Paper page - Qwen-Image-2.0 Technical Report

Source: https://huggingface.co/papers/2605.10730 Published on May 11

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Abstract

Qwen-Image-2.0 is an advanced image generation model that combines high-fidelity synthesis with precise editing capabilities through a unified framework using Qwen3-VL as condition encoder and Multimodal Diffusion Transformer for joint modeling.

We present Qwen-Image-2.0, an omni-capableimage generation foundation modelthat unifies high-fidelity generation and preciseimage editingwithin a single framework. Despite recent progress, existing models still struggle with ultra-long text rendering, multilingual typography, high-resolution photorealism, robustinstruction following, and efficient deployment, especially in text-rich and compositionally complex scenarios. Qwen-Image-2.0 addresses these challenges by coupling Qwen3-VL as thecondition encoderwith aMultimodal Diffusion Transformerforjoint condition-target modeling, supported bylarge-scale data curationand a customizedmulti-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 generatingtext-rich contentsuch as slides, posters, infographics, and comics, while significantly improving multilingual text fidelity and typography. It also enhancesphotorealistic generationwith 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 practicalimage generation foundation models.

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