Model reference · open weights

Qwen-Image-Ed

Qwen-Image-Ed is an open-weight image model from Qwen, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.

Image Qwen 1 variants 122k downloads/mo
Request this model on EU hardware All served models Not on the shared API today — deployed on request.

About

What Qwen-Image-Ed is

💜 <a href="https://chat.qwen.ai/"<bQwen Chat</b</a&nbsp&nbsp | &nbsp&nbsp🤗 <a href="https://huggingface.co/Qwen/Qwen-Image-Edit"Hugging Face</a&nbsp&nbsp | &nbsp&nbsp🤖 <a href="https://modelscope.cn/models/Qwen/Qwen-Image-Edit"ModelScope</a&nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-Image/QwenImage.pdf"Tech Report</a &nbsp&nbsp | &nbsp&nbsp 📑 <a href="https://qwenlm.github.io/blog/qwen-image-edit/"Blog</a &nbsp&nbsp 🖥️ <a href="https://huggingface.co/spaces/Qwen/Qwen-Image-Edit"Demo</a&nbsp&nbsp | &nbsp&nbsp💬 <a href="https://github.com/QwenLM/Qwen-Image/blob/main/assets/wechat.png"WeChat (微信)</a&nbsp&nbsp | &nbsp&nbsp🫨 <a href="https://discord.gg/CV4E9rpNSD"Discord</a&nbsp&nbsp| &nbsp&nbsp <a href="https://github.com/QwenLM/Qwen-Image"Github</a&nbsp&nbsp Introduction 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: Semantic and Appearance Editing: Qwen-Image-Edit supports both low-level visual appearance editing (such as adding, removing, or modifying elements, requiring all other regions of the image to remain completely unchanged) and 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 (Chinese and English) text editing, allowing direct addition, deletion, and modification of text in images while preserving the original font, size, and style. Strong Benchmark Performance: Evaluations on multiple public benchmarks demonstrate that Qwen-Image-Edit achieves state-of-the-art (SOTA) performance in image editing tasks,

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

MakerQwen
TypeImage models
Parameters (lead)20.4B
Variants1
Runs withdiffusers
Released2025-08-17
Popularity122k downloads / month
Likes2,495
LicenceOpen weights

How it works

How image models work

Text promptwhat to makeText encoderunderstands itDiffusion stepsdenoise to pixelsImagePNG / JPEGA diffusion model starts from noise and denoises it, guided by your prompt, into a finished image.

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

VariantParamsPrecisionVRAMFits 16 GBWeights
Qwen-Image-Edit20.4BBF16~47 GBWeights ↗

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys qwen-image-ed for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (qwen-image-ed below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/images/generations \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen-image-ed","prompt":"a red bicycle","size":"1024x1024"}'

Details

Languages, data & research

Languages

en zh

Tags

diffusers safetensors image-to-image en zh diffusers:QwenImageEditPipeline

Papers

Licence

Open weights

Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗

Sources

Weights & code

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