Fine-tune image generation models

Save the file_id. This is the unique identifier for the uploaded dataset.

{
    "data": {
        "uploaded_files": [
            {
                "name": "wan-image-t2i-training-dataset.zip",
                "file_id": "3bff1ef7-f72d-4285-bb75-xxxxxx"
            }
        ],
        "failed_uploads": []
    },
    "request_id": "1f3f1c5b-7418-4976-aaea-xxxxxx"
}

Step 2.1: Create a fine-tuning job

Use the file ID from Step 1 to start a training job.

Request example

Replace <replace_with_training_dataset_file_id> with the file_id obtained in the previous step. For the complete parameter reference and format constraints, see Hyperparameters.

curl --location 'https://dashscope.aliyuncs.com/api/v1/fine-tunes' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' \
--data '{
    "model": "wan2.7-image-pro",
    "training_datasets": [
        {
            "data_source_type": "file_id",
            "file_id": "<replace_with_training_dataset_file_id>"
        }
    ],
    "training_type": "efficient_sft",
    "hyper_parameters": {
        "learning_rate": 3e-5,
        "max_steps": 800,
        "eval_steps": 200,
        "max_token_length": "1k",
        "gradient_clip": 0.5,
        "weight_decay": 0.02,
        "max_pixels": "1k",
        "val_img_size": "1k",
        "generation_type": "t2i",
        "lora_rank": 32,
        "save_total_limit": 10
    }
}'

Response example

Pay attention to three key parameters in the output field:

• job_id: The job ID, used to query progress.

• finetuned_output: The name of the fine-tuned model. You must use this name for subsequent deployment and invocation.

• status: The training status. After creating a fine-tuning job, the initial status is PENDING, indicating that training has not yet started.

{
    ...
    "output": {
        "job_id": "ft-202511111122-xxxx",
        "status": "PENDING",
        "finetuned_output": "xxxx-ft-202511111122-xxxx",
        ...
    }
}

Step 2.2: Query the fine-tuning job status

Use the job_id obtained in Step 2.1 to query the job progress. Poll the following API until the status changes to SUCCEEDED.

Request example

Replace <replace_with_fine_tuning_job_id> in the URL with the value of job_id.

curl --location 'https://dashscope.aliyuncs.com/api/v1/fine-tunes/<replace_with_fine_tuning_job_id>' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json'

Response example

Pay attention to two parameters in the output field:

• status: When its value changes to SUCCEEDED, the model training is complete and you can proceed with model deployment.

• usage: The total number of tokens consumed during model training, used for billing purposes.

{
    ...
    "output": {
        "job_id": "ft-202511111122-xxxx",
        "status": "SUCCEEDED",
        "usage": 432000,
        ...
    }
}

Step 3.1: Deploy the model as an online service

After the fine-tuning job status changes to SUCCEEDED, deploy the model as an online service.

Request example

Replace <replace_with_model_name> with the finetuned_output value from the Create a fine-tuning job output.

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"
}'

Response example

Pay attention to two parameters in the output field:

• deployed_model: The deployed model name, used to query the deployment status and invoke the model.

• status: The model deployment status. After deploying the fine-tuned model, the initial status is PENDING, indicating that deployment has not yet started.

{
    ...
    "output": {
        "deployed_model": "wan2.7-image-pro-xxxxxxxxxxxx",
        "status": "PENDING",
        ...
    }
}

Step 3.2: Query the deployment status

Query the deployment status. Poll the following API until the status changes to RUNNING.

Request example

Replace <replace_with_deployed_model> with the deployed_model value from the Step 3.1 output.

curl --location 'https://dashscope.aliyuncs.com/api/v1/deployments/<replace_with_deployed_model>' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' 

Response example

Pay attention to two parameters in the output field:

• status: When the status changes to RUNNING, the model has been deployed successfully and you can start invoking it.

• deployed_model: The deployed model name.

{
    ...
    "output": {
        "status": "RUNNING",
        "deployed_model": "wan2.7-image-pro-xxxxxxxxxxxx",
        ...
    }
}

Training set: Required

Validation set: Optional

1. Determine the fine-tuning scenario

Wan supports the following fine-tuning scenarios for image generation:

• IP character stylization: Train the model to learn the drawing style of a specific IP character, such as anime characters or mascot images.

• Fixed visual style: Improve the model's ability to reproduce a specific art style, such as flat illustration, ink painting, or pixel art.

• Specific scene generation: Replicate specific composition patterns or scene templates, such as product display images or poster layouts.

2. Obtain raw materials

• AI generation and selection: Use the Wan base model to generate images in bulk, then manually select the high-quality samples that best match the target effect. This is the most commonly used method.

• Real photography: If your goal is to achieve highly realistic scenes (such as real product photos or portrait photography), using real-shot footage is the best choice.

• 3D software rendering: For scenes that require fine detail control or 3D rendering styles, we recommend using 3D software (such as Blender or C4D) to create source materials.

3. Clean the data

Prompt writing formula

Prompt = [Subject description] + [Background description] + [Trigger word] + [Style description]

During training, the system periodically saves model "snapshots" (i.e., checkpoints). By default, the system outputs the last checkpoint as the final fine-tuned model. However, checkpoints produced during intermediate stages may perform better than the final version. You can select the most satisfactory one for deployment.

The system runs checkpoints on the validation set and generates preview images at intervals set by the Hyperparameters (hyper_parameters) eval_steps.

• How to evaluate: Judge the results by directly observing the generated preview images.

• Selection criteria: Find the checkpoint with the best results and the most closely matching style.

Procedure

curl --location 'https://dashscope.aliyuncs.com/api/v1/fine-tunes/<replace_with_fine_tuning_job_id>/validation-results' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY" \
--header 'Content-Type: application/json' 

{
    "request_id": "da1310f5-5a21-4e29-99d4-xxxxxx",
    "output": [
        {
            "checkpoint": "checkpoint-160"
        },
        ...
    ]
}

curl --location 'https://dashscope.aliyuncs.com/api/v1/fine-tunes/<replace_with_fine_tuning_job_id>/validation-details/<replace_with_checkpoint_to_export>?page_no=1&page_size=10' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY"

{
    "request_id": "375b3ad0-d3fa-451f-b629-xxxxxxx",
    "output": {
        "page_no": 1,
        "page_size": 10,
        "total": 5,
        "list": [
            {
                "img_path": "https://finetune-result.oss-cn-wulanchabu.aliyuncs.com/xxx.png?Expires=xxxxxx",
                "prompt": "s86b5p, Change the background to an elevator equipped with a white ceiling lighting, featuring large floor-to-ceiling windows. Change the character's clothing to red tight-fitting mech armor with black stripe decorations.",
                "input_img": "https://finetune-result.oss-cn-wulanchabu.aliyuncs.com/val_dataset/input_001.png?Expires=xxxxxx"
            },
            ...
        ]
    }
}

curl --location 'https://dashscope.aliyuncs.com/api/v1/fine-tunes/<replace_with_fine_tuning_job_id>/export/<replace_with_checkpoint_to_export>?model_name=<replace_with_exported_model_display_name>' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY"

{
    "request_id": "0817d1ed-b6b6-4383-9650-xxxxx",
    "output": true
}

curl --location 'https://dashscope.aliyuncs.com/api/v1/fine-tunes/<replace_with_fine_tuning_job_id>/checkpoints' \
--header "Authorization: Bearer $DASHSCOPE_API_KEY"

{
    "request_id": "b0e33c6e-404b-4524-87ac-xxxxxx",
    "output": [
         ...,
        {
            "create_time": "2025-11-11T13:27:29",
            "full_name": "ft-202511111122-496e:checkpoint-160",
            "job_id": "ft-202511111122-496e",
            "checkpoint": "checkpoint-160",
            "model_name": "xxxx-ft-202511111122-xxxx-c160", // Important field, used for model deployment and invocation
            "model_display_name": "xxxx-ft-202511111122-xxxx",
            "status": "SUCCEEDED" // Successfully exported checkpoint
        },
        ...

    ]
}