# Qwen-Image-2.0 Technical Report

arXiv: https://arxiv.org/abs/2605.10730 · HTML: https://mlsanity.com/paper/2605.10730/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 53 references that are in the map.
- First posted on arXiv 11 May 2026.

## Builds on (most cited first, 30 of 46)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [Scalable Diffusion Models with Transformers](https://mlsanity.com/paper/2212.09748/) (2022)
- [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022)
- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Scaling Rectified Flow Transformers for High-Resolution Image Synthesis](https://mlsanity.com/paper/2403.03206/) (2024)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Qwen3-VL Technical Report](https://mlsanity.com/paper/2511.21631/) (2025)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Consistency Models](https://mlsanity.com/paper/2303.01469/) (2023)
- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [Root Mean Square Layer Normalization](https://mlsanity.com/paper/1910.07467/) (2019)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [MaskGIT: Masked Generative Image Transformer](https://mlsanity.com/paper/2202.04200/) (2022)
- [Qwen-Image Technical Report](https://mlsanity.com/paper/2508.02324/) (2025)
- [Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction](https://mlsanity.com/paper/2404.02905/) (2024)
- [One-step Diffusion with Distribution Matching Distillation](https://mlsanity.com/paper/2311.18828/) (2023)
- [FLUX.1 Kontext: Flow Matching for In-Context Image Generation and Editing in Latent Space](https://mlsanity.com/paper/2506.15742/) (2025)
- [Cosmos World Foundation Model Platform for Physical AI](https://mlsanity.com/paper/2501.03575/) (2025)
- [Adversarial Diffusion Distillation](https://mlsanity.com/paper/2311.17042/) (2023)
- [SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers](https://mlsanity.com/paper/2401.08740/) (2024)
- [Improved Distribution Matching Distillation for Fast Image Synthesis](https://mlsanity.com/paper/2405.14867/) (2024)
- [Flow-GRPO: Training Flow Matching Models via Online RL](https://mlsanity.com/paper/2505.05470/) (2025)
- [Mean Flows for One-step Generative Modeling](https://mlsanity.com/paper/2505.13447/) (2025)
- [Reconstruction vs. Generation: Taming Optimization Dilemma in Latent Diffusion Models](https://mlsanity.com/paper/2501.01423/) (2025)
- [Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis](https://mlsanity.com/paper/2412.04431/) (2024)

## Built on by (most cited first, 30 of 36)

- [Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization](https://mlsanity.com/paper/2608.26103/) (2026)
- [Boogu-Image-0.1: Boosting Open Agentic Multimodal Generation via Understanding under a Minimal Budget](https://mlsanity.com/paper/2607.13125/) (2026)
- [An Empirical Study of Training Pixel-Space Text-to-Image Diffusion Models](https://mlsanity.com/paper/2608.16887/) (2026)
- [VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning](https://mlsanity.com/paper/2608.26105/) (2026)
- [Qwen-Image-2.0-RL Technical Report](https://mlsanity.com/paper/2606.27608/) (2026)
- [ChartSync: A Benchmark for Visuo-Logical Cascading Chart Editing](https://mlsanity.com/paper/2607.10301/) (2026)
- [CPI-Bench: A Comprehensive, Practical and Intelligent Benchmark for Real-World Image Editing](https://mlsanity.com/paper/2608.14546/) (2026)
- [LLaDA-Image: Building Strong Image Generators with Fully Open Training Recipes](https://mlsanity.com/paper/2609.03796/) (2026)
- [TYPO: Instruction-Dense Visual Jailbreaks against Commercial Closed-Source Image-Generation Models](https://mlsanity.com/paper/2607.24897/) (2026)
- [From Corpora to Co-Evolving Capabilities: Capability-Centric Data Design for Generalist Image Generation](https://mlsanity.com/paper/2608.18076/) (2026)
- [Exploring the Performance Frontier of Compact Unified Image Generation Models](https://mlsanity.com/paper/2608.20334/) (2026)
- [Figures as Programs: Recursive Generation of Editable Scientific Figures](https://mlsanity.com/paper/2609.01006/) (2026)
- [UniVVT: A Unified End-to-End Framework for High-Fidelity Video Virtual Try-on](https://mlsanity.com/paper/2608.05745/) (2026)
- [InsertFuse: A Unified Framework for Multi-Category Reference-Guided Image Insertion](https://mlsanity.com/paper/2608.06490/) (2026)
- [Qwen-Image-Flash: Rethinking the Training Recipe for Few-Step Distillation](https://mlsanity.com/paper/2606.03746/) (2026)
- [H-Adapter: Pose-Robust Hairstyle Transfer via Attention-Derived, Source-Aligned Hair Masks](https://mlsanity.com/paper/2606.25578/) (2026)
- [Where did the ambiguity go? Examining how multimodal models interpret polysemous words](https://mlsanity.com/paper/2608.00410/) (2026)
- [Can Text-to-Image Models Draw from the Right Frame of Reference?](https://mlsanity.com/paper/2608.03357/) (2026)
- [Nexus: Structured Synergy for Efficient Text-to-Image Generation using Rectified Flow Model](https://mlsanity.com/paper/2608.16104/) (2026)
- [FUSED: Forensic-Semantic Mixture-of-Experts for AI Inpainting Detection and Localization](https://mlsanity.com/paper/2608.28302/) (2026)
- [KnowVis: Knowledge-Centric Visual Summarization for Video Lectures](https://mlsanity.com/paper/2609.03742/) (2026)
- [Puffin-World: Scaling a Unified Multimodal Model with Native 3D World States](https://mlsanity.com/paper/2609.04196/) (2026)
- [TrafficImag: A Benchmark for Counterfactual Roadside Traffic Video Generation](https://mlsanity.com/paper/2609.30722/) (2026)
- [Elucidating the Design Space of Regression-based Diffusion Reinforcement Learning](https://mlsanity.com/paper/2609.33444/) (2026)
- [IVT-Guard: All-in-One Reasoning Model for AI-Generated Content Detection](https://mlsanity.com/paper/2609.33585/) (2026)
- [SolveEdit: Benchmarking Visual Problem Solving in Generative Models](https://mlsanity.com/paper/2609.35504/) (2026)
- [PE-OPSD: Internalizing Prompt Enhancement into Flow-matching Models via On-Policy Self-Distillation](https://mlsanity.com/paper/2609.36638/) (2026)
- [Visualizing Distribution Coverage in Generative Diffusion Models](https://mlsanity.com/paper/2609.38853/) (2026)
- [DEPICT: Scoring Text-to-Image Alignment by Answer Agreement](https://mlsanity.com/paper/2610.03617/) (2026)
- [Learning to Read the Contextual Tokens in Diffusion Transformers](https://mlsanity.com/paper/2610.06844/) (2026)

## Abstract (arXiv)

We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite recent progress, existing models still struggle with ultra-long text rendering, multilingual typography, high-resolution photorealism, robust instruction following, and efficient deployment, especially in text-rich and compositionally complex scenarios. Qwen-Image-2.0 addresses these challenges by coupling Qwen3-VL as the condition encoder with a Multimodal Diffusion Transformer for joint condition-target modeling, supported by large-scale data curation and a customized multi-stage training pipeline. This enables strong multimodal understanding while preserving flexible generation and editing capabilities. The model supports instructions of up to 1K tokens for generating text-rich content such as slides, posters, infographics, and comics, while significantly improving multilingual text fidelity and typography. It also enhances photorealistic generation with richer details, more realistic textures, and coherent lighting, and follows complex prompts more reliably across diverse styles. Extensive human evaluations show that Qwen-Image-2.0 substantially outperforms previous Qwen-Image models in both generation and editing, marking a step toward more general, reliable, and practical image generation foundation models.