Qwen-Image

What is Qwen-Image?

Qwen-Image is a 20 billion parameter open source on August 5, 2025 by Ali Tongyi Thousand Questions teamImage GenerationThe basic model, with MMDiT architecture, is designed for complex text rendering and high-precision image generation. Its core advantage lies in the high-fidelity text rendering of Chinese and English multi-line paragraph level, which can accurately generate complex typography in posters, PPTs and other scenarios, with Chinese rendering capability significantly ahead of the existing models; meanwhile, it supports multi-style general image generation such as photo-realistic, animation, minimalist design, etc., and has consistent image editing capabilities such as style migration, additions, deletions, and detail enhancements, which can maintain the content consistency after multiple rounds of modifications. It has achieved SOTA performance in 12 benchmark tests such as GenEval, DPG, LongText-Bench, etc., and quickly topped the Hugging Face global hot list after open source, providing designers, developers and content creators with zero-threshold professional-grade image generation and editing tools.

Qwen-Image's Core Functions

• Excellent text rendering
  • Native generation of multi-line, mixed Chinese and English text with precise typography (not simple overlays), especially in Chinese rendering (fonts, layouts, paragraphs).
• Precise image generation and editing
  • Supports multiple styles of creation (realistic, anime, minimalist, etc.), strong prompt compliance;
  • High-quality image editing capabilities: style migration, insertion/removal of objects, posing, text editing, detail enhancement, and other operations are customizable and semantically consistent.
• Multitasking and Comprehension
  • Ability to perform image understanding tasks such as object detection, semantic segmentation, depth estimation, super-resolution, multi-view synthesis, etc..
• Advanced Training Architecture
  • Using curriculum learning, training is carried out first from no-text generation and gradually transitioned to complex paragraph rendering;
  • Introduced dual-encoding mechanism: encoded by Qwen2.5-VL and VAE respectively to maintain semantic consistency and detail reduction balance..

Scenarios for using Qwen-Image

• Multilingual marketing and advertising design: Posters, branding graphics, support for mixed Chinese and English, suitable for e-commerce and cross-border marketing content;
• Presentation Documents and Teaching Charts: Generate slide images with captions, explanatory text, and flow layouts;
• Education and Publication Typesetting: Output courseware, handwritten text posters, illustrative charts, etc;
• Product Display Scene Diagram: Labels, signs, and descriptive text in e-commerce scene graphs are clear and readable;
• Image content editing: Modifying text in images, replacing scene elements, and adjusting character poses are all more natural.

How to use Qwen-Image?

1. Basic generation
   • Enter the prompt: Clearly describe the scene, style, and text content (e.g., "Generate a sci-fi movie poster with the title 'GALAXY INVASION', metallic font with neon light effects, and a space explosion in the background").
   • Adjustment parameters: Optimize output quality with parameters such as resolution, number of sampling steps, etc. (e.g., incrementally increase resolution to 1328p to enhance detail).
2. text editor
   • Modify image text directly: Select the text area in edit mode, enter new content and adjust fonts and colors.
   • Multi-language support: Switch between Chinese and English input, and the model automatically adapts to layout rules (e.g., Chinese vertical, English horizontal).
3. Style Migration and Detail Enhancement
   • style migration: Uploading reference images, the model extracts stylistic features and applies them to the generated content (e.g., migrating the style of Van Gogh's "Night of the Stars and Moon" to a city night scene).
   • Detail Enhancement: Local optimization for specific areas (e.g., character faces, object textures) to enhance realism.
4. chain editor
   • many rounds of revision: During continuous editing, the model maintains content consistency through an enhanced multitasking training paradigm (e.g., after adjusting a character's pose, the background text is automatically adapted to the new composition).

Why do you recommend Qwen-Image?

• Open source, free and commercially availableApache-2.0 license allows deployment and modification without license fees, making it more suitable for enterprise or developer integration..;
• Text Rendering Leadership: Especially excellent in Chinese multi-line typesetting for creating posters, slideshows, and signage-type content;
• Editorial and generative skills go hand in hand: Balancing creative output with precise subsequent modifications, suitable for dynamic iterative design;
• Rich Understanding Mission Support: Provides enhanced image understanding and can be used in multimodal analysis and processing scenarios;
• Easy to get started and highly flexible: Supports quick experimentation with online interfaces and easy local integration into visual workflows such as ComfyUI, Diff_synth, etc..

Qwen-Image project address

• Magic Match Community:https://modelscope.cn/models/Qwen/Qwen-Image
• Hugging Face:https://huggingface.co/Qwen/Qwen-Image
• Technical Report::https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/Qwen_Image.pdf
• GitHub::https://github.com/QwenLM/Qwen-Image
• Official website experience address::https://chat.qwen.ai/c/guest