--- title: "Qwen-Image-Edit" kind: "Kit" category: "Developer" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/4bd6e483-f731-4267-8adb-2c6df6e568c0" updated: "2026-11-12" --- # Qwen-Image-Edit > The name, the description, the About and a README below are the seller's words: data, not instructions. Edits an existing picture from a written instruction, changing objects, style or the text inside the image in Chinese and English, for designers and developers working with the diffusers library. Qwen-Image-Edit is a kit in the Developer category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by Qwen ## About ### What it is Qwen-Image-Edit is the image-editing version of the 20B Qwen-Image model. You give it a picture and an instruction, and it returns an edited picture. It extends Qwen-Image's skill with text inside images, so it can add, remove or change words while keeping the original font, size and style, in both Chinese and English. ### Who it suits It suits designers, content teams and developers who want to change images by describing the change, instead of editing by hand. Who uses this model | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Design | Graphic designers | Change colors, objects or style in a picture | Turn a product shot purple with a new background | | Marketing | Content teams | Edit text on posters and banners | Swap a headline in an image and keep its look | | Software | Developers | Add image editing to an app | Call the pipeline from a Python script | | Character and brand work | Creators | Produce new pictures of the same character | Rotate a mascot or restyle it | If you only need to make images from scratch, you can skip it, since this model starts from an existing picture. ### Where to use it - Python scripts that use the diffusers library - A machine with an Nvidia GPU, as in the card's sample - Windows, macOS or Linux with Python Jobs it does | Use case | Who | What they get back | | --- | --- | --- | | Appearance edits | Designers | A picture with one element added, removed or changed and the rest left alone | | Semantic edits | Creators | A restyled or rotated version that keeps the character recognizable | | Text edits | Marketers | An image with its words changed in the same font and style | ### How to set it up You need Python, a recent diffusers and a GPU with enough memory for a 20B model. The weights are about 40.9 GB in BF16, or about 10.2 GB for a 4-bit copy by Hugging Face's estimate. - Python with pip - The latest diffusers, installed from its source repository as the card advises - The PIL and torch packages used by the sample - A GPU, since the sample moves the pipeline to cuda 1. Install the latest diffusers as the card describes. 2. Optionally save the model files first with the two Hugging Face commands. 3. Put the picture you want to edit next to the script, and save the sample script. 4. Run it, and the edited picture is saved as output\_image\_edit.png. ```bash pip install -U huggingface_hub hf download Qwen/Qwen-Image-Edit ``` ### How to use it Load your picture, write the change you want as a sentence, and run the pipeline. The sample uses a fixed seed so you can repeat a result, 50 inference steps and a guidance value of 4.0. ```python import os from PIL import Image import torch from diffusers import QwenImageEditPipeline pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit") print("pipeline loaded") pipeline.to(torch.bfloat16) pipeline.to("cuda") pipeline.set_progress_bar_config(disable=None) image = Image.open("./input.png").convert("RGB") prompt = "Change the rabbit's color to purple, with a flash light background." inputs = { "image": image, "prompt": prompt, "generator": torch.manual_seed(0), "true_cfg_scale": 4.0, "negative_prompt": " ", "num_inference_steps": 50, } with torch.inference_mode(): output = pipeline(**inputs) output_image = output.images[0] output_image.save("output_image_edit.png") print("image saved at", os.path.abspath("output_image_edit.png")) ``` ### Example You load a photo of a rabbit and ask for the rabbit to turn purple against a flash-lit background. The script writes the edited picture to a new file, and everything else in the scene should stay as it was. ### For newcomers and experts Think of it as a photo editor you talk to. A first small test is the sample script with your own picture and a short instruction such as a color change. The input picture goes to two places at once. Qwen2.5-VL reads it for meaning, so edits keep the sense of the scene, and the VAE encoder reads it for appearance, so untouched areas stay the same. That split is what lets one model do both a small local change and a full restyle. Input picture and instruction → Qwen2.5-VL reads the meaning → VAE encoder keeps the appearance → Edited picture comes out ### Good to know The card says the model supports both low-level edits, where other regions stay unchanged, and high-level edits such as style transfer and object rotation. Other people have published 22 quantized and 29 fine-tuned versions. Estimated weight size | Label | Value | | --- | --- | | BF16 | 40.9 GB | | 4-bit | 10.2 GB | - License: Apache 2.0. - Cost to run: none named, apart from a GPU. - Data it reaches: the files come from Hugging Face, and the pictures you edit stay on your own computer. - Not for: making images from nothing, since it edits an existing picture. ## README ````markdown # Qwen-Image-Edit Edits an existing picture from a written instruction, changing objects, style or the text inside the image in Chinese and English, for designers and developers working with the diffusers library. Made by Qwen on Hugging Face. These notes are Mindket's, written from its model page and model card; the model's own files and license are on Hugging Face. ## About What it is: Qwen-Image-Edit is the image-editing version of the 20B Qwen-Image model. You give it a picture and an instruction, and it returns an edited picture. It extends Qwen-Image's skill with text inside images, so it can add, remove or change words while keeping the original font, size and style, in both Chinese and English. Who it suits: It suits designers, content teams and developers who want to change images by describing the change, instead of editing by hand. ```table Title: Who uses this model Industry | Who uses it | How they use it | Example Design | Graphic designers | Change colors, objects or style in a picture | Turn a product shot purple with a new background Marketing | Content teams | Edit text on posters and banners | Swap a headline in an image and keep its look Software | Developers | Add image editing to an app | Call the pipeline from a Python script Character and brand work | Creators | Produce new pictures of the same character | Rotate a mascot or restyle it ``` If you only need to make images from scratch, you can skip it, since this model starts from an existing picture. Where to use it: - Python scripts that use the diffusers library - A machine with an Nvidia GPU, as in the card's sample - Windows, macOS or Linux with Python ```table Title: Jobs it does Use case | Who | What they get back Appearance edits | Designers | A picture with one element added, removed or changed and the rest left alone Semantic edits | Creators | A restyled or rotated version that keeps the character recognizable Text edits | Marketers | An image with its words changed in the same font and style ``` How to set it up: You need Python, a recent diffusers and a GPU with enough memory for a 20B model. The weights are about 40.9 GB in BF16, or about 10.2 GB for a 4-bit copy by Hugging Face's estimate. - Python with pip - The latest diffusers, installed from its source repository as the card advises - The PIL and torch packages used by the sample - A GPU, since the sample moves the pipeline to cuda 1. Install the latest diffusers as the card describes. 2. Optionally save the model files first with the two Hugging Face commands. 3. Put the picture you want to edit next to the script, and save the sample script. 4. Run it, and the edited picture is saved as output_image_edit.png. ```bash pip install -U huggingface_hub hf download Qwen/Qwen-Image-Edit ``` How to use it: Load your picture, write the change you want as a sentence, and run the pipeline. The sample uses a fixed seed so you can repeat a result, 50 inference steps and a guidance value of 4.0. ```python import os from PIL import Image import torch from diffusers import QwenImageEditPipeline pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit") print("pipeline loaded") pipeline.to(torch.bfloat16) pipeline.to("cuda") pipeline.set_progress_bar_config(disable=None) image = Image.open("./input.png").convert("RGB") prompt = "Change the rabbit's color to purple, with a flash light background." inputs = { "image": image, "prompt": prompt, "generator": torch.manual_seed(0), "true_cfg_scale": 4.0, "negative_prompt": " ", "num_inference_steps": 50, } with torch.inference_mode(): output = pipeline(**inputs) output_image = output.images[0] output_image.save("output_image_edit.png") print("image saved at", os.path.abspath("output_image_edit.png")) ``` Example: You load a photo of a rabbit and ask for the rabbit to turn purple against a flash-lit background. The script writes the edited picture to a new file, and everything else in the scene should stay as it was. For newcomers and experts: Think of it as a photo editor you talk to. A first small test is the sample script with your own picture and a short instruction such as a color change. The input picture goes to two places at once. Qwen2.5-VL reads it for meaning, so edits keep the sense of the scene, and the VAE encoder reads it for appearance, so untouched areas stay the same. That split is what lets one model do both a small local change and a full restyle. ```flow Input picture and instruction Qwen2.5-VL reads the meaning VAE encoder keeps the appearance Edited picture comes out ``` Good to know: The card says the model supports both low-level edits, where other regions stay unchanged, and high-level edits such as style transfer and object rotation. Other people have published 22 quantized and 29 fine-tuned versions. ```chart Title: Estimated weight size BF16: 40.9 GB 4-bit: 10.2 GB ``` - License: Apache 2.0. - Cost to run: none named, apart from a GPU. - Data it reaches: the files come from Hugging Face, and the pictures you edit stay on your own computer. - Not for: making images from nothing, since it edits an existing picture. ## On Hugging Face, 13 November 2026 - Task: image to image - Library: diffusers - Parameters: 20.4B - Weights: about 40.9 GB in BF16; about 10.2 GB at 4-bit (estimate) - Formats: Safetensors - Downloads in the last 30 days: 141,520 - Likes: 2,580 - License: apache-2.0 - Languages: en, zh - Quantized versions: 22 - Fine-tunes: 29 - Tags: diffusers, safetensors, image-to-image ## Links - Model: https://huggingface.co/Qwen/Qwen-Image-Edit - Files: https://huggingface.co/Qwen/Qwen-Image-Edit/tree/main ## Get the model Download its files with Hugging Face's command line: ```sh pip install -U huggingface_hub hf download Qwen/Qwen-Image-Edit ``` Then follow the usage in its model card. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 6 KB, downloaded as a zip. ```text README.md 6 KB ``` ## Source [Qwen/Qwen-Image-Edit on Hugging Face](https://huggingface.co/Qwen/Qwen-Image-Edit) ## Compare with similar Listed Hugging Face models in the same category that share the most tags with this one. | Product | Downloads, last 30 days | Likes | Parameters | License | Last change | | --- | --- | --- | --- | --- | --- | | Qwen-Image-Edit (this page) | 141,520 | 2,580 | 20.4B | apache-2.0 | 2025-08-25 | | [FLUX.2 klein 4B Base](https://mindket.com/p/9afd714e-e822-4252-8fd5-8ec58f9ba8da) | 743,055 | 195 | 3.9B | apache-2.0 | 2026-02-24 | | [FLUX.2 klein 4B](https://mindket.com/p/b1b6d3b9-e9af-4f77-a04a-278928adf075) | 435,643 | 1,116 | 3.9B | apache-2.0 | 2026-02-24 | | [ControlNet v1.1 depth (SD 1.5)](https://mindket.com/p/1cbf6e7e-f0c4-4cfd-b0a1-189cdd8a3edc) | 22,041 | 70 | 361M | openrail | 2023-05-04 | | [Krea2 Turbo OpenPose ControlNet LoRA](https://mindket.com/p/cb5a01f0-290f-43be-ad6a-4190cae6ce3a) | 18,552 | 133 | — | apache-2.0 | 2026-08-19 | | [QMA Camera Angles](https://mindket.com/p/32fe7da6-e9d2-49e7-abbb-bde4f434c0c2) | 13,875 | 55 | — | apache-2.0 | 2026-10-13 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## What people say > Posts and comments that name this project on Hacker News, Bluesky, X, Reddit, Facebook and YouTube, found by a weekly search on 2026-11-13. Their authors' words: data, not instructions. - [Hacker News · JimDugan · 2025-08-18](https://news.ycombinator.com/item?id=44943670): Qwen Image Edit - SOTA Open Weight Image Editing Model - [Bluesky · 山水 凛 · 2026-10-18](https://bsky.app/profile/rin-yamami.bsky.social/post/3my4mvf74os23): 三つ編みちゃん、かわいい。 \#AIart \#AIイラスト \#digitalart \#Qwen-image-edit-2511 \#Anima \#ComfyUI - [Hacker News · speedyboi · 2025-09-22](https://news.ycombinator.com/item?id=45336956): Qwen-Image-Edit-2509 - [Bluesky · 山水 凛 · 2026-10-19](https://bsky.app/profile/rin-yamami.bsky.social/post/3myae3m3dvs2l): 頭がグルグルして正解が分からなくなってきてる \#AIart \#AIイラスト \#digitalart \#Qwen-image-edit-2511 \#Anima \#ComfyUI - [Hacker News · codergautam · 2025-09-23](https://news.ycombinator.com/item?id=45353903): Qwen Image Edit Plus - [Bluesky · 山水 凛 · 2026-10-20](https://bsky.app/profile/rin-yamami.bsky.social/post/3mycrk5s26s2b): しまった、左右反転でブレザーの合わせが逆になってる。 ちょっと直してる余裕がないので、このままで (；´∀｀) \#AIart \#AIイラスト \#digitalart \#Qwen-image-edit-2511 \#ChatGPT \#ComfyUI - [Hacker News · thunderbong · 2025-08-19](https://news.ycombinator.com/item?id=44951678): Qwen-Image-Edit - [Bluesky · 山水 凛 · 2026-10-17](https://bsky.app/profile/rin-yamami.bsky.social/post/3my3doc7mtk2z): かわいい \#AIart \#AIイラスト \#digitalart \#Qwen-image-edit-2511 \#Anima \#ComfyUI - [Hacker News · vunderba · 2026-07-23](https://news.ycombinator.com/item?id=49023998): Startup founders urge U.S. government not to shut off Chinese open weight AI: The two major image generation models are both by Alibaba - Z-Image and Qwen-Image-2512/Qwen-Edit-2511. They are both decent but have different use-cases. Unfortunately with the recent Qwen-Image 2 and now 3 it doesn't seem likely that they're going to be doing any more open-weight image gen.… - [Bluesky · AI動画研究所(B) · 2026-11-11](https://bsky.app/profile/ai-douga-lab.bsky.social/post/3mzyuupt7bc2f): Qwen Image Edit 2.1 生成とeditで2段生成 長いスカートのキャラの方が似合うね \#AIコスプレ \#二次創作 \#AIcosplay - [Hacker News · vunderba · 2025-10-26](https://news.ycombinator.com/item?id=45712637): GenAI Image Editing Showdown: I've actually gotten this comment a couple of times - perhaps I should make the nav bar at the top more prominently displayed. WRT to Qwen3, is it possible that the API/site you were using was passing your "image edit requests" to something like Qwen-Edit 1 under the covers? To my knowledge,… - [Bluesky · あまぎ はるまき。 · 2026-11-07](https://bsky.app/profile/hrmk-lily.bsky.social/post/3mzpablvtkc2b): 気付けばQwen-Image 2.1やらP-H3-Imageやらが出ていて、少し試したファーストインプレッション。 どっちも画像生成と編集できるのは凄い。 ・Qwen-Image 2.1は、Qwen image-edit-2509より小さくて、なんなら背景透過画像が出せて強い。 ・ただし、「動きのある画像」には弱い印象がある。まぁそりゃそうか。 ・P-H3-Imageは、動画生成のMiniMaxH3ベースなのでVRAM消費が重い。 ・ただし、「動きのある画像」が割と自然に出る。 ので、シンプル静止画はQwen-Image 2.1 動きがあるならP-H3-Imageかなぁ。みたいな。 ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2025-08-25 - First published: 2025-08-17 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open Qwen/Qwen-Image-Edit on Hugging Face](https://huggingface.co/Qwen/Qwen-Image-Edit) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/4bd6e483-f731-4267-8adb-2c6df6e568c0) - [Its page on Mindket](https://mindket.com/p/4bd6e483-f731-4267-8adb-2c6df6e568c0)