Today, we will look at an AI model that simplifies image editing: Qwen-Image-Edit. This model builds on the foundation of the original, high-performance Qwen-Image, and brings amazing capabilities in the areas of text rendering and precise image editing.
Qwen-Image-Edit’s capabilities and benefits in brief:
- This model stands out for its ability to precisely edit texts within images, both in a bilingual (Chinese and English) environment. This includes directly adding, deleting, and modifying text while preserving the original text size, font, and style. For example, it can make corrections in calligraphy or modify even the smallest text elements on posters.
- It allows you to modify the content of the image while maintaining the original visual semantics and consistency. This includes creating IP (intellectual property) content (e.g., modifying a mascot to have different personalities), rotating objects (even 90 or 180 degrees to see the back), and style transformation (e.g., transforming a portrait into a Studio Ghibli style).
- Precision Detail Editing: This feature focuses on leaving certain regions of the image completely unchanged while adding, removing, or modifying specific elements. Examples include adding a sign and generating an associated reflection, removing small objects or hair, changing the color of a specific font, or modifying a person's clothing and background.
- Step-by-step editing (chained approach): Qwen-Image-Edit allows users to progressively correct errors in images, such as calligraphy. This means that bounding boxes can be used to mark areas to be corrected and modifications can be made iteratively until the desired result is achieved.
What makes it better than others?
- It not only generates or edits images, but also understands them, making it a comprehensive base model for intelligent visual creation and manipulation, where language, layout and images converge.
- Open source ecosystem. The model is natively supported in ComfyUI and is also available on the HuggingFace and ModelScope platforms, making it widely accessible to developers and users. Optimizations such as low GPU memory requirements, FP8 quantization and acceleration methods further increase its accessibility and efficiency.
Links
Blog: https://qwenlm.github.io/blog/qwen-image-edit/GitHub: https://github.com/QwenLM/Qwen-ImageSystem prompt: https://huggingface.co/spaces/Qwen/Qwen-Image-Edit/blob/main/app.pyHugging Face: https://huggingface.co/Qwen/Qwen-Image-EditHF Demo: https://huggingface.co/spaces/Qwen/Qwen-Image-EditQwen Chat: https://chat.qwen.ai/Qwen-Image-Edit ComfyUI Native Support: https://blog.comfy.org/p/qwen-image-edit-comfyui-supportQwen-Image-Edit ComfyUI Native Workflow Example: https://docs.comfy.org/tutorials/image/qwen/qwen-image-editLenovo UltraReal: https://civitai.com/models/1662740/lenovo-ultrareal?modelVersionId=2106185Realism: https://huggingface.co/flymy-ai/qwen-image-realism-lora