Qwen-Image-2.0 Technical Report

TL;DR

Qwen-Image-2.0 is a versatile image generation model that excels in high-resolution, multilingual, and complex image editing tasks, advancing the state-of-the-art in multimodal understanding and generation.

Contribution

It introduces a unified framework coupling Qwen3-VL with a Multimodal Diffusion Transformer, enabling high-fidelity, multilingual, and detailed image generation and editing.

Findings

Outperforms previous models in generation and editing quality.

Supports instructions up to 1K tokens for complex, text-rich content.

Improves multilingual text fidelity and photorealistic details.

Abstract

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.