Qwen-Image

Product Introduction

1. Qwen-Image is a 20B-parameter open-source image foundation model developed by the Qwen team, designed for high-fidelity image generation and precise editing with a focus on complex text rendering.
2. The core value of Qwen-Image lies in its ability to bridge advanced text rendering capabilities—particularly for logographic languages like Chinese—with robust general image generation and editing performance, enabling professional-grade visual content creation.

Main Features

1. Qwen-Image achieves state-of-the-art text rendering accuracy, supporting multi-line layouts, paragraph-level semantics, and fine-grained details for both alphabetic (e.g., English) and logographic (e.g., Chinese) languages, as demonstrated in scenarios like posters, PPTs, and bilingual signage.
2. The model delivers consistent image editing through an enhanced multi-task training paradigm, preserving semantic coherence and visual realism during operations such as style transfer, object addition/removal, and text modification.
3. Qwen-Image exhibits strong cross-benchmark performance, outperforming existing models on public benchmarks including GenEval, DPG, and GEdit for general generation and editing tasks, validated by metrics like LongText-Bench and TextCraft.

Problems Solved

1. Qwen-Image addresses the challenge of generating images with accurate, contextually embedded text—especially in complex layouts or bilingual scenarios—where traditional models often fail to maintain legibility or stylistic consistency.
2. The model targets professional users such as graphic designers, marketers, and content creators who require precise text integration and editing in visual assets like advertisements, presentations, and branded materials.
3. Typical use cases include generating marketing collateral with multilingual text, editing product images while preserving brand elements, and creating detailed infographics or slides with automated layout optimization.

Unique Advantages

1. Unlike most image models that prioritize English text, Qwen-Image natively supports high-fidelity Chinese text rendering, including calligraphic styles and multi-paragraph layouts, while maintaining competitive English performance.
2. The model integrates a 20B MMDiT architecture optimized for multi-modal tasks, combining text understanding and image generation in a unified framework, which enhances editing precision and semantic alignment.
3. Qwen-Image’s competitive edge stems from its open-source availability, verified performance across 10+ public benchmarks, and ability to handle niche scenarios like ultra-small text (e.g., book covers) and dense bilingual annotations without quality degradation.

Frequently Asked Questions (FAQ)

1. How does Qwen-Image ensure accuracy in Chinese text rendering compared to other models? Qwen-Image employs specialized training on logographic character structures and contextual layout prediction, validated by benchmarks like ChineseWord and TextCraft where it outperforms competitors by over 15% in character recognition accuracy.
2. What types of image editing operations does Qwen-Image support? The model enables text-based edits (e.g., modifying signage), object manipulation (adding/removing elements), style transfers (e.g., converting photos to anime), and detail enhancement while preserving scene coherence through its multi-task training framework.
3. How does Qwen-Image compare to closed-source alternatives like DALL-E or Midjourney? As an open-source model, Qwen-Image provides transparency and customization while achieving comparable or superior performance in text-heavy and editing tasks, as evidenced by its top rankings on GenEval and GEdit benchmarks.