Qwen Image Edit AI
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Sample Image
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What is Qwen Image Edit?
Discover Qwen Image Edit: a cutting-edge AI image editing model built on the 20B Qwen-Image foundation. With MMDiT architecture, it enables precise bilingual text editing (Chinese & English), supports both semantic- and appearance-level editing, and preserves original style with high fidelity. Try Qwen Image Edit for free on Seedance AI.
How to Use Qwen Image Edit
Get started with Qwen Image Edit in a few simple steps:
Upload Your Image
Import your image (JPEG/PNG/WEBP) from local files or URLs. Low‑resolution photos, product shots, and screenshots are all supported.
Select Resolution and Controls
Choose upscale output to 2K, 4K, or 8K. Adjust the creativity level to balance realism and stylized enhancement while preserving texture and structure.
Process and Download
Run fast, optimized inference to generate high‑quality results. Export your upscaled image in JPEG, PNG, or WEBP.
Qwen Image Edit FAQ
Everything you need to know about Qwen Image Edit, the advanced AI image editing tool.
What is Qwen Image Edit?
Qwen Image Edit is an advanced image editing model built on the 20B Qwen-Image foundation. It supports precise bilingual text editing and both semantic- and appearance-level modifications, delivering high-fidelity, style-consistent results.
What are its key features?
Bilingual (Chinese & English) text editing with font preservation, semantic-level edits (e.g., object rotation, style transfer), appearance-level edits (e.g., color, texture changes), and high-fidelity outputs.
How can I control the editing results?
You can adjust steps for detail, guidance_scale for prompt adherence, and seed for reproducibility. Negative prompts can prevent unwanted artifacts or quality loss.
What are common use cases?
Ideal for UI/UX mockups, product packaging localization, brand asset translation, e-commerce listings, and creative marketing content editing.
Why is Qwen Image Edit unique?
It inherits Qwen-Image’s strengths in complex text rendering (especially bilingual and paragraph-level), combines semantic consistency with pixel-level fidelity, and leverages dual-encoder architecture (Qwen2.5-VL + VAE) for state-of-the-art editing performance.