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Free Image Generation AI Qwen-Image Overview

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In recent years, advances in generative AI have made it possible to create high-quality images for advertising and design in a short time. Among them, Qwen-Image, released by Alibaba, has attracted attention for its ability to accurately incorporate text into images. It is a significant strength that it can handle complex sentences and signage depictions, which were difficult with conventional image generation AI.

This article provides an organized introduction to Qwen-Image’s overview, features, usage methods, pricing, and use cases.

Table of Contents

Qwen-Image Overview

Source: https://github.com/QwenLM/Qwen-Image?tab=readme-ov-file

Qwen-Image is an open-source image generation AI model released by Alibaba in 2025. Developed as a derivative of the text generation AI “Qwen” series, it is characterized by its ability to generate images containing text in a natural layout. Unlike conventional AI, it supports multiple languages centered on English and Chinese, demonstrating its strengths in scenarios requiring text-heavy images such as posters and signage.

Technical Features and Strengths

Qwen-Image’s greatest appeal lies in its ability to handle diverse use cases. Here are its specific strengths.

High-Resolution Generation

It can output high-resolution images, making them directly usable for print materials and online advertising.

  • Standard output of 1024×1024 pixels

  • Supports a wide range of styles from photorealistic to anime and illustration

  • Precise detail expression with quality suitable for direct use in advertising and document creation

These points enable confident use even in scenarios emphasizing design quality.

Text Rendering Performance

It covers areas that were difficult for conventional AI in text embedding into images.

  • Reproduces long sentences and multi-line text without distortion

  • Handles mixed English and Chinese text, with Japanese rendering at a practical level

  • Naturally expresses text-dominant designs like signage and posters

  • Qwen-Image completes the process in one step, whereas previously separate software was needed to add text after image generation

As a result, the efficiency of visual production containing text is significantly improved.

Advanced Image Editing

Not only generation but also the ability to freely edit existing images is noteworthy.

  • Can replace backgrounds and add elements based on input photos

  • Strong at partial modifications such as replacing only text in signage or documents

  • Supports diverse editing including pose changes and style conversion for people

  • The derivative model “Qwen-Image-Edit” achieves even higher precision editing

This makes it easy to modify and improve productions without recreating them from scratch.

Open-Source Advantages

The open-source publication approach and multilingual support are also significant advantages.

  • Open-Source Provision Released under the commercially usable Apache 2.0 license

  • Bilingual Support Optimized for English and Chinese, with Japanese usable in practical range

  • Extensibility Researchers and developers can perform additional training and optimization with their own data

These elements establish a foundation that both developers and users can confidently utilize long-term.

Usage and Pricing

Qwen-Image is provided as open-source and can be used for free from GitHub and Hugging Face. Usage methods are broadly divided into the following two types:

Pricing is basically free, but local execution requires separate costs for cloud GPU usage. The license is Apache 2.0, and commercial use is permitted.

Public Examples and Use Cases

Here we introduce some actual generation videos by Qwen-Image posted on X (formerly Twitter).

[Official] Qwen-Image — Freely Create Posters with Text

Image Relighting with Qwen Edit

Edit Photos Using Natural Language Only

From these examples, we can see that Qwen-Image can be utilized for diverse creative work from poster creation to detailed image editing, using natural language alone.

Pre-Implementation Checklist

When considering the implementation of Qwen-Image, it is important to verify the following points in advance and operate with an understanding of the risks.

  • Environment Requirements Verification High-precision generation requires high-performance hardware such as GPUs.

  • Legal Risk Understanding Copyright and rights for generated images are not guaranteed, requiring special attention for commercial use.

  • Quality Control Human verification in combination is recommended for practical use.

  • Understanding Generation Restrictions Some content, such as extreme content, may not be generated.

  • Non-Disclosure of Training Data Since training data details are not disclosed, copyright concerns may remain in generation results.

With these in mind, it is safe to establish appropriate operational rules for implementation.

Conclusion

Qwen-Image stands apart from other image generation AI in its ability to accurately handle text. It is free to use and can be broadly applied from advertising and documents to creative production. While implementation requires verification of legal risks and operational environments, appropriate use can significantly expand possibilities for operational efficiency and new expressions. As this is a field expected to continue evolving, there is value in trying it early.