alibaba/wan-2.7/image-edit

Görüntü-Görüntü

Wan 2.7 Image-to-Image API by Alibaba

alibaba/wan-2.7/image-edit

Image-edit

Edits and recomposes images with Wan 2.7 image using text instructions, multi-image references, and optional interaction boxes.

Girdi

Prompt *

Görseller *(0/9)

Dosyaları buraya sürükleyip bırakabilir veya yüklemek için tıklayabilirsiniz

MAX:9

Boyut

Adet

Düşünme modu

Seed

Çıktı

Boşta

Oluşturulan görüntüleriniz burada görünecek

Parametreleri yapılandırın ve oluşturmaya başlamak için Çalıştır'a tıklayın

Her çalıştırma $0.03 maliyete sahip. 10$ ile yaklaşık 333 kez çalıştırabilirsiniz.

Şununla devam edebilirsiniz:

Görüntüden videoya Görüntüden görüntüye

Parametreler

Kod örneği
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/wan-2.7/image-edit",  # Required. Model name. options: alibaba/wan-2.7/image-edit
    "prompt": "A beautiful landscape with mountains and lake",  # Required. Text instruction used to edit the image
    "images": [
        "https://example.com/image1.jpg"
    ],  # Required. Input images, each provided as a URL or Base64 string
    "size": "2K",  # Output image resolution. options: 1K | 2K
    "n": 1,  # Number of images to generate. (min: 1, max: 4)
    "thinking_mode": True,  # Whether to enable thinking mode for higher-quality image generation
    "seed": -1,  # Random seed for image generation. (min: -1, max: 2147483647)
    "enable_sync_mode": False,  # Whether to wait for generation to finish and return the final result directly in the response
    "enable_base64_output": False,  # Whether to return the generated image as a Base64 string instead of a URL
}

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()

Kurulum

Programlama diliniz için gerekli paketi kurun.

pip install requests

Kimlik Doğrulama

Tüm API istekleri, API anahtarı ile kimlik doğrulama gerektirir. API anahtarınızı Atlas Cloud kontrol panelinden alabilirsiniz.

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

HTTP Başlıkları

import os

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

API anahtarınızı güvende tutun

API anahtarınızı asla istemci tarafı kodunda veya herkese açık depolarda ifşa etmeyin. Bunun yerine ortam değişkenleri veya arka uç proxy kullanın.

İstek gönder

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())

İstek Gönder

Asenkron bir oluşturma isteği gönderin. API, durumu kontrol etmek ve sonucu almak için kullanabileceğiniz bir tahmin ID'si döndürür.

POST/api/v1/model/generateImage

İstek Gövdesi

import requests

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

data = {
    "model": "alibaba/wan-2.7/image-edit",
    "prompt": "A beautiful landscape with mountains and lake"
}

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

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

Yanıt

{
  "code": 200,
  "data": {
    "id": "pred_abc123",
    "status": "processing",
    "model": "model-name",
    "created_at": "2025-01-01T00:00:00Z"
  }
}

Durumu Kontrol Et

İsteğinizin mevcut durumunu kontrol etmek için tahmin uç noktasını sorgulayın.

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

Sorgulama Örneği

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)

Durum Değerleri

processingİstek hâlâ işleniyor.

completedOluşturma tamamlandı. Çıktılar kullanılabilir.

succeededOluşturma başarılı oldu. Çıktılar kullanılabilir.

failedOluşturma başarısız oldu. Hata alanını kontrol edin.

Tamamlanmış Yanıt

{
  "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"
  }
}

Dosya Yükle

Dosyaları Atlas Cloud depolama alanına yükleyin ve API isteklerinizde kullanabileceğiniz bir URL alın. Yüklemek için multipart/form-data kullanın.

POST/api/v1/model/uploadMedia

Yükleme Örneği

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}")

Yanıt

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

Input Schema

İstek gövdesinde aşağıdaki parametreler kabul edilir.

Toplam: 9Zorunlu: 3İsteğe Bağlı: 6

modelstringrequired

Model name.

Default: "alibaba/wan-2.7/image-edit"

alibaba/wan-2.7/image-edit

promptstringrequired

Text instruction used to edit the image. Maximum length is 5000 characters.

imagesarray[string]required

Input images, each provided as a URL or Base64 string. The first image is treated as the primary input; subsequent images serve as references.

Min items: 1Max items: 9

sizestring

Output image resolution.

Default: "2K"

1K2K

ninteger

Number of images to generate.

Default: 1Min: 1Max: 4

thinking_modeboolean

Whether to enable thinking mode for higher-quality image generation.

Default: true

seedinteger

Random seed for image generation. Range: 0 to 2147483647. Use -1 for a random seed.

Default: -1Min: -1Max: 2147483647

enable_sync_modeboolean

Whether to wait for generation to finish and return the final result directly in the response.

Default: false

enable_base64_outputboolean

Whether to return the generated image as a Base64 string instead of a URL.

Default: false

Örnek İstek Gövdesi

{
  "model": "alibaba/wan-2.7/image-edit",
  "prompt": "A beautiful landscape",
  "images": [
    "https://example.com/file.jpg"
  ],
  "size": "2K",
  "n": 1,
  "thinking_mode": true,
  "seed": -1,
  "enable_sync_mode": false,
  "enable_base64_output": false
}

Output Schema

API, oluşturulan çıktı URL'lerini içeren bir tahmin yanıtı döndürür.

codeinteger

messagestring

dataunknown

Örnek Yanıt

{
  "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, 400'den fazla AI modelini doğrudan AI kodlama asistanınıza entegre eder. Kurmak için tek bir komut, ardından görüntü, video oluşturmak ve LLM ile sohbet etmek için doğal dil kullanın.

Desteklenen İstemciler

Claude Code

OpenAI Codex

Gemini CLI

Cursor

Windsurf

VS Code

Trae

GitHub Copilot

Cline

Roo Code

Amp

Goose

Replit

40+ desteklenen i̇stemciler

Kurulum

npx skills add AtlasCloudAI/atlas-cloud-skills

API Anahtarını Ayarla

API anahtarınızı Atlas Cloud kontrol panelinden alın ve ortam değişkeni olarak ayarlayın.

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

Yetenekler

Kurulduktan sonra, tüm Atlas Cloud modellerine erişmek için AI asistanınızda doğal dil kullanabilirsiniz.

Görüntü OluşturmaNano Banana 2, Z-Image ve daha fazla model ile görüntüler oluşturun.

Video OluşturmaKling, Vidu, Veo vb. ile metin veya görüntülerden videolar oluşturun.

LLM SohbetQwen, DeepSeek ve diğer büyük dil modelleri ile sohbet edin.

Medya YüklemeGörüntü düzenleme ve görüntüden videoya iş akışları için yerel dosyaları yükleyin.

Daha fazla bilgi

github.com/AtlasCloudAI/atlas-cloud-skills

MCP Server

Atlas Cloud MCP Server, IDE'nizi Model Context Protocol aracılığıyla 400'den fazla AI modeline bağlar. Herhangi bir MCP uyumlu istemci ile çalışır.

Desteklenen İstemciler

Cursor

VS Code

Windsurf

Claude Code

OpenAI Codex

Gemini CLI

Cline

Roo Code

100+ desteklenen i̇stemciler

Kurulum

npx -y atlascloud-mcp

Yapılandırma

Aşağıdaki yapılandırmayı IDE'nizin MCP ayarları dosyasına ekleyin.

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

Mevcut Araçlar

atlas_generate_imageMetin istemlerinden görüntüler oluşturun.

atlas_generate_videoMetin veya görüntülerden videolar oluşturun.

atlas_chatBüyük dil modelleri ile sohbet edin.

atlas_list_models400'den fazla mevcut AI modelini keşfedin.

atlas_quick_generateOtomatik model seçimi ile tek adımda içerik oluşturma.

atlas_upload_mediaAPI iş akışları için yerel dosyaları yükleyin.

Daha fazla bilgi

github.com/AtlasCloudAI/mcp-server

API Şeması

{
  "info": {
    "title": "AtlasCloud API",
    "version": "1.0.0",
    "description": "The AtlasCloud API."
  },
  "openapi": "3.0.0",
  "paths": {
    "/api/v1/model/generateImage": {
      "post": {
        "requestBody": {
          "content": {
            "application/json": {
              "schema": {
                "$ref": "#/components/schemas/Input"
              }
            }
          },
          "required": true
        },
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "The request status."
          }
        }
      },
      "x-api-name": "model_run"
    },
    "/api/v1/model/prediction/{request_id}": {
      "get": {
        "parameters": [
          {
            "in": "path",
            "name": "request_id",
            "required": true,
            "schema": {
              "description": "Request ID",
              "type": "string"
            }
          }
        ],
        "responses": {
          "200": {
            "content": {
              "application/json": {
                "schema": {
                  "$ref": "#/components/schemas/PredictionResponse"
                }
              }
            },
            "description": "Result of the request."
          }
        }
      },
      "x-api-name": "model_result"
    }
  },
  "components": {
    "schemas": {
      "Input": {
        "type": "object",
        "properties": {
          "model": {
            "type": "string",
            "description": "Model name.",
            "default": "alibaba/wan-2.7/image-edit",
            "enum": [
              "alibaba/wan-2.7/image-edit"
            ]
          },
          "prompt": {
            "description": "Text instruction used to edit the image. Maximum length is 5000 characters.",
            "type": "string"
          },
          "images": {
            "description": "Input images, each provided as a URL or Base64 string. The first image is treated as the primary input; subsequent images serve as references.",
            "type": "array",
            "items": {
              "type": "string"
            },
            "minItems": 1,
            "maxItems": 9
          },
          "size": {
            "default": "2K",
            "description": "Output image resolution.",
            "enum": [
              "1K",
              "2K"
            ],
            "type": "string"
          },
          "n": {
            "default": 1,
            "minimum": 1,
            "maximum": 4,
            "description": "Number of images to generate.",
            "type": "integer"
          },
          "thinking_mode": {
            "default": true,
            "description": "Whether to enable thinking mode for higher-quality image generation.",
            "type": "boolean"
          },
          "seed": {
            "default": -1,
            "minimum": -1,
            "maximum": 2147483647,
            "description": "Random seed for image generation. Range: 0 to 2147483647. Use -1 for a random seed.",
            "type": "integer"
          },
          "enable_sync_mode": {
            "default": false,
            "description": "Whether to wait for generation to finish and return the final result directly in the response.",
            "disabled": true,
            "type": "boolean"
          },
          "enable_base64_output": {
            "default": false,
            "description": "Whether to return the generated image as a Base64 string instead of a URL.",
            "disabled": true,
            "type": "boolean"
          }
        },
        "required": [
          "model",
          "prompt",
          "images"
        ],
        "x-order-properties": [
          "model",
          "prompt",
          "images",
          "size",
          "n",
          "thinking_mode",
          "seed",
          "enable_sync_mode",
          "enable_base64_output"
        ]
      },
      "PredictionResponse": {
        "type": "object",
        "properties": {
          "code": {
            "type": "integer"
          },
          "message": {
            "type": "string"
          },
          "data": {
            "$ref": "#/components/schemas/PredictionData"
          }
        }
      },
      "PredictionData": {
        "type": "object",
        "properties": {
          "id": {
            "type": "string",
            "description": "Unique ID of the generation task."
          },
          "model": {
            "type": "string",
            "description": "Model used for the generation."
          },
          "outputs": {
            "type": "array",
            "description": "Generated image results. Each item is an image URL, or a Base64 string when Base64 output is enabled.",
            "items": {
              "type": "string"
            }
          },
          "urls": {
            "type": "object",
            "properties": {
              "get": {
                "type": "string"
              }
            }
          },
          "has_nsfw_contents": {
            "type": "array",
            "items": {
              "type": "boolean"
            }
          },
          "status": {
            "type": "string",
            "description": "Current task status, such as created, processing, completed, unknown, or failed."
          },
          "created_at": {
            "type": "string",
            "format": "date-time"
          },
          "error": {
            "type": "string",
            "description": "Error message returned when the task fails."
          }
        }
      }
    }
  },
  "servers": [
    {
      "url": "https://api.atlascloud.ai"
    }
  ]
}

LLM Dostu İstem Şablonu

# alibaba/wan-2.7/image-edit

> Edits and recomposes images with Wan 2.7 image using text instructions, multi-image references, and optional interaction boxes.


## Overview

- **Submit endpoint (POST)**: `https://api.atlascloud.ai/api/v1/model/generateImage` — start an async generation; returns a `prediction_id`
- **Poll endpoint (GET)**: `https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}` — poll this until the prediction finishes
- **Model ID**: `alibaba/wan-2.7/image-edit`


## API Information

This model can be used via our HTTP API or more conveniently via our client libraries.
See the input and output schema below, as well as the usage examples.


### Input Schema

The API accepts the following input parameters:

- **`model`** (`string`, _required_):
  Model name.
  - Default: `"alibaba/wan-2.7/image-edit"`
  - Options: "alibaba/wan-2.7/image-edit"

- **`prompt`** (`string`, _required_):
  Text instruction used to edit the image. Maximum length is 5000 characters.

- **`images`** (`array[string]`, _required_):
  Input images, each provided as a URL or Base64 string. The first image is treated as the primary input; subsequent images serve as references.
  - Min items: 1
  - Max items: 9

- **`size`** (`string`, _optional_):
  Output image resolution.
  - Default: `"2K"`
  - Options: "1K", "2K"

- **`n`** (`integer`, _optional_):
  Number of images to generate.
  - Default: `1`
  - Min: 1
  - Max: 4

- **`thinking_mode`** (`boolean`, _optional_):
  Whether to enable thinking mode for higher-quality image generation.
  - Default: `true`

- **`seed`** (`integer`, _optional_):
  Random seed for image generation. Range: 0 to 2147483647. Use -1 for a random seed.
  - Default: `-1`
  - Min: -1
  - Max: 2147483647

- **`enable_sync_mode`** (`boolean`, _optional_):
  Whether to wait for generation to finish and return the final result directly in the response.
  - Default: `false`

- **`enable_base64_output`** (`boolean`, _optional_):
  Whether to return the generated image as a Base64 string instead of a URL.
  - Default: `false`



**Required Parameters Example**:

```json
{
  "model": "alibaba/wan-2.7/image-edit",
  "prompt": "",
  "images": [
    ""
  ]
}
```


**Full Example**:

```json
{
  "model": "alibaba/wan-2.7/image-edit",
  "prompt": "",
  "images": [
    ""
  ],
  "size": "2K",
  "n": 1,
  "thinking_mode": true,
  "seed": -1,
  "enable_sync_mode": false,
  "enable_base64_output": false
}
```


### Output Schema

The API returns the following output format:


- **`code`** (`integer`, _optional_):

- **`message`** (`string`, _optional_):

- **`data`** (`string`, _optional_):



**Example Response**:

```json
{
  "code": 0,
  "message": "",
  "data": null
}
```


## Usage Examples

### cURL

```bash
# Step 1: Start generation (async)
curl -X POST "https://api.atlascloud.ai/api/v1/model/generateImage" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "alibaba/wan-2.7/image-edit",
  "prompt": "",
  "images": [
    ""
  ],
  "size": "2K",
  "n": 1,
  "thinking_mode": true,
  "seed": -1,
  "enable_sync_mode": false,
  "enable_base64_output": false
}'

# Response will contain: {"code": 200, "data": {"id": "prediction_id", "status": "processing"}}

# Step 2: Poll for result (replace {prediction_id} with the id returned above)
curl -X GET "https://api.atlascloud.ai/api/v1/model/prediction/{prediction_id}" \
  -H "Authorization: Bearer $ATLASCLOUD_API_KEY"

# Keep polling until status is "completed", "succeeded" or "failed"
# When completed, outputs will contain the generated content URL(s)
```

## Additional Resources

### Documentation

- [Model Playground](https://www.atlascloud.ai/models/alibaba/wan-2.7/image-edit)

Örnek mevcut değil

Yükleniyor...

Alibaba WAN 2.7 Image Edit

Alibaba WAN 2.7 Image Edit enables instruction-driven image editing and multi-image composition. It is designed for workflows where you want to preserve the source scene, combine references, and steer the result with natural language.

Why creators love it

Instruction-based editing: Rewrite clothing, materials, color, scene details, or object relationships with a plain-language prompt.
Single-image and multi-image input: Works well for focused edits as well as reference-driven composition.
Reference composition: Combine multiple images and a text instruction in one edit request.
Practical controls: Supports size, n, watermark, thinking_mode, and seed.

Perfect for

Marketing teams adapting hero visuals for multiple campaigns.
E-commerce teams refreshing product shots and styled composites.
Users who want Wan 2.7 editing quality in the standard tier.
Creative teams that need reliable visual revisions without rebuilding an image from scratch.

How to Use

Provide a main source image, and add extra references when style, objects, or composition details need to be guided.
Write a prompt that explains the desired edit clearly and specifically.
Choose the target size and decide whether you want one result or multiple variations.
Review the edited outputs and keep the one that best balances fidelity and creative change.

Pro tips

Be explicit about what should change and what should remain stable.
Use a single output when you want tighter control, and multiple outputs when exploring different directions.

Wan 2.7 Image-to-Image API by Alibaba

Girdi

Çıktı

Parametreler

Kod örneği

Kurulum

Kimlik Doğrulama

HTTP Başlıkları

İstek gönder

İstek Gönder

İstek Gövdesi

Yanıt

Durumu Kontrol Et

Sorgulama Örneği

Durum Değerleri

Tamamlanmış Yanıt

Dosya Yükle

Yükleme Örneği

Yanıt

Input Schema

Örnek İstek Gövdesi

Output Schema

Örnek Yanıt

Atlas Cloud Skills

Desteklenen İstemciler

Kurulum

API Anahtarını Ayarla

Yetenekler

MCP Server

Desteklenen İstemciler

Kurulum

Yapılandırma

Mevcut Araçlar

API Şeması

LLM Dostu İstem Şablonu

Alibaba WAN 2.7 Image Edit

Why creators love it

Perfect for

How to Use

Pro tips

Benzer Modelleri Keşfedin

Wan-2.7 Pro Image-to-image

Wan-2.7 Pro Text-to-image

Wan-2.7 Text-to-image

Qwen Image 2.0 Pro Text-to-image

Qwen Image 2.0 Text-to-image

Qwen Image 2.0 Edit

Qwen Image 2.0 Pro Edit

Qwen-Image Edit Plus 20251215

Wan-2.6 Image-to-image

Qwen Image Edit

Qwen-Image Text-to-image Max

Qwen-Image Text-to-image Plus

Wan-2.5 Image Edit

Wan-2.5 Text-to-image

Qwen-Image Edit Plus

Qwen-Image Edit

Tüm Medya Yapay Zekâsı için Tek API.

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Girdi

Çıktı

Parametreler

Kod örneği

Kurulum

Kimlik Doğrulama

HTTP Başlıkları

İstek gönder

İstek Gönder

İstek Gövdesi

Yanıt

Durumu Kontrol Et

Sorgulama Örneği

Durum Değerleri

Tamamlanmış Yanıt

Dosya Yükle

Yükleme Örneği

Yanıt

Input Schema

Örnek İstek Gövdesi

Output Schema