Qwen/Qwen-Image-Edit

image-to-image model by Qwen

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Model card & documentation

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🖥️ Demo&nbsp&nbsp | &nbsp&nbsp💬 WeChat (微信)&nbsp&nbsp | &nbsp&nbsp🫨 Discord&nbsp&nbsp| &nbsp&nbsp Github&nbsp&nbsp We are excited to introduce Qwen-Image-Edit, the image editing version of Qwen-Image. Built upon our 20B Qwen-Image model, Qwen-Image-Edit successfully extends Qwen-Image’s unique text rendering capabilities to image editing tasks, enabling precise text editing. Furthermore, Qwen-Image-Edit simultaneously feeds the input image into Qwen2.5-VL (for visual semantic control) and the VAE Encoder (for visual appearance control), achieving capabilities in both semantic and appearance editing. To experience the latest model, visit Qwen Chat and select the "Image Editing" feature. Key Features: Install the latest version of diffusers The following contains a code snippet illustrating how to use the model to generate images based on text prompts: One of the highlights of Qwen-Image-Edit lies in its powerful capabilities for semantic and appearance editing. Semantic editing refers to modifying image content while preserving the original visual semantics. To intuitively demonstrate this capability, let's…

Qwen/Qwen-Image-Edit on Hugging Face A short preview, not the full document.

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Model overview

Qwen/Qwen-Image-Edit is a image-to-image model repository published by Qwen on Hugging Face. The source reports the diffusers library.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task: image-to-image
Library: diffusers
Recent downloads (30 days): 136,273
Cumulative likes: 2,566
Hugging Face trending score: 4
Reported safetensors parameters: 20,430,401,088
Architecture: Not reported
License: apache-2.0
Access: Not gated by Hugging Face
Created: 2025-08-17T08:32:01.000Z
Last modified: 2025-08-25T04:41:11.000Z

Compatibility and lineage

Reported languages: en, zh

Base-model lineage was not included.

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

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Model card and usage instructions

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