mlsanityData to 7 Oct 2026

Lumina-Image 2.0: A Unified and Efficient Image Generative Framework

arXiv 2503.21758 ↗First posted 27 Mar 2025cs.CVcited by 93

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Cited by 31 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce Lumina-Image 2.0, an advanced text-to-image generation framework that achieves significant progress compared to previous work, Lumina-Next. Lumina-Image 2.0 is built upon two key principles: (1) Unification - it adopts a unified architecture (Unified Next-DiT) that treats text and image tokens as a joint sequence, enabling natural cross-modal interactions and allowing seamless task expansion. Besides, since high-quality captioners can provide semantically well-aligned text-image training pairs, we introduce a unified captioning system, Unified Captioner (UniCap), specifically designed for T2I generation tasks. UniCap excels at generating comprehensive and accurate captions, accelerating convergence and enhancing prompt adherence. (2) Efficiency - to improve the efficiency of our proposed model, we develop multi-stage progressive training strategies and introduce inference acceleration techniques without compromising image quality. Extensive evaluations on academic benchmarks and public text-to-image arenas show that Lumina-Image 2.0 delivers strong performances even with only 2.6B parameters, highlighting its scalability and design efficiency. We have released our training details, code, and models at https://github.com/Alpha-VLLM/Lumina-Image-2.0.

Key facts. Cited by 93 papers in the map (data through 7 Oct 2026). Builds on 67 of its 82 references that are in the map. First posted on arXiv 27 Mar 2025.

Builds on

67 of 82 references.

References · 67

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  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
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  4. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
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38 more
  1. 2025Qwen2.5-VL Technical Reportcited by 5,418
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  3. 2023SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesiscited by 4,290
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  5. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,094
  6. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
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  17. 2023Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generationcited by 950
  18. 2023GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignmentcited by 946
  19. 2022DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Modelscited by 924
  20. 2024Chameleon: Mixed-Modal Early-Fusion Foundation Modelscited by 904
  21. 2023PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesiscited by 854
  22. 2024Emu3: Next-Token Prediction is All You Needcited by 761
  23. 2024Autoregressive Model Beats Diffusion: Llama for Scalable Image Generationcited by 760
  24. 2024Show-o: One Single Transformer to Unify Multimodal Understanding and Generationcited by 734
  25. 2023CogVLM: Visual Expert for Pretrained Language Modelscited by 704
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  27. 2024ELLA: Equip Diffusion Models with LLM for Enhanced Semantic Alignmentcited by 455
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  29. 2023Florence-2: Advancing a Unified Representation for a Variety of Vision Taskscited by 403
  30. 2024OmniGen: Unified Image Generationcited by 388
  31. 2024SANA: Efficient High-Resolution Image Synthesis with Linear Diffusion Transformerscited by 331
  32. 2024PixArt-Sigma: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generationcited by 326
  33. 2023PhotoMaker: Customizing Realistic Human Photos via Stacked ID Embeddingcited by 324
  34. 2023InternLM-XComposer: A Vision-Language Large Model for Advanced Text-image Comprehension and Compositioncited by 297
  35. 2024Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesiscited by 297
  36. 2024Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understandingcited by 294
  37. 2024Timestep Embedding Tells: It's Time to Cache for Video Diffusion Modelcited by 245
  38. 2025Inference-Time Scaling for Diffusion Models beyond Scaling Denoising Stepscited by 225

50 most cited of 67 All 67 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 50; 2026: 43.

93 papers.

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  1. 2025Qwen-Image Technical Reportcited by 973
  2. 2025Z-Image: An Efficient Image Generation Foundation Model with Single-Stream Diffusion Transformercited by 247
  3. 2025OmniGen2: Towards Instruction-Aligned Multimodal Generationcited by 241
  4. 2025LongCat-Image Technical Reportcited by 92
  5. 2025Lumina-DiMOO: An Omni Diffusion Large Language Model for Multi-Modal Generation and Understandingcited by 83
  6. 2025TIIF-Bench: How Does Your T2I Model Follow Your Instructions?cited by 64
  7. 2025NextStep-1: Toward Autoregressive Image Generation with Continuous Tokens at Scalecited by 55
  8. 2025From Reflection to Perfection: Scaling Inference-Time Optimization for Text-to-Image Diffusion Models via Reflection Tuningcited by 53
  9. 2025No Other Representation Component Is Needed: Diffusion Transformers Can Provide Representation Guidance by Themselvescited by 50
  10. 2025Distribution Matching Distillation Meets Reinforcement Learningcited by 43
  11. 2025Interleaving Reasoning for Better Text-to-Image Generationcited by 40
  12. 2025Decoupled DMD: CFG Augmentation as the Spear, Distribution Matching as the Shieldcited by 38
38 more
  1. 2025FLUX-Reason-6M & PRISM-Bench: A Million-Scale Text-to-Image Reasoning Dataset and Comprehensive Benchmarkcited by 37
  2. 2025Lumina-mGPT 2.0: Stand-Alone AutoRegressive Image Modelingcited by 35
  3. 2025R2I-Bench: Benchmarking Reasoning-Driven Text-to-Image Generationcited by 31
  4. 2026Reward Hacking in the Era of Large Models: Mechanisms, Emergent Misalignment, Challengescited by 30
  5. 2026Gen-Searcher: Reinforcing Agentic Search for Image Generationcited by 25
  6. 2025OmniCaptioner: One Captioner to Rule Them Allcited by 19
  7. 2026D-OPSD: On-Policy Self-Distillation for Continuously Tuning Step-Distilled Diffusion Modelscited by 17
  8. 2025MMIG-Bench: Towards Comprehensive and Explainable Evaluation of Multi-Modal Image Generation Modelscited by 16
  9. 2025Factuality Matters: When Image Generation and Editing Meet Structured Visualscited by 16
  10. 2025Query-Kontext: An Unified Multimodal Model for Image Generation and Editingcited by 15
  11. 2026GenEvolve: Self-Evolving Image Generation Agents via Tool-Orchestrated Visual Experience Distillationcited by 15
  12. 2025Rectified-CFG++ for Flow Based Modelscited by 14
  13. 2025ImAgent: A Unified Multimodal Agent Framework for Test-Time Scalable Image Generationcited by 14
  14. 2026LLaDA2.0-Uni: Unifying Multimodal Understanding and Generation with Diffusion Large Language Modelcited by 12
  15. 2025ForgetMe: Evaluating Selective Forgetting in Generative Modelscited by 11
  16. 2025TBAC-UniImage: Unified Understanding and Generation by Ladder-Side Diffusion Tuningcited by 10
  17. 2026GenClaw: Code-Driven Agentic Image Generationcited by 10
  18. 2025Resurrect Mask AutoRegressive Modeling for Efficient and Scalable Image Generationcited by 9
  19. 2025MammothModa2: A Unified AR-Diffusion Framework for Multimodal Understanding and Generationcited by 9
  20. 2025UniGen-1.5: Enhancing Image Generation and Editing through Reward Unification in Reinforcement Learningcited by 8
  21. 2026SRA 2: Variational Autoencoder Self-Representation Alignment for Efficient Diffusion Trainingcited by 8
  22. 2026Better Source, Better Flow: Learning Condition-Dependent Source Distribution for Flow Matchingcited by 8
  23. 2025UniFusion: Vision-Language Model as Unified Encoder in Image Generationcited by 7
  24. 2025Latent Wavelet Diffusion For Ultra-High-Resolution Image Synthesiscited by 6
  25. 2025TR-PTS: Task-Relevant Parameter and Token Selection for Efficient Tuningcited by 6
  26. 2025iMontage: Unified, Versatile, Highly Dynamic Many-to-many Image Generationcited by 6
  27. 2025STAR: STacked AutoRegressive Scheme for Unified Multimodal Learningcited by 6
  28. 2026When Pretty Isn't Useful: Investigating Why Modern Text-to-Image Models Fail as Reliable Training Data Generatorscited by 6
  29. 2025DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?cited by 5
  30. 2025Beyond Words and Pixels: A Benchmark for Implicit World Knowledge Reasoning in Generative Modelscited by 4
  31. 2025VDOT: Efficient Unified Video Creation via Optimal Transport Distillationcited by 4
  32. 2026BitDance: Scaling Autoregressive Generative Models with Binary Tokenscited by 4
  33. 2026AccelAes: Accelerating Diffusion Transformers for Training-Free Aesthetic-Enhanced Image Generationcited by 4
  34. 2026MeanFlowNFT: Bringing Forward-Process RL to Average-Velocity Generatorscited by 4
  35. 2025Multimodal Benchmarking and Recommendation of Text-to-Image Generation Modelscited by 3
  36. 2026Generative Refinement Networks for Visual Synthesiscited by 3
  37. 2026Mean Flow Distillation: Robust and Stable Distillation for Flow Matching Modelscited by 3
  38. 2026An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Modelscited by 3

50 most cited of 93 All 93 (JSON)

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