alibaba/qwen-image/edit

image-to-image

Qwen-Image Edit API by Alibaba

alibaba/qwen-image/edit

Edit

Supports multiple image inputs and outputs, allowing for precise modification of text within images, addition, deletion, or movement of objects, alteration of subject actions, transfer of image styles, and enhancement of image details.

INPUT

Loading parameter configuration...

OUTPUT

Idle

Your generated images will appear here

Configure your settings and click Run to get started

Your request will cost $0.032 per run. For $10 you can run this model approximately 312 times.

Here's what you can do next:

Image-to-Video Image-to-Image

Parametri

Esempio di codice
import requests
import time

# Step 1: Start image generation
generate_url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "alibaba/qwen-image/edit",
    "prompt": "A beautiful landscape with mountains and lake",
    "width": 512,
    "height": 512,
    "steps": 20,
    "guidance_scale": 7.5,
}

generate_response = requests.post(generate_url, headers=headers, json=data)
generate_result = generate_response.json()
prediction_id = generate_result["data"]["id"]

# Step 2: Poll for result
poll_url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"

def check_status():
    while True:
        response = requests.get(poll_url, headers={"Authorization": "Bearer $ATLASCLOUD_API_KEY"})
        result = response.json()

        if result["data"]["status"] == "completed":
            print("Generated image:", result["data"]["outputs"][0])
            return result["data"]["outputs"][0]
        elif result["data"]["status"] == "failed":
            raise Exception(result["data"]["error"] or "Generation failed")
        else:
            # Still processing, wait 2 seconds
            time.sleep(2)

image_url = check_status()

Installa

Installa il pacchetto richiesto per il tuo linguaggio.

bash

pip install requests

Autenticazione

Tutte le richieste API richiedono l'autenticazione tramite una chiave API. Puoi ottenere la tua chiave API dalla dashboard di Atlas Cloud.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

Header HTTP

python

import os

API_KEY = os.environ.get("ATLASCLOUD_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": f"Bearer {API_KEY}"
}

Proteggi la tua chiave API

Non esporre mai la tua chiave API nel codice lato client o nei repository pubblici. Utilizza invece variabili d'ambiente o un proxy backend.

Invia una richiesta

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}
data = {
    "model": "your-model",
    "prompt": "A beautiful landscape"
}

response = requests.post(url, headers=headers, json=data)
print(response.json())

Invia una richiesta

Invia una richiesta di generazione asincrona. L'API restituisce un ID di previsione che puoi usare per controllare lo stato e recuperare il risultato.

POST/api/v1/model/generateImage

Corpo della richiesta

import requests

url = "https://api.atlascloud.ai/api/v1/model/generateImage"
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer $ATLASCLOUD_API_KEY"
}

data = {
    "model": "alibaba/qwen-image/edit",
    "input": {
        "prompt": "A beautiful landscape with mountains and lake"
    }
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

print(f"Prediction ID: {result['id']}")
print(f"Status: {result['status']}")

Risposta

{
  "id": "pred_abc123",
  "status": "processing",
  "model": "model-name",
  "created_at": "2025-01-01T00:00:00Z"
}

Controlla lo stato

Interroga l'endpoint di previsione per verificare lo stato attuale della tua richiesta.

GET/api/v1/model/prediction/{prediction_id}

Esempio di polling

import requests
import time

prediction_id = "pred_abc123"
url = f"https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

while True:
    response = requests.get(url, headers=headers)
    result = response.json()
    status = result["data"]["status"]
    print(f"Status: {status}")

    if status in ["completed", "succeeded"]:
        output_url = result["data"]["outputs"][0]
        print(f"Output URL: {output_url}")
        break
    elif status == "failed":
        print(f"Error: {result['data'].get('error', 'Unknown')}")
        break

    time.sleep(3)

Valori di stato

processingLa richiesta è ancora in fase di elaborazione.

completedLa generazione è completata. I risultati sono disponibili.

succeededLa generazione è riuscita. I risultati sono disponibili.

failedLa generazione è fallita. Controlla il campo errore.

Risposta completata

{
  "data": {
    "id": "pred_abc123",
    "status": "completed",
    "outputs": [
      "https://storage.atlascloud.ai/outputs/result.png"
    ],
    "metrics": {
      "predict_time": 8.3
    },
    "created_at": "2025-01-01T00:00:00Z",
    "completed_at": "2025-01-01T00:00:10Z"
  }
}

Carica file

Carica file nello storage Atlas Cloud e ottieni un URL utilizzabile nelle tue richieste API. Usa multipart/form-data per il caricamento.

POST/api/v1/model/uploadMedia

Esempio di caricamento

import requests

url = "https://api.atlascloud.ai/api/v1/model/uploadMedia"
headers = { "Authorization": "Bearer $ATLASCLOUD_API_KEY" }

with open("image.png", "rb") as f:
    files = {"file": ("image.png", f, "image/png")}
    response = requests.post(url, headers=headers, files=files)

result = response.json()
download_url = result["data"]["download_url"]
print(f"File URL: {download_url}")

Risposta

{
  "data": {
    "download_url": "https://storage.atlascloud.ai/uploads/abc123/image.png",
    "file_name": "image.png",
    "content_type": "image/png",
    "size": 1024000
  }
}

Schema di input

I seguenti parametri sono accettati nel corpo della richiesta.

Totale: 0Obbligatorio: 0Opzionale: 0

Nessun parametro disponibile.

Esempio di corpo della richiesta

json

{
  "model": "alibaba/qwen-image/edit"
}

Schema di output

L'API restituisce una risposta di previsione con gli URL degli output generati.

idstringrequired

Unique identifier for the prediction.

statusstringrequired

Current status of the prediction.

processingcompletedsucceededfailed

modelstringrequired

The model used for generation.

outputsarray[string]

Array of output URLs. Available when status is "completed".

errorstring

Error message if status is "failed".

metricsobject

Performance metrics.

predict_timenumber

Time taken for image generation in seconds.

created_atstringrequired

ISO 8601 timestamp when the prediction was created.

Format: date-time

completed_atstring

ISO 8601 timestamp when the prediction was completed.

Format: date-time

Esempio di risposta

json

{
  "id": "pred_abc123",
  "status": "completed",
  "model": "model-name",
  "outputs": [
    "https://storage.atlascloud.ai/outputs/result.png"
  ],
  "metrics": {
    "predict_time": 8.3
  },
  "created_at": "2025-01-01T00:00:00Z",
  "completed_at": "2025-01-01T00:00:10Z"
}

Atlas Cloud Skills

Atlas Cloud Skills integra oltre 300 modelli di IA direttamente nel tuo assistente di codifica IA. Un comando per installare, poi usa il linguaggio naturale per generare immagini, video e chattare con LLM.

Client supportati

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ client supportati

Installa

bash

npx skills add AtlasCloudAI/atlas-cloud-skills

Configura chiave API

Ottieni la tua chiave API dalla dashboard di Atlas Cloud e impostala come variabile d'ambiente.

bash

export ATLASCLOUD_API_KEY="your-api-key-here"

Funzionalità

Una volta installato, puoi usare il linguaggio naturale nel tuo assistente IA per accedere a tutti i modelli Atlas Cloud.

Generazione di immaginiGenera immagini con modelli come Nano Banana 2, Z-Image e altri.

Creazione di videoCrea video da testo o immagini con Kling, Vidu, Veo, ecc.

Chat LLMChatta con Qwen, DeepSeek e altri grandi modelli linguistici.

Caricamento mediaCarica file locali per la modifica di immagini e flussi di lavoro da immagine a video.

Scopri di più

github.com/AtlasCloudAI/atlas-cloud-skills

Server MCP

Il server MCP di Atlas Cloud collega il tuo IDE con oltre 300 modelli di IA tramite il Model Context Protocol. Funziona con qualsiasi client compatibile MCP.

Client supportati

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ client supportati

Installa

bash

npx -y atlascloud-mcp

Configurazione

Aggiungi la seguente configurazione al file delle impostazioni MCP del tuo IDE.

json

{
  "mcpServers": {
    "atlascloud": {
      "command": "npx",
      "args": [
        "-y",
        "atlascloud-mcp"
      ],
      "env": {
        "ATLASCLOUD_API_KEY": "your-api-key-here"
      }
    }
  }
}

Strumenti disponibili

atlas_generate_imageGenera immagini da prompt testuali.

atlas_generate_videoCrea video da testo o immagini.

atlas_chatChatta con grandi modelli linguistici.

atlas_list_modelsEsplora oltre 300 modelli di IA disponibili.

atlas_quick_generateCreazione di contenuti in un solo passaggio con selezione automatica del modello.

atlas_upload_mediaCarica file locali per i flussi di lavoro API.

Scopri di più

github.com/AtlasCloudAI/mcp-server

API Schema

Schema not available

No examples available

Please log in to view request history

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Alibaba Qwen-Image Edit

An advanced image editing model from Alibaba Cloud, offering precise control and high-quality results. Qwen-Image Edit is designed to handle common editing tasks, allowing users to modify images with natural language prompts. It supports single-image editing and multi-image blending.

Overview

Purpose: Perform image edits using text instructions.
Core Capability: Supports single-image editing and multi-image blending.
Foundation: Powered by Alibaba's advanced multi-modal generative AI technology.
Typical Output: High-quality edited image (1 per request) that blends changes with the original content.
Use Cases: Social media content adjustment, basic photo retouching, and creative experimentation.

Key Features

Multi-image Blending:
- Example: Combine a girl from Image 1, wearing a skirt from Image 2, sitting in a pose from Image 3.
- Example: Combine a girl from Image 1, a necklace from Image 2, and a bag from Image 3.
Single-image Editing:
- Generate depth-compliant images.
- Replace text (e.g., "HEALTH INSURANCE" -> "明天会更好").
- Replace shirt color.
- Change background (e.g., to Antarctica).
High Fidelity: Preserves the quality of the original image while applying edits.
Smart Inpainting: Fills gaps or replaces objects based on the surrounding context.

Designed For

Content Creators: Quickly adjust visuals for social platforms.
General Users: Easily modify personal photos or creative projects.
Developers: Integrate image editing capabilities into applications.

Input Requirements

To achieve the best results, follow these guidelines:

Inputs

Structure:
- messages array with role: user.
- content array: 1-3 images ({"image": "..."}) + 1 text instruction ({"text": "..."}).
Image Format: JPG, JPEG, PNG, BMP, TIFF, WEBP, GIF (first frame).
Resolution: Recommended 384px - 3072px.
Size Limit: Max 10MB per image.
Text Limit: Max 800 characters.

Parameters

n: Number of output images. Fixed at 1.
negative_prompt: Description of content to exclude (max 500 characters).
size: Not customizable. Output resolution maintains the aspect ratio of the input (or last input image), typically around 1024x1024.
watermark: Boolean to add "Qwen-Image" watermark. Default is false.
seed: Integer for reproducibility.

Pricing

Billing Logic: Pay-as-you-go based on the number of successful output images.
Tier: Standard tier offering a balance of performance and cost.

Limitations & FAQ

Conversation: Does not support multi-turn conversation (single turn only).
Languages: Chinese and English are supported; other languages are unverified.
Aspect Ratio: Output follows the aspect ratio of the input image (or the last image if multiple are provided).
Resolution: Does not support custom output resolution.
Output Quantity: Only supports generating 1 image per request.

Version

Model: Alibaba Qwen-Image Edit
Family: Qwen-Image
Technical Context: Standard version with essential editing capabilities.

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From

$0.03/PIC

Inizia con Oltre 300 Modelli,

Esplora tutti i modelli

Qwen-Image Edit API by Alibaba

INPUT

OUTPUT

Parametri

Esempio di codice

Installa

Autenticazione

Header HTTP

Invia una richiesta

Invia una richiesta

Corpo della richiesta

Risposta

Controlla lo stato

Esempio di polling

Valori di stato

Risposta completata

Carica file

Esempio di caricamento

Risposta

Schema di input

Esempio di corpo della richiesta

Schema di output

Esempio di risposta

Atlas Cloud Skills

Client supportati

Installa

Configura chiave API

Funzionalità

Server MCP

Client supportati

Installa

Configurazione

Strumenti disponibili

API Schema

Please log in to view request history

Alibaba Qwen-Image Edit

Overview

Key Features

Designed For

Input Requirements

Inputs

Parameters

Pricing

Limitations & FAQ

Version

Explore Similar Models

Wan-2.7 Pro Image-to-image

Wan-2.7 Pro Text-to-image

Wan-2.7 Image-to-image

Wan-2.7 Text-to-image

Inizia con Oltre 300 Modelli,

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