐ Highly recommended!
• [2025-09-08] OneCAT: Decoder-Only Auto-Regressive Model for Unified Understanding and Generation
  
• [2024-08-07] UniCoT: Unifying Instruction Tuning and Chain-of-Thought Reasoning for Vision-Language Models
  
• [2025-08-08] Bifrost-1: Bridging Multimodal LLMs and Diffusion Models with Patch-level CLIP Latents
  
• [2025-08-04] Qwen-Image Technical Report
  ⭐ Highly recommended!
• [2025-07-30] X-Omni: Reinforcement Learning Makes Discrete Autoregressive Image Generative Models Great Again
  
• [2025-07-23] Turning Internal Gap into Self-Improvement: Promoting the Generation-Understanding Unification in MLLMs
• [2025-06-25] Vision as a Dialect: Unifying Visual Understanding and Generation via Text-Aligned Representations
  
• [2025-06-19] Show-o2: Improved Native Unified Multimodal Models
  
• [2025-06-09] Reinforcing Multimodal Understanding and Generation with Dual Self-rewards
  
• [2025-06-08] LaTtE-Flow: Layerwise Timestep-Expert Flow-based Transformer
  
• [2025-06-04] UniWorld: High-Resolution Semantic Encoders for Unified Visual Understanding and Generation
  ⭐ Highly recommended!
• [2025-05-30] Are Unified Vision-Language Models Necessary: Generalization Across Understanding and Generation
  
• [2025-05-30] UniRL: Self-Improving Unified Multimodal Models via Supervised and Reinforcement Learning
  
• [2025-05-30] Muddit: Liberating Generation Beyond Text-to-Image with a Unified Discrete Diffusion Model
  ⭐ Highly recommended!
• [2025-05-30] OpenUni: A Simple Baseline for Unified Multimodal Understanding and Generation
  
• [2025-05-30] R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generation
  
• [2025-05-21] Emerging Properties in Unified Multimodal Pretraining
  ⭐ Highly recommended!
• [2025-05-21] UniGen: Enhanced Training & Test-Time Strategies for Unified Multimodal Understanding and Generation
  
• [2025-05-19] MindOmni: Unleashing Reasoning Generation in Vision Language Models with RGPO
  
• [2025-05-16] End-to-End Vision Tokenizer Tuning
  
• [2025-05-16] Exploring the Deep Fusion of Large Language Models and Diffusion Transformers for Text-to-Image Synthesis
  
• [2025-05-15] BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset
  ⭐ Highly recommended!
• [2025-05-12] Discrete Visual Tokens of Autoregression, by Diffusion, and for Reasoning
  
• [2025-05-09] Mogao: An Omni Foundation Model for Interleaved Multi-Modal Generation
  
• [2025-05-09] TokLIP: Marry Visual Tokens to CLIP for Multimodal Comprehension and Generation
  
• [2025-05-08] Ming-Lite-Uni: Advancements in Unified Architecture for Natural Multimodal Interaction
  
• [2025-05-01] T2I-R1: Reinforcing Image Generation with Collaborative Semantic-level and Token-level CoT
  
• [2025-04-29] X-Fusion: Introducing New Modality to Frozen Large Language Models
  
• [2025-04-29] YoChameleon: Personalized Vision and Language Generation
  
• [2025-04-24] Step1X-Edit: A Practical Framework for General Image Editing
  
• [2025-04-20] Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens
  
• [2025-04-09] Transfer between Modalities with MetaQueries
  
  ⭐ Highly recommended!
• [2025-04-03] VARGPT-v1.1: Improve Visual Autoregressive Large Unified Model via Iterative Instruction Tuning and Reinforcement Learning
  
• [2025-04-03] UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding
  
• [2025-03-27] UGen: Unified Autoregressive Multimodal Model with Progressive Vocabulary Learning
  
• [2025-03-27] Harmonizing Visual Representations for Unified Multimodal Understanding and Generation
  
• [2025-03-27] ILLUME+: Illuminating Unified MLLM with Dual Visual Tokenization and Diffusion Refinement
  
• [2025-03-20] Unified Multimodal Discrete Diffusion
  
• [2025-03-19] DualToken: Towards Unifying Visual Understanding and Generation with Dual Visual Vocabularies
  
• [2025-03-18] Unified Autoregressive Visual Generation and Understanding with Continuous Tokens
  
• [2025-03-10] WISE: A World Knowledge-Informed Semantic Evaluation for Text-to-Image Generation
  
  ⭐ Highly recommended! The only study that examines whether understanding benefits generation at the level of world knowledge. (and yes, it's my work! haha).
• [2025-03-09] SemHiTok: A Unified Image Tokenizer via Semantic-Guided Hierarchical Codebook for Multimodal Understanding and Generation
  
• [2025-03-08] USP: Unified Self-Supervised Pretraining for Image Generation and Understanding
  
• [2025-02-27] UniTok: A Unified Tokenizer for Visual Generation and Understanding
  
• [2025-02-17] HermesFlow: Seamlessly Closing the Gap in Multimodal Understanding and Generation
  
• [2025-02-07] QLIP: Text-Aligned Visual Tokenization Unifies Auto-Regressive Multimodal Understanding and Generation
  
• [2025-01-21] VARGPT: Unified Understanding and Generation in a Visual Autoregressive Multimodal Large Language Model
  
• [2025-01-21] Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling
  

2024

• [2024-12-31] Dual Diffusion for Unified Image Generation and Understanding
  
• [2024-12-26] LMFusion: Adapting Pretrained Language Models for Multimodal Generation
  
• [2024-12-18] MetaMorph: Multimodal Understanding and Generation via Instruction Tuning
  ⭐ Highly recommended! The most easy training way!
• [2024-12-12] SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding
  
• [2024-12-11] Multimodal Latent Language Modeling with Next-Token Diffusion
  
• [2024-12-09] ILLUME: Illuminating Your LLMs to See, Draw, and Self-Enhance
  
• [2024-12-09] Visual Lexicon: Rich Image Features in Language Space
  
• [2024-12-08] SILMM: Self-Improving Large Multimodal Models for Compositional Text-to-Image Generation
  
• [2024-12-05] Liquid: Language Models are Scalable Multi-modal Generators
  
• [2024-12-05] MUSE-VL: Modeling Unified VLM through Semantic Discrete Encoding
  
• [2024-12-04] TokenFlow: Unified Image Tokenizer for Multimodal Understanding and Generation
  
• [2024-11-28] Orthus: Autoregressive Interleaved Image-Text Generation with Modality-Specific Heads
  
• [2024-11-12] JanusFlow: Harmonizing Autoregression and Rectified Flow for Unified Multimodal Understanding and Generation
  
• [2024-10-31] MIO: A Foundation Model on Multimodal Tokens
  
• [2024-10-23] VILA-U: A Unified Foundation Model Integrating Visual Understanding and Generation
  
• [2024-10-21] PUMA: Empowering Unified MLLM with Multi-granular Visual Generation
  
• [2024-10-21] Show-o: One Single Transformer to Unify Multimodal Understanding and Generation
  
• [2024-10-17] Janus: Decoupling Visual Encoding for Unified Multimodal Understanding and Generation
  
• [2024-10-15] MMAR: Towards Lossless Multi-Modal Auto-Regressive Probabilistic Modeling
  
• [2024-09-27] Emu3: Next-Token Prediction is All You Need
  
• [2024-08-20] Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
  
• [2024-05-16] Chameleon: Mixed-Modal Early-Fusion Foundation Models
  
• [2024-05-08] Emu: Generative Pretraining in Multimodality
  
• [2024-05-08] Emu2: Generative Multimodal Models are In-Context Learners
  
• [2024-04-22] SEED-X: Multimodal Models with Unified Multi-granularity Comprehension and Generation
  
• [2024-03-22] LaVIT: Unified Language-Vision Pretraining in LLM with Dynamic Discrete Visual Tokenization
  

2023

• [2023-08-12] SEED: Planting a SEED of Vision in Large Language Model
  
  Pioneering vision integration into LLMs.

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🔗 Useful Links

• (TMLR 2025) Awesome-Autoregressive-Models-in-Vision
  You can refer this page for related unified models.
• Awesome-Unified-Multimodal-Models
  Another excellent resource for multimodal research.
• SOTA-Paper-Rating
  Compare and evaluate state-of-the-art papers.

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Contributions welcome! Add new papers via pull requests or email me directly.
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