Inference Logoinference shell

Qwen-Image-Edit

Advanced image editing that excels at rendering and manipulating text within images, allowing for precise changes to appearance and meaning.

run in browser →run via API
run with your agent
# install belt
$curl -fsSL https://cli.inference.sh | sh
# sign in (or set INFERENCE_API_KEY on a headless machine)
$belt login
# view schema & details
$belt app get infsh/qwen-image-edit
# run
$belt app run infsh/qwen-image-edit --input '{"prompt":"prompt","image":{}}'
maker
infsh
category
image
runs via
api, javascript and python sdk, belt cli, mcp
updated
2025-09-12

api reference

about

advanced image editing that excels at rendering and manipulating text within images, allowing for precise changes to appearance and meaning.

1. calling the api

install the client

the client provides a convenient way to interact with the api.

bash
1pip install inferencesh

setup your api key

set INFERENCE_API_KEY as an environment variable. get your key from settings → api keys.

bash
1export INFERENCE_API_KEY="inf_your_key"

run and get result

submit a request and wait for the final result. best for batch processing or when you don't need progress updates.

python
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567result = client.run({8        "app": "infsh/qwen-image-edit",9        "input": {10            "prompt": "prompt",11            "image": {}12        }13    })1415print(result["output"])

stream live updates

get real-time progress updates as the task runs. ideal for showing progress bars, partial results, or long-running tasks.

python
1import os2from inferencesh import inference34client = inference(api_key=os.environ["INFERENCE_API_KEY"])567# stream=True yields updates as they arrive8for update in client.run({9        "app": "infsh/qwen-image-edit",10        "input": {11            "prompt": "prompt",12            "image": {}13        }14    }, stream=True):15    if update.get("progress"):16        print(f"progress: {update['progress']}%")17    if update.get("output"):18        print(f"output: {update['output']}")

2. authentication

the api uses api keys for authentication. see the authentication docs for detailed setup instructions.

3. files

file inputs are automatically handled by the sdk. you can pass local paths, urls, or base64 data.

automatic upload

the python sdk automatically detects local file paths and uploads them. urls are passed through as-is.

python
1# local file paths are automatically uploaded2result = client.run({3    "app": "infsh/qwen-image-edit",4    "input": {5        "image": "/path/to/local/image.png",  # detected & uploaded6        "audio": "https://example.com/audio.mp3",  # url passed through7    }8})

manual upload

you can also upload files manually and use the returned url.

python
1# upload and get a hosted URL2file = client.files.upload("/path/to/file.png")3print(file.uri)  # https://cloud.inference.sh/...

4. webhooks

get notified when a task completes by providing a webhook url. when the task reaches a terminal state (completed, failed, or cancelled), a POST request is sent to your url with the task result.

python
1result = client.run({2    "app": "infsh/qwen-image-edit",3    "input": {4        "prompt": "prompt",5        "image": {}6    },7    "webhook": "https://your-server.com/webhook"8}, wait=False)

webhook payload

your endpoint receives a JSON POST with the task result:

json
1{2  "event": "task.completed",3  "timestamp": "2024-01-15T10:30:05Z",4  "data": {5    "id": "task_abc123",6    "short_id": "abc123",7    "status": 10,8    "status_text": "completed",9    "output": { ... },10    "created_at": "2024-01-15T10:30:00Z",11    "updated_at": "2024-01-15T10:30:05Z"12  }13}
eventstringtask.completed, task.failed or task.cancelled
timestampstringiso timestamp of the delivery
data.idstringtask id
data.short_idstringshort task id
data.statusnumberterminal status (10=completed, 11=failed, 12=cancelled, 13=rejected)
data.status_textstringthe status as a word: completed, failed or cancelled
data.outputobjecttask output (when completed)
data.errorstringerror message (when failed, omitted otherwise)
data.session_idstringsession id (when the task ran in a session, omitted otherwise)
data.created_atstringiso timestamp
data.updated_atstringiso timestamp

5. schema

input

promptstring*

the text prompt describing the desired edits to apply to the input image. supports both english and chinese text rendering.

imageobject*

input image to edit. this field is required for image editing.

negative_promptstring

the negative prompt to guide what not to include in the image.

widthinteger

the width in pixels of the generated image.

default: 1024
heightinteger

the height in pixels of the generated image.

default: 1024
num_inference_stepsinteger

the number of inference steps for generation quality.

default: 50
true_cfg_scalenumber

the cfg scale for generation guidance.

default: 4
seedinteger

the seed for reproducible generation.

languagestring

language for prompt optimization (english or chinese).

default: "en"
options:"en""zh"
cache_thresholdnumber

first-block cache threshold for transformer (0 disables caching).

default: 0min:0max:1
use_unipcm_flow_matchingboolean

if true, switch scheduler to unipcm flow matching configuration.

default: false
lorasarray

list of lora configs to apply

output

image_outputobject*

the edited image file.

other image apps

Gemini Nano Banana 2.1

google/gemini-nano-banana-2-1

$1.50/M input, $0.0336–$0.1134/image (1K–4K), +$0.014/query Google Search

Grok Imagine Image 2.0

xai/grok-imagine-image-2-0

$0.04/img low 1k, $0.06/img low 2k, $0.06/img med 1k, $0.08/img med 2k; $0.01/source img (edits)

GPT Image 2.5 Sunburst

openai/gpt-image-2-5-sunburst

$0.006-$0.21 per 1024x1024 image: low $0.006, medium $0.013, high $0.053, xhigh $0.094, max $0.21. Larger sizes cost more; edits add ~$0.008 per reference image.

GPT Image 2.5 Flare

openai/gpt-image-2-5-flare

$0.006-$0.21/image at 1024x1024: low $0.0059, medium $0.0132, high $0.0527, xhigh $0.0937, max $0.2107. Larger sizes cost more; edits add ~$0.0083 per reference image.

Image Resize & Crop

eval/resize

from $0.00000277/sec

more from infsh

HTTP Request

infsh/http

from $0.00000277/sec

DramaBox Text-to-Speech

infsh/dramabox

GPU time, billed per millisecond

Markdown to Word Converter

infsh/markdown-to-word

from $0.00000277/sec

Harrier OSS v1 Text Embeddings

infsh/harrier-oss-v1

GPU time, billed per millisecond

new models

  1. H3 Max Styles

    falai/minimax-h3-max-styles

    video$0.08/sec (768P)

  2. H3 Max Insert Video

    falai/minimax-h3-max-insert-video

    video$0.05/sec 480p, $0.06/sec 768p

  3. H3 Max 3D to Video

    falai/minimax-h3-max-3d-to-video

    video$0.50/request + $0.05/sec (480P), $0.08/sec (768P), $0.16/sec (1080P) + $0.10/generated ref; 5s min

  4. H3 Max Recast

    falai/minimax-h3-max-recast

    video$0.30/sec at 768P, $0.45/sec at 1080P

  5. H3 Max Relight

    falai/minimax-h3-max-relight

    video$0.05/s 480P, $0.08/s 768P, $0.16/s 1080P, $0.32/s 2K; up to 15s/video

ready to run Qwen-Image-Edit?

try in browserbrowse all apps

we use cookies

we use cookies to ensure you get the best experience on our website. for more information on how we use cookies, please see our cookie policy.

by clicking "accept", you agree to our use of cookies.
learn more.