Key features of Qwen-Image-Edit include:

• Dual Semantic and Appearance Editing: Qwen-Image-Edit supports not only low-level visual appearance editing (such as element addition, deletion, and modification, requiring other areas of the image to remain completely unchanged), but also high-level visual semantic editing (such as IP creation, object rotation, and style transfer, allowing overall pixel changes while maintaining semantic consistency).
• Precise Text Editing: Qwen-Image-Edit supports bilingual text editing in Chinese and English, allowing direct operations like addition, deletion, and modification of text in images while preserving the original font, size, and style.
• Strong Benchmark Performance: Evaluations across multiple public benchmarks show that Qwen-Image-Edit achieves SOTA performance in image editing tasks, making it a powerful foundation model for image editing.

A major highlight of Qwen-Image-Edit lies in its dual semantic and appearance editing capabilities. Semantic editing refers to modifying image content while preserving the visual semantics of the original image.

ModelScope: https://modelscope.cn/models/Qwen/Qwen-Image-Edit

Hugging Face: https://huggingface.co/Qwen/Qwen-Image-Edit

GitHub: https://github.com/QwenLM/Qwen-Image