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Image Generation - Deploy Model

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Publish a trained image generation model as an online API service.

Prerequisites

  • Supported regions: The model deployment API is currently available only in the China (Beijing) region. If you use other regions, please deploy models through the Model Studio console in that region.

  • Account permissions: If you use Alibaba Cloud sub-accounts (RAM users), you need to grant the sub-account model invocation, training, and deployment permissions.

  • Configure environment variables: You have successfully obtained an API Key and configured it as an environment variable.

  • Prerequisite: Model fine-tuning training has been completed. Please first call the Query Fine-tuning Job API to confirm the job status status is SUCCEEDED before proceeding with deployment.

Deploy Model

China (Beijing)

POST https://dashscope.aliyuncs.com/api/v1/deployments

For Windows CMD, replace $DASHSCOPE_API_KEY with %DASHSCOPE_API_KEY%. For PowerShell, replace with $env:DASHSCOPE_API_KEY

Request Parameters

Deploy Model

curl --location 'https://dashscope.aliyuncs.com/api/v1/deployments' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model_name": "<replace with model_name>",
    "capacity": 1,
    "plan": "lora"
}'
Headers

Content-Type string (Required)

Fixed value: application/json

Authorization string (Required)

API Key authentication, in the format Bearer sk-xxxx.
Request Body

model_name string (Required)

The model ID to deploy (not the base model name, but the model identifier generated after fine-tuning or export). How to obtain:

capacity integer (Required)

The number of deployed model instances. Recommended value: 1.

plan string (Required)

Deployment method. Recommended value for LoRA efficient fine-tuning: lora.

Response Parameters

Success Response Example

Key fields: output.deployed_model (unique identifier of the deployed model), output.status (deployment status).

{
    "request_id": "96020b2e-9072-4c8a-9981-xxxxxxxxx",
    "output": {
        "deployed_model": "wan2.7-image-pro-ft-202507011122-xxxx",
        "gmt_create": "2025-07-01T10:30:00.000",
        "gmt_modified": "2025-07-01T10:30:00.000",
        "status": "PENDING",
        "model_name": "wan2.7-image-pro-ft-202507011122-xxxx",
        "base_model": "wan2.7-image-pro",
        "workspace_id": "llm-xxxxxxxxx",
        "charge_type": "post_paid",
        "creator": "12xxxxxxx",
        "modifier": "12xxxxxxx",
        "plan": "lora"
    }
}

Error Response Example

{
    "code": "InvalidParameter",
    "request_id": "BE213CDD-8A5C-59EE-9A67-055EAB0CB59B",
    "message": "The model xxx does not exist or is not deployable"
}

request_id string

Unique identifier of the request.

output object

Task details.

Properties

deployed_model string

Unique identifier of the deployed model. Used to query model deployment status and invoke the model.

model_name string

Model identifier name.

status string

Deployment status:

  • PENDING: Deploying.

  • RUNNING: Running.

  • FAILED: Deployment failed.

base_model string

The base model used.

gmt_create string

Deployment task creation time.

gmt_modified string

Deployment task update time.

workspace_id string

The workspace ID associated with the Alibaba Cloud Model Studio API Key. See Get the Workspace ID。

charge_type string

Billing mode. post_paid indicates pay-as-you-go.

creator string

Alibaba Cloud account ID of the creator.

modifier string

Alibaba Cloud account ID of the modifier.

plan string

Deployment method.

code string

Error code. Returned when the call fails.

message string

Detailed error description. Returned when the call fails.

Next Step

Deployment is an asynchronous operation. After calling this API, you can query the deployment status through the Query and Manage Deployments API.

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